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\n\t\t\t
1. Introduction
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Model predictive control, MPC, has many interesting features for its application to mobile robot control. It is a more effective advanced control technique, as compared to the standard PID control, and has made a significant impact on industrial process control (Maciejowski, 2002). MPC usually contains the following three ideas:
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The model of the process is used to predict the future outputs along a horizon time.
An index of performance is optimized by a control sequence computation.
It is used a receding horizon idea, so at each instant of time the horizon is moved towards the future. It involves the application of the first control signal of the sequence computed at each step.
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The majority of the research developed using MPC techniques and their application to WMR (wheeled mobile robots) is based on the fact that the reference trajectory is known beforehand (Klancar & Skrjanc, 2007). The use of mobile robot kinematics to predict future system outputs has been proposed in most of the different research developed (Kühne et al., 2005; Gupta et al., 2005). The use of kinematics have to include velocity and acceleration constraints to prevent WMR of unfeasible trajectory-tracking objectives. MPC applicability to vehicle guidance has been mainly addressed at path-tracking using different on-field fixed trajectories and using kinematics models. However, when dynamic environments or obstacle avoidance policies are considered, the navigation path planning must be constrained to the robot neighborhood where reactive behaviors are expected (Fox et al., 1997; Ögren & Leonard, 2005). Due to the unknown environment uncertainties, short prediction horizons have been proposed (Pacheco et al., 2008). In this context, the use of dynamic input-output models is proposed as a way to include the dynamic constraints within the system model for controller design. In order to do this, a set of dynamic models obtained from experimental robot system identification are used for predicting the horizon of available coordinates. Knowledge of different models can provide information about the dynamics of the robot, and consequently about the reactive parameters, as well as the safe stop distances. This work extends the use of on-line MPC as a suitable local path-tracking methodology by using a set of linear time-varying descriptions of the system dynamics when short prediction horizons are used. In the approach presented, the trajectory is dynamically updated by giving a straight line to be tracked. In this way, the control law considers the local point to be achieved and the WMR coordinates. The cost function is formulated with parameters that involve the capacity of turning and going straight. In the case considered, the Euclidean distance between the robot and the desired trajectory can be used as a potential function. Such functions are CLF (control Lyapunov function), and consequently asymptotic stability with respect to the desired trajectory can be achieved. On-line MPC is tested by using the available WMR. A set of trajectories is used for analyzing the path-tracking performance. In this context, the different parameter weights of the cost function are studied. The experiments are developed by considering five different kinds of trajectories. Therefore, straight, wide left turning, less left turning, wide right turning, and less right turning are tested. Experiments are conducted by using factorial design with two levels of quantitative factors (Box et al., 2005). Studies are used as a way of inferring the weight of the different parameters used in the cost function. Factor tuning is achieved by considering aspects, such as the time taken, or trajectory deviation, within different local trajectories. Factor tuning depicts that flexible cost function as an event of the path to be followed, can improve control performance when compared with fixed cost functions. It is proposed to use local artificial potential attraction field coordinates as a way to attract WMR towards a local desired goal. Experiments are conducted by using a monocular perception system and local MPC path-tracking. On-line MPC is reported as a suitable navigation strategy for dynamics environments.
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This chapter is organized as follows: Section 1 gives a brief presentation about the aim of the present work. In the Section 2, the WMR dynamic models are presented. This section also describes the MPC formulation, algorithms and simulated results for achieving local path-tracking. Section 3 presents the MPC implemented strategies and the experimental results developed in order to adjust the cost function parameters. The use of visual data is presented as a horizon where trajectories can be planned by using MPC strategies. In this context local MPC is tested as a suitable navigation strategy. Finally, in Section 4 some conclusions are made.
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2. The control system identification and the MPC formulation
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This section introduces the necessary previous background used for obtaining the control laws that are tested in this work as a suitable methodology for performing local navigation. The WMR PRIM, available in our lab, has been used in order to test and orient the research (Pacheco et al., 2009). Fig. 1 shows the robot PRIM and sensorial and system blocs used in
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Figure 1.
a) The robot PRIM used in this work; (b) The sensorial and electronic system blocs
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the research work. The mobile robot consists of a differential driven one, with two independent wheels of 16cm diameters actuated by two DC motors. A third spherical omni-directional wheel is used to guarantee the system stability. Next subsection deals with the problem of modeling the dynamics of the WMR system. Furthermore, dynamic MPC techniques for local trajectory tracking and some simulated results are introduced in the remaining subsections. A detailed explanation of the methods introduced in this section can be found in (Pacheco et al., 2008).
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2.1. Experimental model and system identification
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The model is obtained through the approach of a set of lineal transfer functions that include the nonlinearities of the whole system. The parametric identification process is based on black box models (Norton, 1986; Ljung, 1989). The nonholonomic system dealt with in this work is considered initially to be a MIMO (multiple input multiple output) system, as shown in Fig. 2, due to the dynamic influence between two DC motors. This MIMO system is composed of a set of SISO (single input single output) subsystems with coupled connection.
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Figure 2.
The MIMO system structure
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The parameter estimation is done by using a PRBS (Pseudo Random Binary Signal) such as excitation input signal. It guarantees the correct excitation of all dynamic sensible modes of the system along the whole spectral range and thus results in an accurate precision of parameter estimation. The experiments to be realized consist in exciting the two DC motors in different (low, medium and high) ranges of speed. The ARX (auto-regressive with external input) structure has been used to identify the parameters of the system. The problem consists in finding a model that minimizes the error between the real and estimated data. By expressing the ARX equation as a lineal regression, the estimated output can be written as:
with \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\ty\n\t\t\t\t\t\t\t\t^\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t being the estimated output vector, θ the vector of estimated parameters and φ the vector of measured input and output variables. By using the coupled system structure, the transfer function of the robot can be expressed as follows:
where Y\n\t\t\t\t\t\n\t\t\t\t\t\tR\n\t\t\t\t\t and Y\n\t\t\t\t\t\n\t\t\t\t\t\tL\n\t\t\t\t\t represent the speeds of right and left wheels, and U\n\t\t\t\t\t\n\t\t\t\t\t\tR\n\t\t\t\t\t and U\n\t\t\t\t\t\n\t\t\t\t\t\tL\n\t\t\t\t\t the corresponding speed commands, respectively. In order to know the dynamics of robot system, the matrix of transfer function should be identified. In this way, speed responses to PBRS input signals are analyzed. The filtered data, which represent the average value of five different experiments with the same input signal, are used for identification. The system is identified by using the identification toolbox “ident” of Matlab for the second order models. Table 1 shows the continuous transfer functions obtained for the three different lineal speed models.
The coupling effects should be studied as a way of obtaining a reduced-order dynamic model. It can be seen from Table 1 that the dynamics of two DC motors are different and the steady gains of coupling terms are relatively small (less than 20% of the gains of main diagonal terms). Thus, it is reasonable to neglect the coupling dynamics so as to obtain a simplified model. In order to verify the above facts from real results, a set of experiments have been done by sending a zero speed command to one motor and different non-zero speed commands to the other motor. The experimental result confirms that the coupled dynamics can be neglected. The existence of different gains in steady state is also verified experimentally. Finally, the order reduction of the system model is carried out through the analysis of pole positions by using the root locus method. It reveals the existence of a dominant pole and consequently the model order can be reduced from second order to first order. Table 2 shows the first order transfer functions obtained. Afterwards, the system models are validated through the experimental data by using the PBRS input signal.
2.2. Dynamic MPC techniques for local trajectory tracking
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The minimization of path tracking error is considered to be a challenging subject in mobile robotics. In this subsection the LMPC (local model predictive control) techniques based on the dynamics models obtained in the previous subsection are presented. The use of dynamic models avoids the use of velocity and acceleration constraints used in other MPC research based on kinematic models. Moreover, contractive constraints are proposed as a way of guaranteeing convergence towards the desired coordinates. In addition, real-time implementations are easily implemented due to the fact that short prediction horizons are used. By using LMPC, the idea of a receding horizon can deal with local on-robot sensor information. The LMPC and contractive constraint formulations as well as the algorithms and simulations implemented are introduced in the next subsections.
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2.2.1. The LMPC formulation
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The main objective of highly precise motion tracking consists in minimizing the error between the robot and the desired path. Global path-planning becomes unfeasible since the sensorial system of some robots is just local. In this way, LMPC is proposed in order to use the available local perception data in the navigation strategies. Concretely, LMPC is based on minimizing a cost function related to the objectives for generating the optimal WMR inputs. Define the cost function as follows:
The first term of (3) refers to the attainment of the local desired coordinates, X\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tld\n\t\t\t\t\t\t\n\t\t\t\t\t\t=(x\n\t\t\t\t\t\t\n\t\t\t\t\t\t\td\n\t\t\t\t\t\t\n\t\t\t\t\t\t,y\n\t\t\t\t\t\t\n\t\t\t\t\t\t\td\n\t\t\t\t\t\t), where (x\n\t\t\t\t\t\t\n\t\t\t\t\t\t\td\n\t\t\t\t\t\t\n\t\t\t\t\t\t, y\n\t\t\t\t\t\t\n\t\t\t\t\t\t\td\n\t\t\t\t\t\t) denote the desired Cartesian coordinates. X(k+n/k) represents the terminal value of the predicted output after the horizon of prediction n. The second one can be considered as an orientation term and is related to the distance between the predicted robot positions and the trajectory segment given by a straight line between the initial robot Cartesian coordinates X\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tl0\n\t\t\t\t\t\t\n\t\t\t\t\t\t=(x\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tl0\n\t\t\t\t\t\t\n\t\t\t\t\t\t, y\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tl0\n\t\t\t\t\t\t) from where the last perception was done and the desired local position, X\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tld\n\t\t\t\t\t\t, to be achieved within the perceived field of view. This line orientation is denoted by θ\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tld\n\t\t\t\t\t\t and denotes the desired orientation towards the local objective. X(k+i/k) and θ(k+i/k) (i=1,…n-1) represents the predicted Cartesian and orientation values within the prediction horizon. The third term is the predicted orientation error. The last one is related to the power signals assigned to each DC motor and are denoted as U. The parameters P, Q, R and S are weighting parameters that express the importance of each term. The control horizon is designed by the parameter m. The system constraints are also considered:
where X(k) and θ(k) denote the current WMR coordinates and orientation, X(k+n/k) and θ(k+n/k) denote the final predicted coordinates and orientation, respectively. The limitation of the input signal is taken into account in the first constraint, where G\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t0\n\t\t\t\t\t\t and G\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t1\n\t\t\t\t\t\t respectively denote the dead zone and saturation of the DC motors. The second and third terms are contractive constraints (Wang, 2007), which result in the convergence of coordinates or orientation to the objective, and should be accomplished at each control step.
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2.2.2. The algorithms and simulated results
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By using the basic ideas introduced in the previous subsection, the LMPC algorithms have the following steps:
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Read the current position
Minimize the cost function and to obtain a series of optimal input signals
Choose the first obtained input signal as the command signal.
Go back to the step 1 in the next sampling period.
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The minimization of the cost function is a nonlinear problem in which the following equation should be verified:
The use of interior point methods can solve the above problem (Nesterov et al., 1994; Boyd & Vandenberghe, 2004). Gradient descent method and complete input search can be used for obtaining the optimal input. In order to reduce the set of possibilities, when optimal solution is searched for, some constraints over the DC motor inputs are taken into account:
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The signal increment is kept fixed within the prediction horizon.
The input signals remain constant during the remaining interval of time.
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The above considerations will result in the reduction of the computation time and the smooth behavior of the robot during the prediction horizon (Maciejowski, 2002). Thus, the set of available input is reduced to one value, as it is shown in Fig. 3.
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Figure 3.
LMPC strategy with fixed increment of the input during the control horizon and constant value for the remaining time
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Both search methods perform accurate path-tracking. Optimal input search has better time performance and subinterval gradient descent method does not usually give the optimal solution. Due to these facts obtained from simulations, complete input search is selected for the on-robot experiences presented in the next section.
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The evaluation of the LMPC performance is made by using different parametric values in the proposed cost function (3). In this way, when only the desired coordinates are considered, (P=1, Q=0, R=0, S=0), the trajectory-tracking is done with the inputs that can minimize the cost function by shifting the robot position to the left. The reason can be found in Table 2, where the right motor has more gain than the left one for high speeds. This problem can be solved, (P=1, Q=1, R=0, S=0) or (P=1, Q=0, R=1, S=0) by considering either the straight-line trajectory from the point where the last perception was done to the final desired point belonging to the local field of perception or the predicted orientations. Simulated results by testing both strategies provide similar satisfactory results. Thus, the straight line path or orientation should be considered in the LMPC cost function. Fig. 4 shows a simulated result of LMPC for WMR by using the orientation error, the trajectory distance and the final desired point for the cost function optimization (P=1, Q=1, R=1, S=0). Obtained results show the need of R parameter when meaningful orientation errors are produced.
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The prediction horizon magnitude is also analyzed. The possible coordinates available for prediction when the horizon is larger (n=10, m=5), depict a less dense possibility of coordinates when compared with shorter horizons of prediction. Short prediction horizon strategy is more time effective and performs path-tracking with better accuracy. For these reasons, a short horizon strategy (n=5, m=3) is proposed for implementing experimental results.
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Figure 4.
Trajectory tracking simulated result by using the orientation error, trajectory distance and the final desired point for the optimization.
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The sampling time for each LMPC step was set to 100ms. Simulation time performance of complete input search and gradient descent methods is computed. For short prediction horizon (n=5, m=3), the simulation processing time is less than 3ms for the complete input search strategy and less than 1ms for the gradient descent method when algorithms are running in a standard 2.7 GHz PC. Real on-robot algorithm time performance is also compared for different prediction horizons by using the embedded 700 Mhz PC and additional hardware system. Table 3 shows the LMPC processing time for different horizons of prediction when complete optimal values search or the gradient descent method are used. Surprisingly, when the horizon is increased the computing time is decreased. It is due to the fact that the control horizon is also incremented, and consequently less range of signal increments are possible because the signal increment is kept fixed within the control horizon. Thus, the maximum input value possibilities decrease with larger horizons. Hence for n=5 there are 1764 possibilities (42x42), and for n=10 there are 625 (25x25).
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Horizon of prediction (n)
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Complete search method
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Gradient descent method
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n=5
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45ms
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16ms
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n=8
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34ms
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10ms
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n=10
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25ms
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7ms
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Table 3.
LMPC processing times
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3. Tuning the control law parameters by using path-tracking experimental results
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In this section, path-tracking problem and the cost function parameter weights are analyzed, within a constrained field of perception provided by the on-robot sensor system. The main objective is to obtain further control law analysis by experimenting different kind of trajectories. The importance of the cost function parameter weights is analyzed by developing the factorial design of experiments for a representative set of local trajectories. Statistical results are compared and control law performance is analyzed as a function of the path to be followed. Experimental LMPC results are conducted by considering a constrained horizon of perception provided by a monocular camera where artificial potential fields are used in order to obtain the desired coordinates within the field of view of the robot.
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3.1. The local field of perception
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In order to test the LMPC by using constrained local perception, the field of view obtained by a monocular camera has been used. Ground available scene coordinates appear as an image, in which the camera setup and pose knowledge are used, and projective perspective is assumed to make each pixel coordinate correspond to a 3D scene coordinate (Horn, 1998). Fig. 5 shows a local map provided by the camera, which corresponds to a field of view with a horizontal angle of 48º, a vertical angle of 37º, H set to 109cm and a tilt angle of 32º.
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Figure 5.
Available local map coordinates (in green), the necessary coordinates free of obstacles and the necessary wide-path (in red).
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It is pointed out that the available floor coordinates are reduced due to the WP (wide-path) of the robot (Schilling, 1990). It should also be noted that for each column position corresponding to scene coordinates Y\n\t\t\t\t\t\n\t\t\t\t\t\tj\n\t\t\t\t\t\n\t\t\t\t\t, there are R row coordinates X\n\t\t\t\t\t\n\t\t\t\t\t\ti\n\t\t\t\t\t. Once perception is introduced, the problem is formulated as finding the optimal cell that brings the WMR close to the desired coordinates (X\n\t\t\t\t\t\n\t\t\t\t\t\td\n\t\t\t\t\t\n\t\t\t\t\t, Y\n\t\t\t\t\t\n\t\t\t\t\t\td\n\t\t\t\t\t) by searching for the closest local desired coordinates (X\n\t\t\t\t\t\n\t\t\t\t\t\tld\n\t\t\t\t\t\n\t\t\t\t\t, Y\n\t\t\t\t\t\n\t\t\t\t\t\tld\n\t\t\t\t\t) within the available local coordinates (X\n\t\t\t\t\t\n\t\t\t\t\t\ti\n\t\t\t\t\t\n\t\t\t\t\t, Y\n\t\t\t\t\t\n\t\t\t\t\t\tj\n\t\t\t\t\t). In this sense, perception is considered to be a local receding horizon on which the trajectory is planned. The local desired cell is obtained by minimizing a cost function J that should act as a potential field corridor. Thus, the cost function is minimized by attracting the robot to the desired objective through the free available local cell coordinates. It is noted that from local perception analysis and attraction potential fields a local on field path can be obtained. The subsequent subsections infer control law parameter analysis by considering a set of path possibilities obtained within the perception field mentioned in this section.
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3.2. The path-tracking experimental approach by using LMPC methods
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The path tracking performance is improved by the adequate choice of a cost function that is derived from (3) and consists of a quadratic expression containing some of the following four parameters to be minimized:
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The squared Euclidean approaching point distance (APD) between the local desired coordinates, provided by the on-robot perception system, and the actual robot position. It corresponds with the parameter “P” of the LMPC cost function given by (3).
The squared trajectory deviation distance (TDD) between the actual robot coordinate and a straight line that goes from the robot coordinates, when the local frame perception was acquired, and the local desired coordinates belonging to the referred frame of perception. It corresponds with the parameter “Q” of the cost function shown by (3).
The third parameter consists of the squared orientation deviation (OD); it is expressed by the difference between the robot desired and real orientations. It corresponds with the parameter “R” of the LMPC cost function depicted by (3).
The last parameter refers to changes allowed to the input signal. It corresponds with the parameter “S” of the LMPC cost function given by (3).
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One consideration that should be taken into account is the different distance magnitudes. In general, the approaching distance could be more than one meter. However, the magnitude of the deviation distance is normally in the order of cm, which becomes effective only when the robot is approaching the final desired point. Hence, when reducing the deviation distance further to less than 1cm is attempted, an increase, in the weight value for the deviation distance in the cost function, is proposed.
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The subsequent subsections use statistical knowledge for inferring APD (P) and TDD (Q) or APD (P) and OD (R) factor performances as a function of the kind of paths to be tracked. Other cost function parameters are assumed to be equal to zero.
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3.3. Experimental tuning of APD and TDD factors
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This subsection presents the results achieved by using factorial design in order to study the LMPC cost function tuning when APD and TDD factors are used. Path-tracking performance is analyzed by the mean of the different factor weights. The experiments are developed by considering five different kinds of trajectories within the reduced field of view as shown in Fig. 5. Therefore, straight, wide left turning, less left turning, wide right turning and less right turning trajectories are tested. Experiments are conducted by using factorial design with two levels of quantitative factors (Box et al, 2005). Referred to the cost function, let us assume that high value (H) is equal to “1” and low value (L) is equal to “0.5”. For each combination of factors three different runs are experimented. The averaged value of the three runs allows statistical analysis for each factor combination. From these standard deviations, the importance of the factor effects can be determined by using a rough rule that considers the effects when the value differences are similar or greater than 2 or 3 times their standard deviations. In this context, the main effects and lateral effects, related to APD and TDD, are analyzed. Fig. 6 shows the four factor combinations (APD, TDD) obtained by both factors with two level values.
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Figure 6.
The different factor combinations and the influence directions, in which the performances should be analyzed.
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The combinations used for detecting lateral and main effect combinations are highlighted by blue arrows. Thus, the main effect of APD factor, ME\n\t\t\t\t\t\n\t\t\t\t\t\tAPD\n\t\t\t\t\t, can be computed by the following expression:
Path-tracking statistical performances to be analyzed in this research are represented by Y. The subscripts depict the different factor combinations. The main effect for TDD factor, ME\n\t\t\t\t\t\n\t\t\t\t\t\tTDD\n\t\t\t\t\t, is computed by:
The detailed measured statistics with parameters such as time (T), trajectory error (TE), and averaged speeds (AS) are presented in (Pacheco & Luo, 2011). The results were tested for straight trajectories, wide and less left turnings, and wide and less right turnings. The main and lateral effects are represented in Table 4.
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The performance is analyzed for the different trajectories:
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The factorial analysis for straight line trajectories, (σT = 0.16s, σTE = 0.13cm, σAS = 2.15cm/s), depicts a main time APD effect of -0.45s, and an important lateral effect of -0.6s and -0.32cm. Speed lateral effect of only 1.9cm/s is not considered as meaningful. Considering lateral effects that improve time and accuracy, high values (APD, TDD) are proposed for both factors.
The analysis for wide left turning trajectories, (σT = 0.26s, σTE = 0.09cm, σAS = 0.54cm/s) show negative APD main effect of 0.53s, and 0.15cm. However, the TDD factor tends to decrease the time and trajectory deviation. The 0.3cm/s speed TDD main factor is irrelevant. In this case, low value for APD factor and high value for the TDD factor is proposed.
The factor analysis for less left turning, (σT = 0.29s, σTE = 0.36cm, σAS = 0.84cm/s), depicts a considerable lateral effect of -0.46s and -0.31cm. Speed -0.2cm/s lateral effect is not important. In this sense high values are proposed for APD and TDD factors.
The analysis for wide right turning, (σT = 0.18s, σTE = 0.15cm, σAS = 1.04cm/s) does not provide relevant clues, but small time improvement seems to appear when TDD factor is set to a low value. Low values are proposed for APD and TDD factors.
Finally, the factorial analysis for less right turning trajectories, (σT = 0.12s, σTE = 0.18cm, σAS = 1.94cm/s), depicts APD and lateral effects that increase the trajectory time with 0.32s and 0.44s. Main or lateral effects related to the speed have not been detected. Low values are proposed for APD and TDD factors.
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Straight line trajectory
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Parameter Performance
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Main Effect TDD factor
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Main Effect APD factor
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Lateral Effect TDD & APD factors
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Time
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-0.05s
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-0.45s
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-0.6s
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Trajectory accuracy
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-0.18cm
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-0.14cm
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-0.32cm
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Averaged speed
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1.25cm/s
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0.6cm/s
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1.9cm/s
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Wide left turn trajectory
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Time
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-0.34s
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0.53s
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0.16s
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Trajectory accuracy
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-0.17cm
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0.15cm
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-0.01cm
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Averaged speed
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0.3cm/s
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0.4cm/s
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0.7cm/s
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Slight left turn trajectory
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Time
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-0.24s
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0.02s
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-0.46s
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Trajectory accuracy
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-0.14cm
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-0.17cm
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-0.31cm
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Averaged speed
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0.8cm/s
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-1cm/s
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-0.2cm/s
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Wide right turn trajectory
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Time
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0.27s
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-0.10s
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0.17s
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Trajectory accuracy
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-0.22cm
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0.1cm
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-0.12cm
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Averaged speed
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0.7cm/s
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0.2cm/s
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0.9cm/s
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Slight right turn trajectory
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Time
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0.12s
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0.32s
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0.44s
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Trajectory accuracy
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-0.18cm
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-0.06cm
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-0.25cm
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Averaged speed
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-1.3cm/s
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2.8cm/s
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1.5cm/s
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Table 4.
Main and lateral effects
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3.4. Experimental performance by using fixed or flexible APD & TDD factors
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Once factorial analysis is carried out, this subsection compares path-tracking performance by using different control strategies. The experiments developed consist in analyzing the performance when a fixed factor cost function or a flexible factor cost function is used. The trajectories to be analyzed are formed by straight lines, less right or left turnings, and wide right or left turnings. The fixed factor cost function maintains the high values for APD and TDD factors, while the flexible factor cost function is tested as function of the path to be tracked.
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Different experiments are done; see (Pacheco & Luo, 2011). As instance one experiment consists in tracking a trajectory that is composed of four points ((0, 0), (-25, 40), (-25, 120), (0, 160)) given as (x, y) coordinates in cm. It consists of wide left turning, straight line and wide right turning trajectories. The results obtained by using fixed and flexible factor cost function are depicted in Table 5. Three runs are obtained for each control strategy and consequently path-tracking performance analysis can be done.
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Results show that flexible factor strategy improves an 8% the total time performance of the fixed factor strategy. The turning trajectories are done near 50% of the path performed. Remaining path consists of a straight line trajectory that is performed with same cost
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Figure 7.
a) Trajectory-tracking experimental results by using flexible or fixed cost function. (b) WMR orientation experimental results by using flexible or fixed cost function. (c) Left wheel speed results by using flexible or fixed cost function. (d) Right wheel speed results by using flexible or fixed cost function.
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function values for fixed and flexible control laws. It is during the turning actions, where the two control laws have differences, when time improvement is nearly 16%. Fig. 7 shows an example of some results achieved. Path-tracking coordinates, angular position, and speed for the fixed and flexible cost function strategies are shown.
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It can be seen that flexible cost function, when wide left turning is performed approximately during the first three seconds, produces less maximum speed values when compared with fixed one. However, a major number of local maximum and minimum are obtained. It results in less trajectory deviation when straight line trajectory is commanded. In general flexible cost function produces less trajectory error with less orientation changes and improves time performance.
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Trajectory points: (0,0), (-25,40), (-25,120), (0,160) ((x,y) in cm)
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Time (s)
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Trajectory error (cm)
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Averaged Speed (cm/s)
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Experiment
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Fixed Law
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Flexible Law
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Fixed Law
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Flexible Law
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Fixed Law
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Flexible Law
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Run 1
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10,5
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10,3
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3,243
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3,653
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18,209
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16,140
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Run 2
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10,9
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9,8
\n\t\t\t\t\t\t\t
3,194
\n\t\t\t\t\t\t\t
2,838
\n\t\t\t\t\t\t\t
16,770
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16,632
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Mean
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10,70
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10,05
\n\t\t\t\t\t\t\t
3,219
\n\t\t\t\t\t\t\t
3,245
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17,489
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16,386
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Variance
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0,0800
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0,1250
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0,0012
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0,3322
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1,0354
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0,1210
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Standart deviation
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0,2828
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0,3536
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0,0346
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0,5764
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1,0175
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0,3479
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Table 5.
Results obtained by using fixed or flexible cost function
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Developed experiences with our WMR platform show that flexible LMPC cost function related with the path to be tracked can improve the control system performance.
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3.5. Experimental tuning using APD and OD factors
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In a similar way APD and OD factors can be used. This subsection compares path-tracking performance by using different control strategies. The experiments developed consist in analyzing the performance when a fixed factor cost function or a flexible factor cost function is used. The trajectories to be analyzed are formed by straight lines, less right or left turnings, and wide right or left turnings. The fixed factor cost function maintains the high values for APD and OD factors, while the flexible factor cost function is tested as function of the path to be tracked. The experiments developed show the measured performance statistics, time, trajectory accuracy, and averaged speeds, for straight trajectories, wide and less left turnings, and wide and less right turnings. The standard deviation obtained as well as the main and lateral effects are represented in Table 6. The time, trajectory error and averaged speed standard deviations are respectively denoted by σT, σTE, and σAS. Table 6 represents the experimental statistic results obtained for the set of proposed trajectories. The standard deviations computed for each kind of trajectory by testing the different factor weights under different runs are also depicted.
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The main and lateral effects were calculated by using (6), (7), (8), and the mean values obtained for the different factor combinations. Therefore, in Table 6 are highlighted the significant results achieved using experimental factorial analysis. The inferred results obtained can be tested using different trajectories.
\n\t\t\t\t\t\t\t\tWide left turning\n\t\t\t\t\t\t\t
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Parameters
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OD
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APD
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APD & OD
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Time (s) σT = 0.06s
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-0,10
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0,20
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0,10
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Trajectory error (cm) σTE = 0.18cm
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0,36
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0,38
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0,02
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Speed (cm/s) σAS = 0.59cm/s
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0,36
\n\t\t\t\t\t\t\t
-0,87
\n\t\t\t\t\t\t\t
-0,52
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Less left turning
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Parameters
\n\t\t\t\t\t\t\t
OD
\n\t\t\t\t\t\t\t
APD
\n\t\t\t\t\t\t\t
APD & OD
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Time (s) σT = 0.09s
\n\t\t\t\t\t\t\t
-0,12
\n\t\t\t\t\t\t\t
0,07
\n\t\t\t\t\t\t\t
-0,05
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Trajectory error (cm) σTE = 0.11cm
\n\t\t\t\t\t\t\t
0,58
\n\t\t\t\t\t\t\t
1,08
\n\t\t\t\t\t\t\t
0,50
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Speed (cm/s) σAS = 0.92cm/s
\n\t\t\t\t\t\t\t
0,60
\n\t\t\t\t\t\t\t
-0,13
\n\t\t\t\t\t\t\t
0,47
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tWide right turning\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Parameters
\n\t\t\t\t\t\t\t
OD
\n\t\t\t\t\t\t\t
APD
\n\t\t\t\t\t\t\t
APD & OD
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Time (s) σT = 0.11s
\n\t\t\t\t\t\t\t
0,10
\n\t\t\t\t\t\t\t
0,35
\n\t\t\t\t\t\t\t
0,45
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Trajectory error (cm) σTE = 0.08cm
\n\t\t\t\t\t\t\t
0,44
\n\t\t\t\t\t\t\t
0,45
\n\t\t\t\t\t\t\t
0,01
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Speed (cm/s) σAS = 0.67cm/s
\n\t\t\t\t\t\t\t
-0,58
\n\t\t\t\t\t\t\t
-1,67
\n\t\t\t\t\t\t\t
-2,25
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\tLess right turning\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Parameters
\n\t\t\t\t\t\t\t
OD
\n\t\t\t\t\t\t\t
APD
\n\t\t\t\t\t\t\t
APD & OD
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Time (s) σT = 0.26s
\n\t\t\t\t\t\t\t
-0,07
\n\t\t\t\t\t\t\t
0,07
\n\t\t\t\t\t\t\t
0,00
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Trajectory error (cm) σTE = 0.20cm
\n\t\t\t\t\t\t\t
1,38
\n\t\t\t\t\t\t\t
0,65
\n\t\t\t\t\t\t\t
-0,73
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Speed (cm/s) σAS = 0.13cm/s
\n\t\t\t\t\t\t\t
-0,33
\n\t\t\t\t\t\t\t
-0,14
\n\t\t\t\t\t\t\t
-0,48
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 6.
Main and lateral effects
\n\t\t\t\t
The experiments developed consist in analyzing the time performance when a fixed factor cost function or a flexible factor cost function is used. The trajectories to be analyzed are formed by straight lines, less right or left turnings, and wide right or left turnings. The fixed factor cost function maintains the high values for APD and OD factors, while the flexible factor cost function is tested as function of the trajectory to be tracked. The experiments presented consist in tracking a trajectory that is composed of three points ((0, 0), (-25, 40), (-25, 120)) given as (x, y) coordinates in cm. The results obtained by using fixed and flexible factor cost function are depicted in Table 7.
\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Trajectory (x,y) in cm: (0,0), (-25,40), (-25,120)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Features
\n\t\t\t\t\t\t\t
Time (s)
\n\t\t\t\t\t\t\t
Error (cm)
\n\t\t\t\t\t\t\t
Aver. speed (cm/s)
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Experiment
\n\t\t\t\t\t\t\t
Fixed
\n\t\t\t\t\t\t\t
Flexible
\n\t\t\t\t\t\t\t
Fixed
\n\t\t\t\t\t\t\t
Flexible
\n\t\t\t\t\t\t\t
Fixed
\n\t\t\t\t\t\t\t
Flexible
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Run 1
\n\t\t\t\t\t\t\t
7,2
\n\t\t\t\t\t\t\t
7,0
\n\t\t\t\t\t\t\t
3,8
\n\t\t\t\t\t\t\t
3,0
\n\t\t\t\t\t\t\t
19,4
\n\t\t\t\t\t\t\t
17,5
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Run 2
\n\t\t\t\t\t\t\t
7,4
\n\t\t\t\t\t\t\t
6,6
\n\t\t\t\t\t\t\t
2,2
\n\t\t\t\t\t\t\t
3,5
\n\t\t\t\t\t\t\t
16,5
\n\t\t\t\t\t\t\t
20,1
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Mean
\n\t\t\t\t\t\t\t
7,3
\n\t\t\t\t\t\t\t
6,8
\n\t\t\t\t\t\t\t
3,0
\n\t\t\t\t\t\t\t
3,2
\n\t\t\t\t\t\t\t
18,0
\n\t\t\t\t\t\t\t
18,8
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Variance
\n\t\t\t\t\t\t\t
0,02
\n\t\t\t\t\t\t\t
0,1
\n\t\t\t\t\t\t\t
1,3
\n\t\t\t\t\t\t\t
0,1
\n\t\t\t\t\t\t\t
4,2
\n\t\t\t\t\t\t\t
3,4
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Stand. dev.
\n\t\t\t\t\t\t\t
0,14
\n\t\t\t\t\t\t\t
0,3
\n\t\t\t\t\t\t\t
1,1
\n\t\t\t\t\t\t\t
0,3
\n\t\t\t\t\t\t\t
2,0
\n\t\t\t\t\t\t\t
1,9
\n\t\t\t\t\t\t
\n\t\t\t\t\t
Table 7.
Experimental performances
\n\t\t\t\t
Two runs are obtained for each strategy and consequently time performance analysis can be done. The averaged standard deviation between the two cost function systems is of 0.22s, and the difference of means are 0.5s. Thus, flexible factor strategy improves a 6.85% the time performance of the fixed factor strategy. However, left turning is done only a 33% of the trajectory. Thus, time improvement during the left turning is of near 20%. Fig. 8 shows an example of some results achieved. Path-tracking coordinates, angular position, and speed for the fixed and flexible cost function strategies are shown. Trajectory error and averaged speed statistical results are not significant, due to the fact that the differences of means between fixed and flexible laws are less than two times the standard deviations.
\n\t\t\t\t
Figure 8.
a) Trajectory-tracking experimental results by using flexible or fixed cost function. (b) WMR orientation experimental results by using flexible or fixed cost function. (c) Left wheel speed results by using flexible or fixed cost function. (d) Right wheel speed results by using flexible or fixed cost function.
\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
4. Conclusion
\n\t\t\t
This research can be used on dynamic environments in the neighborhood of the robot. On-line LMPC is a suitable solution for low level path-tracking. LMPC is more time expensive when compared with traditional PID controllers. However, instead of PID speed control approaches, LMPC is based on a horizon of available coordinates within short prediction horizons that act as a reactive horizon. Therefore, path planning and convergence to coordinates can be more easily implemented by using LMPC methods. In this way, contractive constraints are used for guaranteeing the convergence towards the desired coordinates. The use of different dynamic models avoids the need of kinematical constraints that are inherent to other MPC techniques applied to WMR. In this context the control law is based on the consideration of two factors that consist of going straight or turning. Therefore, orientation deviation or trajectory deviation distance can be used as turning factors. The methodology used for performing the experiments is shown. From on-robot depicted experiences, the use of flexible cost functions with relationships to the path to be tracked can be considered as an important result. Thus, control system performance can be improved by considering different factor weights as a function of path to be followed.
\n\t\t\t
The necessary horizon of perception is constrained to just few seconds of trajectory planning. The short horizons allow real time implementations and accuracy trajectory tracking. The experimental LMPC processing time was 45ms, (m=3, n=5), running in the WMR embedded PC of 700MHz. The algorithms simplicity is another relevant result obtained. The factorial design, with two levels of quantitative factors, is presented as an easy way to infer experimental statistical data that allow testing feature performances as functionof the different factor combinations. Further studies on LMPC should be done in order to analyze its relative performance with respect to other control laws or to test the cost function performance when other factors are used. The influence of the motor dead zones is also an interesting aspect that should make further efforts to deal with it.
\n\t\t
\n\t
Acknowledgments
\n\t\t\t
This work has been partially funded by the Commission of Science and Technology of Spain (CICYT) through the coordinated projects DPI2007-66796-C03-02 and DPI 2008-06699-C02-01.
\n\t\t
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/16068.pdf",chapterXML:"https://mts.intechopen.com/source/xml/16068.xml",downloadPdfUrl:"/chapter/pdf-download/16068",previewPdfUrl:"/chapter/pdf-preview/16068",totalDownloads:2189,totalViews:184,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,dateSubmitted:"October 25th 2010",dateReviewed:"February 6th 2011",datePrePublished:null,datePublished:"July 5th 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/16068",risUrl:"/chapter/ris/16068",book:{slug:"advanced-model-predictive-control"},signatures:"Lluís Pacheco, Xavier Cufí and Ningsu Luo",authors:[{id:"30281",title:"Dr.",name:"Lluís",middleName:null,surname:"Pacheco",fullName:"Lluís Pacheco",slug:"lluis-pacheco",email:"lluispa@eia.udg.edu",position:null,institution:null},{id:"32046",title:"Dr.",name:"Ningsu",middleName:null,surname:"Luo",fullName:"Ningsu Luo",slug:"ningsu-luo",email:"ningsu.luo@udg.edu",position:null,institution:null},{id:"118672",title:"Dr.",name:"Xavier",middleName:null,surname:"Cufí",fullName:"Xavier Cufí",slug:"xavier-cufi",email:"xcuf@eia.udg.edu",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The control system identification and the MPC formulation",level:"1"},{id:"sec_2_2",title:"2.1. Experimental model and system identification",level:"2"},{id:"sec_3_2",title:"2.2. Dynamic MPC techniques for local trajectory tracking",level:"2"},{id:"sec_3_3",title:"2.2.1. The LMPC formulation",level:"3"},{id:"sec_4_3",title:"Table 3.",level:"3"},{id:"sec_7",title:"3. Tuning the control law parameters by using path-tracking experimental results",level:"1"},{id:"sec_7_2",title:"3.1. The local field of perception",level:"2"},{id:"sec_8_2",title:"3.2. The path-tracking experimental approach by using LMPC methods",level:"2"},{id:"sec_9_2",title:"3.3. Experimental tuning of APD and TDD factors",level:"2"},{id:"sec_10_2",title:"3.4. Experimental performance by using fixed or flexible APD & TDD factors",level:"2"},{id:"sec_11_2",title:"3.5. Experimental tuning using APD and OD factors",level:"2"},{id:"sec_13",title:"4. 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IEEE Transaction on Robotics, 21\n\t\t\t\t\t2 , (April 2005) 188-195, 1552-3098\n\t\t\t\t\n\t\t\t'},{id:"B13",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPacheco\n\t\t\t\t\t\t\tL.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLuo\n\t\t\t\t\t\t\tN.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCufí\n\t\t\t\t\t\t\tX.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2008 Predictive Control with Local Visual Data, In: Robotics, Automation and Control, Percherková, P., Flídr, M., Duník, J., 289\n\t\t\t\t\t306 , Publisher I-TECH, 978-9-53761-918-4 Printed in Croatia.\n\t\t\t'},{id:"B14",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPacheco\n\t\t\t\t\t\t\tL.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLuo\n\t\t\t\t\t\t\tN.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFerrer\n\t\t\t\t\t\t\tI.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCufí\n\t\t\t\t\t\t\tX.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2009 Interdisciplinary Knowledge Integration Through an Applied Mobile Robotics Course, The International Journal of Engineering Education, 25\n\t\t\t\t\t4 (July, 2009), 830\n\t\t\t\t\t840 , ISSN: 0949-149X\n\t\t\t'},{id:"B15",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPacheco\n\t\t\t\t\t\t\tL.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLuo\n\t\t\t\t\t\t\tN.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2011 Mobile robot local trajectory tracking with dynamic model predictive control techniques, International Journal of Innovative Computing, Information and Control, 7\n\t\t\t\t\t6 (June 2011), in press, 1349-4198\n\t\t\t\t\n\t\t\t'},{id:"B16",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSchilling\n\t\t\t\t\t\t\tR. J.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1990 Fundamental of Robotics. Prentice-Hall (Ed.), New Jersey (USA) 1990, 0-13334-376-6\n\t\t\t\t\n\t\t\t'},{id:"B17",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWan\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2007 Computational reliable approaches of contractive MPC for discrete-time systems, PhD Thesis, University of Girona.\n\t\t\t'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Lluís Pacheco",address:"",affiliation:'
'}],corrections:null},book:{id:"160",title:"Advanced Model Predictive Control",subtitle:null,fullTitle:"Advanced Model Predictive Control",slug:"advanced-model-predictive-control",publishedDate:"July 5th 2011",bookSignature:"Tao Zheng",coverURL:"https://cdn.intechopen.com/books/images_new/160.jpg",licenceType:"CC BY-NC-SA 3.0",editedByType:"Edited by",isbn:null,printIsbn:"978-953-307-298-2",pdfIsbn:"978-953-51-6015-1",editors:[{id:"10515",title:"Prof.",name:"Tao",middleName:null,surname:"Zheng",slug:"tao-zheng",fullName:"Tao Zheng"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"16055",title:"Fast Model Predictive Control and its Application to Energy Management of Hybrid Electric Vehicles",slug:"fast-model-predictive-control-and-its-application-to-energy-management-of-hybrid-electric-vehicles",totalDownloads:4004,totalCrossrefCites:5,signatures:"Sajjad Fekri and Francis 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1. Introduction
Brassica spp., commonly known as rapeseed-mustard, plays an important role in the Indian economy by providing edible oils, vegetables, condiments and animal feed [1]. Nine oilseeds are the primary sources of vegetable oil in India. Among them soybean (39%), groundnut (26%) and rapeseed-mustard (24%) contribute more than 88% of total oilseeds production in the country. However, rapeseed-mustard (31%) contributes maximum in terms of edible oil production followed by soybean (26%) and groundnut (25%) in the country [2].
Rapeseed-mustard is the third major edible oilseed crop of the world after soybean and palm oil. Globally, as per USDA during 2018-2019, it was grown over 36.6 million hectares and produced 72.4 MT with a productivity of 19.8 q/ha. Globally, India accounts 19.8% of total acreage and 9.8% of total production. Rapeseed-mustard (8.3 MT) is the third most important annual oilseed crop in India, next to soybean (13.6 MT) and groundnut (9.1 MT) [2]. In India, rapeseed-mustard is widely grown in diverse agro-climatic environments from North-East, North-West, Central to Southern states under different conditions such as sole crop/mixed crop, early/timely/late, rainfed/irrigated and saline or alkaline soils [3]. Based on average of 2014-2015 to 2018-2019 area and production data, major rapeseed-mustard growing states are Rajasthan (producing 44.9% of total rapeseed-mustard from 40.7% area), Madhya Pradesh (producing 11.3% from 11.9% area) and Uttar Pradesh (producing 10.6% from 11.2% area). Rapeseed-mustard crops in India comprise eight species viz., Indian mustard, toria, black mustard, yellow sarson, brown sarson, gobhi sarson, karan rai and taramira (Table 1).
Species
Common name
Type of Pollination
Chromosome No. (2n)
Genome
Genome size (Mb)
B. juncea (L.) Czern.
Indian mustard
Often-self
36
AABB
~922
B. carinata A. Braun
Karan rai or Ethiopian mustard
Often-self
34
BBCC
—
B. napus L.
Gobhi sarson
Self and cross
38
AACC
~1130
B. nigra (L.) Koch
Black mustard
Cross
16
BB
~558
B. oleracea L.
Cabbage, cauliflower etc.
Cross
18
CC
~630
B. rapa L.
var. brown sarson
Lotni type: Cross Tora type: Self
20
AA
~485
var. toria
Cross
var. yellow sarson
Self
Eruca sativa
Taramira
Self
22
EE
—
B. alba Rab. (Syn. Sinapis alba)
White mustard
Self
24
SS
—
Table 1.
List of limited and importantly cultivated species of Brassica species.
2. Origin
Historically, the cultivation of Brassica spp. has been quoted in numerous ancient scriptures and believed to be cultivated on or prior to 5000 BC. It has also been reported that mustard crop had cultivated in Channhu-daro of Harrapan ancient civilization during 2300-1750 BC [4]. There is ambiguity in the history as the origin of B. juncea is concerned. It had been believed that center of origin for B. juncea is Middle-East, where putative parents i.e. B. nigra and B. rapa would have crossed with each other. Later on, it had been disseminated to other parts of the world such as Europe, Asia, and Africa etc. [5]. Today, there are two centers of diversity i.e. China and Eastern India based on the prevalence of their wild progenitors and relatives. At present, it has been proved that there are two geographical races i.e. Chinese and Indian of B. juncea based on molecular and biochemical studies [6].
In 1935, Nagaharu U [7] proposed a theory known as U’s triangle to show genetic relationships based on artificial inter-specific hybridization experiments among six species, namely; B. rapa, B. nigra, B. oleracea, B. carinata, B. napus and B. juncea. As per theory, three allotetrapolyploid species (B. napus, B. juncea and B. carinata) were derived by natural hybridization of three basic diploid species (B. rapa, B. nigra and B. oleracea) followed by genome doubling (Figure 1). Nowadays, with the accomplishments of genome sequencing of Brassica taxa, this hypothesis has been increasingly accepted. Furthermore, it has been scientifically proved that allotetraploid B. napus and B. juncea had been derived from their diploid parents based on comparative genomic analysis and the results were in accordance with ‘U’ triangle [8].
Figure 1.
U’s triangle showing genetic relationship among six Brassica species [7].
3. Distribution
Brassicas include large number of crops under cultivation. Among them, the Indian mustard occupies maximum area (> 90%) and predominantly cultivated in North-Western states followed by some nontraditional areas of Central and Southern states of the country [1]. The lotni (cross-pollinated) and tora (self-pollinated) are two different ecotypes of brown sarson. Earlier one is mainly cultivated in temperate regions of the country such as parts of Jammu, Kashmir and hilly areas of Himachal Pradesh, whereas later one is cultivated in parts of Eastern Uttar Pradesh [3]. However, yellow sarson is predominantly cultivated in parts of Bihar, West Bengal and Orissa. Toria is mainly used as short period crop in parts of Bihar, West Bengal, Orissa and Assam. Whereas, it is grown as a catch crop in Haryana, Himachal Pradesh, Madhya Pradesh, Punjab, Uttarakhand and Western Uttar Pradesh. Taramira, relatively more drought tolerant, is cultivated in drier parts of Rajasthan, Uttar Pradesh and Haryana. However, karan rai and gobhi sarson have limited area under cultivation in India [1].
4. Breeding approaches in rapeseed-mustard
4.1 Abiotic stresses
Plant stress factors can be elucidated as any adverse condition or substance that affects the growth, reproduction, metabolism and development of the plant [3]. Acclimatization or hardening refers to exposure of unfavorable environmental circumstance to the plant and thereby results into physiological adjustment that protects it from injury or impaired growth which is mostly occurred due to environmental stresses [9]. There might be fixed genetic changes if plant faces several generations under constant stress condition by selective environmental pressure and thereby population show adaptation to changed environment. Abiotic factors are the main yield-limiting factors for crop plants including rapeseed-mustard. The major abiotic factors are- moisture variation (drought and flood), temperature variation (heat, cold and frost), salinity and heavy metal that adversely affect the metabolic pathways and thereby result into yield penalty.
4.1.1 Drought stress
Globally, rapid climate change under anthropogenic accelerated interventions crafts drought a major menace to the agricultural production system and consequently has a great challenge to the global food and nutritional security. Plants have different ways to synergies with drought stress such as modifications in plant growth, behavior, morphology, and physiology. In Brassica, drought tolerance is a complex trait and thereby associated with different traits; and can be evaluated by various indicators. Moreover, it is difficult to choose all the exiting indicators at a time to use in breeding programs for crop improvement. Drought can adversely affect plant growth at various stages from seed germination to reproduction and flowering to harvesting, and ultimately results into oil and yield penalty [3]. Prolonged drought reduces chlorophyll content mostly due to impaired functioning of thylakoid membrane and heavy loss of pigments [10]. In the context, the pattern of gene expression of those traits which are associated with osmotic balance, water transport, damage repair and oxidative stress will be altered by prolonged drought stress (Table 2). Thus, drought is one of the major factors to reduce potential yield of crop plants and introgression of traits from wild relatives can be used for the development of drought resilient cultivars in rapeseed-mustard.
Brief summary of abiotic stress tolerance associated genes and their functions.
4.1.2 Salt stress
Recent advances in molecular breeding have been characterized and genetically mapped various salt related genes in plants. Gradual increase of the understanding of several biochemical, and physiological mechanisms and pathways of salt related genes has made it easy to develop genetically improved varieties which are more resilient and high yielding under salinity stress. In this context, transgenic approaches have also been used to know the effect of salt tolerant genes into the different genetic background by up-regulating or down-regulating genes under salt stress [33]. The progress under salt tolerance is great in major agricultural crops such as wheat, rice, mustard and tomato. A large number of gene (s)/QTLs have been mapped as well as cloned [33]. As Brassica crops are concerned, there are limited studies on salt regulating genes or QTLs across the world. In India, only limited salt tolerant varieties have been developed so far such as “CS56” and breeding approaches are not as much successful as to other stresses [3]. It is need of the hour to understand the mechanism of salt tolerance and to identify stable salt tolerance genotypes from available genetic resources by extensive screening methods to use them in breeding programs. Researchers have done excellent work on ion homeostasis and osmolytes regulation by using transgenic approach in Brassica crops [34] and identified few candidate genes (Table 2).
Apparently, both drought and salinity stress have few similarities in plants. Both stresses are primarily responsible for cellular dehydration, which removes water from the cytoplasm into the intercellular space [35]. Based on the functional similarity of both the stresses in plants, it can be concluded that plants have almost identical mechanism to deal with both stresses. In the present scenario, researchers are extensively working on model plant i.e. A. thaliana to understand the genetics of salt and drought stress tolerance, which can positively help to develop tolerance cultivars in Brassica spp. and will improve agronomically important traits [36].
4.1.3 Heat stress
As the global warming is increasing due to unwarranted human activities, heat stress has become a major factor to hamper plant growth and development in agricultural crops including rapeseed-mustard. Early sowing of Indian mustard, have various advantages as enlisted by Kaur and coworkers [37] but high temperature during the germination stage leads to reduction in the plant emergence and poor plant stand. The yield potential of Indian mustard was significantly reduced under late sown condition compared to timely sown due to terminal heat stress [38]. The reduction in emergence of Indian mustard due to hot soils can lead to substantial economic losses [39]. Where irrigation is available and multiple cropping system followed, especially in Central and North-Western plain zones, sowing of the mustard crop is delayed up to end of November due to late vacation of Kharif crop, leads to exposure of the crop to high temperature at maturity.
Rapeseed-mustard is adversely affected by heat stress (35/15 °C) at the early stage of flowering. Moreover, yield penalty can be avoided if high temperature occurs during early pod formation. In this context, B. rapa is more sensitive to high temperature whereas B. juncea and B. napus are equally affected [40]. It has been reported that optimal temperature for B. napus is lower than B. juncea and B. rapa [41]. Generally, as temperature increased, the number of pods produced by the plants increased and seed weight decreased. High temperature has a direct effect on the formation of reproductive organs. More research is needed under controlled environments to identify the critical temperature, sensitive reproductive organ stage, source-sink relationship, and genotypic variations for heat stress tolerance and must be verified under natural conditions [42].
4.1.4 Low temperature stress
Freezing injury has adverse effect on plant growth and development, and thereby leads to yield penalty. Seed germination is seriously affected by low temperature. Plant stress hormones such as Brassinolide (BR) regulate plant physiological pathways and helps in plant protection to combat low temperature stress [43]. Exogenous application of BR increased cold stress tolerance in A. thaliana and B. napus [44]. In this context, BR increases chlorophyll content, PS-II, antioxidant enzymatic activities and protect photosynthetic membrane system from oxidative damage [45]. It has been reported that accumulation of reactive oxygen species such as superoxide anion, hydrogen peroxide, singlet oxygen and hydroxyl radical is high under cold stress, and thereby causes oxidative stress in plants which leads to cell death [46]. The B. rapa has been reported more cold tolerance than B. napus. The impact of heat stress is high than cold stress because of inactivation of RuBisCO and/or other associated enzymes under heat stress. Intriguingly, B. oleracea is cold tolerant due to its acclimatization in cold regions of Europe, where summer temperature is also low and crop had domesticated since long back.
Thus, acclimatization, domestication, adaptive trans-generational plasticity and genetic adaptation phenomenon can work simultaneously to abiotic stress tolerance in Brassica species.
4.2 Biotic stresses
A number of biotic stresses adversely affect the yield potential of rapeseed-mustard in India. The major diseases are- Alternaria blight (Alternaria brassicae and A. brassicicola), white rust (Albugo candida), stem rot (Sclerotinia sclerotiorum), Rhizoctonia rot and downy mildew (Peronospora brassicae); and major insect pests are- aphid (Lipaphis erysimi), mustard saw fly (Athalia proxima) and painted bug (Bagrada hilaris). There are several methods to control insect and disease incidence such as application of pesticides, fungicides, biological agents and other non-chemical techniques. However, the most economic, eco-friendly and cheap way to mitigate these menaces are to use of resistant or tolerant cultivars through convention and molecular breeding approaches.
4.2.1 Alternaria blight
The yield potential of Brassica spp. is adversely affected by Alternaria blight [Alternaria brassicae (Berk) Sacc.] disease. The pathogen can affect the host plant at all stages of growth and highest disease severity was observed during rainy season. The B. juncea and B. rapa are more susceptible than B. carinata and B. napus to Alternaria blight. The researchers have reported several sources of disease tolerance such as B. juncea cv. Divya, and wild species such as Sinapis alba L., B. maurorum, Diplotaxis berthautii and D. erucoides etc. [47]. Higher concentration of phenolic compounds (polyphenol peroxidase, oxidase and catalase), low N content, higher leaf sugar content, and more leaf wax deposition have been reported to deliver resistance to plants against Alternaria blight disease [48]. Pre and post fertilization barriers are major concern while using wild relatives and progenitors as donor source in rapeseed-mustard breeding programs. However, limited sources of B. juncea (PHR 2, RC781, Divya, PAB 9534, and EC 399301) have been reported tolerance against this disease and extensively being used in breeding programs [3].
4.2.2 White rust
White rust [Albugo candida (Pers.) Kuntze] is a destructive disease in B. juncea and B. rapa; and significantly reduces potential yield up to 60% in mustard [49]. Forty-nine races of A. candida have been reported in India based on their infectivity on different Brassica spp. and their cultivars [50]. Most of the varieties under Indian mustard are susceptible to white rust whereas B. carinata and B. napus demonstrate high degree of resistance. Thus, gene introgression from B. carinata and B. napus to B. juncea through interspecific hybridization is essential for development of resistant or tolerant cultivars in the country [51]. The varieties bred for disease tolerance are- JM-1, JM-2, DMH-1 and Basanti etc.
4.2.3 Sclerotinia rot
In rapeseed-mustard, Sclerotinia rot disease is triggered by Sclerotinia sclerotiorum and adversely affects plant growth and development. The disease has turned form minor significance to major one since last decade due to change in climatic condition. Pre-mature ripening is the cause of the disease. The pathogen has an array of alternate host therefore breeding for disease resistant is difficult [3].
4.2.4 Insect (Aphid)
Mustard aphid (Lipaphis erysimi) is one of the major insect pests in rapeseed-mustard and adversely affects plant growth, development, and reproduction; and thereby results into yield penalty. They are also act as vector for plant viral diseases such as turnip mosaic virus. There are several methods to identify resistant source for aphid resistance/tolerance in Brassica family such as based on seedling survival, aphid fecundity, and aphid infestation index etc. Some genotypes of B. juncea such as Glossy B-85, RH 7847, and T 6343 were reported more tolerant to aphid infestation. B. campestris is more susceptible to aphid infestation than B. juncea and B. carinata [3].
4.3 Oil quality improvement
The oil quality for human consumption is determined by its fatty acid composition and concentration. Seed oil with high proportion of unsaturated fatty acid, particularly 16 and 18 carbon chain, is considered suitable for human consumption as edible oil. Rapeseed-mustard is mostly used as oilseed crop in India and its seed contain 35-45% oil content with 92-98% triacylglycerol of fatty acids (C16-C22). Seed oil contains lowermost saturated fat and possesses high proportion of essential fatty acid such as linoleic (C18:2) and linolenic (C18:3) which are not synthesized by human body. Linolenic acid is an essential dietary fatty acid; however, its higher concentration reduces shelf-life of oil because of auto-oxidation [3]. Erucic acid (C22:1) comprises almost 50% of total seed oil fatty acid in rapeseed-mustard and is undesirable for human consumption due to its adverse role in myocardial conductance and increase the level of blood cholesterol. The level of detrimental saturated fatty acid is less in rapeseed-mustard compared to other edible oilseed crops. The major constrains in seed oil are- erucic acid and glucosinolates [52]. Therefore, reduced concentration of glucosinolates and erucic acids is one of the important objectives in quality amelioration of Indian mustard seed oil. It has been reported that genetic inheritance of glucosinolates is complex and mostly are aliphatic (methionine derived) in nature in B. juncea. Genetic control of total glucosinolates in B. juncea has been reported to be under two major genes [53], multiple additive alleles at a single locus with maternal effects involved [54], six to seven genes [55] and up to five major QTLs [56] based on molecular mapping information.
The rapeseed-mustard varieties with low erucic (<2%) and glucosinolates (<30 μ mole/g of defatted cake) are termed as double zero (“00”). The term single zero (“0”) is used when variety contains only one factor either low erucic (<2%) or glucosinolates (<30 μ mole/g of defatted cake). In this context, several efforts have been made to improve oil quality of rapeseed-mustard in India since last three decades. In India, first low erucic acid (“0”) variety was LES-39 (Pusa Karishma) followed by LES-1-27 (Pusa Mustard 21), LET-18 (PM 24), and LET-17 (PM-22) in B. juncea, whereas double zero variety was Pusa Double Zero Mustard 31 (PDZM-1).
4.4 Hybrid breeding
Rapeseed-mustard exploits high level of heterosis but employ difficulty in seed production due to complex flower structure, presence of self-compatibility and thereby self-pollination in nature, however crop also enjoyed cross-pollination (30%) by pollinators such as honey bees. The extent of heterosis was reported by Sun [57] in rapeseed-mustard during early forties and was pioneer to begin with hybridization for exploitation of hybrid vigor. Subsequently, Ogura [58] had successfully transferred male sterile cytoplasm from radish (Raphanus sativus L.) to B. juncea. In this context, several cytoplasmic male sterility systems have been reported such as tour [59] in B. napus, oxyrrhina [59], siifolia [60], trachystoma [61], moricandia [62], catholica [63], alba [62], lyratus [64], canariense [65], erucoides [66], 126-1 [67] and barthauti [68]. Transgenic male sterility (barnase-barstar system) system was also used for exploitation of heterosis and development of hybrid varieties [69, 70]. It has been reported that large number of sterile cytoplasm is available, however only few can be utilized in heterosis due to lack of adequate and efficient fertility restoration system. Therefore, ICAR sponsored project (1989) “Promotion of Research and Development Efforts on Hybrids in Crops” which aimed for systematic and coordinated efforts for hybrid development in rapeseed-mustard in India with two CMS systems (ogu and tour) in B. juncea while polima in B. napus.
In India, heterosis was first reported in brown sarson (B. rapa) by Singh and Mehta [71]. It has been reported that the extent of heterosis is 13 to 99% in B. juncea, 10 to 72% in B. napus, 25 to 110% in B. rapa. Generally, hybridization between genetically distinct groups exploits high level heterosis than within group. Exploitation of high level of heterosis in plants necessitates large and usable heterosis, effective pollination control mechanism, and profitability of seed production [70]. Thus, there is urgent need to improve genetic gain and heterosis in rapeseed-mustard; genetic variability, in terms of variety, can be tested for 2-3 years across the centers in the country through All India Coordinated Research Project [72] and by result of high yielding, stress tolerance and stable variety would be produced.
4.4.1 Cytoplasmic male sterility and hybrids
A large number of CMS systems are available in rapeseed-mustard such as Raphanus/ogu, tour, oxyrrhina, siifolia, trachystoma, moricandia, catholica, lyratus, canariense, erucoides, and barthauti (Table 3). All the CMS sources cannot be directly used in hybridization programme due to their negative effects on plant growth and development such as chlorosis (ogura, oxyrrhina and moricandia), impaired flower opening (tour, trachystoma and lyratus), and also absence of fertility restoration. The chlorosis of three systems (ogu, oxyrrhina, moricandia) had been cured through somatic hybridization by fusing protoplast of chlorotic sterile and normal green plant [74]. The fertility restorer genes (Rfs) were identified in five CMS systems viz. trachystoma, moricandia, catholica, canariense and lyratus in their respective cytoplasmic donor species and restorer can be isolated simultaneously during transfer of sterile cytoplasm.
Important sources of CMS in rapeseed-mustard for hybrid seed production.
The success of hybridization programme, by using CMS system, depends upon availability of efficient fertility restoration. In rapeseed-mustard, the utmost used CMS system in India are-Raphanus/ogu CMS system, B. tournefortii CMS system, Moricandia arvensis CMS system, and Erucastrum canariense CMS system. In India, the first commercial hybrid PGSH 51 (B. napus) was released in 1994 based on tour CMS and yield was increased by 18% over the best hybrid check. The other hybrids are as follow- Hyola 401 hybrid (2000) was based on pol CMS system, NRCHB-506 (2008) on mori cytoplasm, DMH-1 (2008) on 126-1 CMS, and PAC-432 (2009) on ogu cytoplasm etc. The genetic engineering techniques had also utilized for the development of male sterile system to exploit the heterosis in rapeseed-mustard and develop the barnase-barstar male sterile system [69, 70]. Hybrid DMH-11 was developed by Delhi University in India which became India’s first transgenic hybrid through barnase-barstar system. But DMH-11 was not released for commercial cultivation due to resistance from environmental activist in thought of its harm to environment.
4.5 Pre-breeding
Wild progenitors and wild relatives are to be known as repository of valuable traits (quality, agronomic, biotic and abiotic stress tolerance) in crop plants but cannot be introgressed into the cultivated ones due to linkage drag, and cross-incompatibility barriers. Pre-breeding helps to identify the useful traits in wild germplasm and employ its use in breeding programs. The major objective of pre-breeding is to introduce new variation into the species of interest with minimum linkage drag. Molecular markers would play a great role to accelerate the breeding cycle, reduction in cost and time, and increase in the efficiency of introgression in pre-breeding programs [75].
Globally, India (15%) ranked second after China (17%) in terms of repository of Brassica germplasm. In India, National Bureau of Plant Genetic Resources (NBPGR) has contributed 4095 indigenous and 3401 exotic rapeseed-mustard accessions from 1986-2006 [76]. All the efforts have resulted into the collection of a total of 14,722 accessions of cultivated, wild relatives, wild progenitors and related species [3]. There is a wide gap between available germplasm in gene banks and its utilization in the breeding programs due to lack of available identified traits. Thus, there is urgent need to broaden the plant genetic diversity to combat anthropogenically accelerated climate change in the near future.
5. Biotechnological approaches
Rapeseed (B. napus), cultivated in temperate climate, have been believed to originate by natural hybridization between B. oleracea and B. rapa. B. napus was resynthesized by protoplast fusion of B. oleracea and B. rapa to widen genetic diversity and alter oil content. The biotechnical intervention was used either to increase of genetic variability or transfer of desirable traits from other related species such wild relatives, wild progenitors or other unrelated crops to improve yield potential of crop which were not possible due to conventional or classical breeding methods.
5.1 Anther culture
Pollen culture can be used to develop stable homozygous lines by double haploid (DH) technique to improve agronomic traits in B. juncea. Improvement in culture condition and associated factors, which are limiting factor for embryo production, tend to increase efficiency of microspore culture or anther culture in B. juncea [77]. It has been reported that microspore culture is more successful than anther culture due to better response of genotypes for embryo culture. Microspore culture can be used for gene transfer, biochemical studies, and modification of fatty acid profile through mutagenesis [77]. The major factors which affect doubled haploid production are- isolation of microspore, culture media, embryo selection, plant regeneration, and chromosomal duplication. In India, there is no variety under cultivation of this technique.
5.2 Somaclonal variation
Somaclonal variation can be defined as genetic variation in somatic cells due to chromosomal rearrangement and regeneration of variable plants from callus by plant tissue culture. Furthermore, B. juncea variety Prakash produced multiple shoots in cotyledonary callus when high cytokinin and low IAA concentration was used in MS media [78]. A large genetic variation has been created in B. juncea by tissue culture through induced somaclonal, chemical mutagens, and gamma rays induced variation. For example, somaclone- SC-122 was developed with improvement of five traits which were associated with yield improvement [79]. In India, Pusa Jai Kisan (Bio-902) was first somaclonal derived variety in 1993 by using Varuna as a parent and yield was improved by 17.4% over the parent.
5.3 Protoplast culture
Protoplast, cell without cell wall, culture induces protoclonal variation and creates stable genetic variability in rapeseed-mustard by using tissue culture technique. This technique was used B. juncea cv. RLM-198 by using V-47 media for production of somatic embryo and organogenesis. This method can be used for those Brassica species where hybridization is not possible and will help to create genetic variability for betterment of crop improvement.
5.4 Transgenic plants
In crop species, transgenic plants have been developed by using the recombinant DNA technology. It has been widely used to transfer alien gene/chromosomal segment to the recipient parent where naturally gene of interest is absent for betterment of mankind. Various direct and indirect methods have been used for gene transfer in crop plants including rapeseed-mustard and mostly used direct method is Agrobacterium mediated gene transfer for seed yield, seed quality, biotic and abiotic stress tolerance and desirable agronomic traits [80]. As earlier mentioned, transgenic male sterility system was used for production of hybrids in India. Thus, these biotechnological interventions can solve the problems of conventional breeding which are mainly associated with hybridization and selection.
5.5 -Omics approaches
The world of –omics is vast and covers several disciplines such as genomics (total DNA content of organism), transcriptomics (deals with total RNA content), proteomics (deals with total proteins), and metabolomics (total metabolites of an individual). Being amphidiploid and tetraploid in nature, both B. juncea and B. napus need -omics approaches to understand the trait based genetics for improvement of these crops.
5.6 Genomics
Linkage mapping and association studies were used to identify the genomic locations of a particular trait of interest. Genomic locations were identified based on molecular markers in Brassica spp. For example, Mukherjee and coworkers [81] mapped genes governing white rust resistance using BSA in B. juncea. Padmaja and coworkers [82] mapped seed coat color gene and identified microsatellite markers, Ra2-A11, Na10-A08 and Ni4-F11 linked to seed coat color in B. juncea. Furthermore, Liu and coworkers [83] dissected genetic architecture for glucosinolates accumulation in seed and leaves using GWAS in B. napus. Kaur and coworkers [84] carried out genome wide association mapping and candidate gene analysis for pod shatter resistance in B.juncea. Comparative mapping was also used in rapeseed-mustard for different agronomic and quality traits. For example, Cai and coworkers [85] identified candidate gene- BnAP2 for seed weight in B. napus by using comparative mapping with A. thaliana. Bisht and coworkers [86] identified candidate genes, BjuA.GSL-ELONG.a, BjuA.GSL-ELONG.c, BjuA.GSL-ELONG.d, BjuA.GSL-ALK.a and BjuA.Myb28.a for glucosinolates biosynthesis through comparative mapping among A. thaliana, B. oleracea and B. juncea. Genomics has been extensively used for evolutionary studies in Brassica spp. Couvreur and coworkers [87] used nad4 intron 1 marker for phylogenetic analysis to study temporal diversification and establishment of evolutionary pattern in the mustard family. Furthermore, Augustine and coworkers [88] isolated four BjuCYB83A1 genes from B. juncea, which involved in glucosinolates synthesis and through phylogenetic and divergence analysis they have revealed that these genes have evolved via duplication and hybridization of two diploid Brassica genomes i.e. B. rapa and B. nigra.
5.7 Transcriptomics
Transcriptomics contributes the comprehensive understanding about the gene expression, through which it is easy to allocate gene function and its effect on any organism. It has been used for expression studies, gene silencing, and genome editing in Brassica spp. for example, Heng and coworkers [89] identified orf288 gene associated with male sterility in B. juncea through expression analysis of orf288 transcript. Bhattacharya and coworkers [90] studied down regulation of BjAGPase and seed specific expression of AtWRI1 gene of Arabidopsis in order to increase seed lipid content in B. juncea. Savadi and coworkers [91] increased seed weight and seed oil content in Indian mustard through seed specific overexpression of DGAT1 gene of A. thaliana. Zhao and coworkers [92] carried out RNAi mediated gene silencing of mutS homolog1 which results in male sterility in B. juncea due to sub-stoichiometric shifting in ORF220. Zheng and coworkers [93] carried out gene knockout experiment through CRISPR/Cas9 in BnaMAX1 homologs of B. napus, which resulted in reduction in plant height and increase in branch number.
5.8 Proteomics
Proteins are the ultimate products which confer the gene function and govern the phenotypic expression to an individual. Proteomics approaches such as protein expression profiling and comparative proteomics analysis were used to study the gene function in Brassica spp. For example, Mihr and coworkers [94] used “Tournefortii” CMS system of B. napus to study protein content of mitochondrial compartments in male sterile and fertile NILs. Mohammadi and coworkers [95] performed comparative proteome analysis in rapeseed seedlings for root traits under draught stress and concluded that proteins such as H+ ATPase, HSP 90 and EF2 play a key role in draught tolerance. Yousuf and coworkers [96] identified salt stress responsive proteins in the shoots of Indian mustard genotypes through comparative proteome analysis approach. Yousuf and coworkers [97] studied different protein expression profiles of N2 efficient and N2 inefficient Indian mustard in response to elevated CO2 and low N2.
5.9 Metabolomics
Recent efforts in metabolomics have been directed to improve quality and yield of any crop. An integration of metabolomics with other approaches establishes an important relevance in crop improvement. However, metabolomics has not exploited much in mustard breeding, so it would be an emerging field of research for Brassica improvement. Few studies have been carried out in B. juncea. For example, Sinha and coworkers [98] performed metabolic engineering of fatty acid biosynthesis in order to improve nutritional quality of seed oil in Indian mustard. Kortesniemi and coworkers [99] investigated seed metabolomics using NMR in B. napus and B. rapa and found that unsaturated fatty acids, sucrose and sinapine were most discriminating metabolites.
6. Achievements
In India, 189 rapeseed-mustard varieties (118 Indian mustard; 7 karan rai; 14 gobhi sarson; 24 toria; 15 yellow sarson; 3 brown sarson; 1 black mustard; 7 taramira) were developed and released and some of them are enlisted in Table 4. Several CMS based hybrids were developed by government and non-government institutes. A total of 7029 accessions comprising toria (508), Indian mustard (4,600), yellow sarson (548), gobhi sarson (146), brown sarson (108), karan rai (232), taramira (67), B. caudatus (04), R. caudates (01), B. rugose (30), B. nigra (22), S. alba (01), Crambe spp. (02), and Lapidium spp. (02) were maintained through appropriate mating system at various coordinated centers in the country [100]. As seed oil quality is concerned, low glucosinolates content was transferred from agronomically poor exotic genetic stock of B. juncea, BJ-1058 to the genetic background of high yielding mustard varieties. Genetics of fatty acid profile and glucosinolates content has been worked out and gene pool for high oil content and disease resistance were developed.
Indian mustard: Basanti, JM 1, JM 2, Maya, Pusa Jagannath
Powdery mildew and Alternaria blight
Indian mustard: DRMR 150-35, NRCDR 2, NRCDR 601
Wider adaptability
Indian mustard: Pusa Bold
Table 4.
Improved varieties of Indian mustard for specific environmental conditions.
7. Future outlook and strategy
To fulfill the demand of edible oil for ever increasing population, constant efforts are needed for higher production and productivity by conventional, molecular or biotechnological approaches in the country. Genetic variability is the prerequisite for crop improvement program. Moreover, there is imperative need to diversify the genetic base of varieties by utilization of exotic germplasm as well as other wild and related species. In this context, combination of conventional plant breeding with biotechnological tools can be used for development of high yielding varieties with good oil quality and tolerance against biotic and abiotic stresses. Global warming and the climate change are very critical challenges in the near future. Efforts to develop climate resilient crop cultivars are the need of the hour. Marker assisted selection (MAS), functional genomics, phenomics, proteomics and metabolomics are the next step to develop varieties for drought and heat tolerance and breeding programs must be reoriented to meet the future challenges. Nowadays, omics breeding has emerged as a novel concept in crop improvement and upcoming era will be dominated by this approach as it is more robust and rapid as compared to conventional breeding.
Conflict of interest
“The authors declare no conflict of interest.”
\n',keywords:"rapeseed-mustard, hybrid breeding, oil quality, pre-breeding, biotic and abiotic stress",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/75542.pdf",chapterXML:"https://mts.intechopen.com/source/xml/75542.xml",downloadPdfUrl:"/chapter/pdf-download/75542",previewPdfUrl:"/chapter/pdf-preview/75542",totalDownloads:73,totalViews:0,totalCrossrefCites:0,dateSubmitted:"October 28th 2020",dateReviewed:"February 1st 2021",datePrePublished:"March 4th 2021",datePublished:null,dateFinished:"March 4th 2021",readingETA:"0",abstract:"Brassica spp., commonly known as rapeseed-mustard, plays a significant role in the Indian economy by providing edible oils, vegetables, condiments and animal feed. Globally, India holds second and third position in rapeseed-mustard area under cultivation and production, respectively. However, anthropogenically accelerated climate change thwarts yield potential of rapeseed-mustard by employing abiotic (drought, flood, temperature variation and salinity) and biotic (disease and insects) stresses. Various approaches such as molecular breeding, pre-breeding, −omics and biotechnological interventions have been used to develop varieties for improved yield and oil quality, climate resilient and resistance or tolerance to abiotic and biotic stresses. In this context, this chapter highlighted the different cytoplasmic male sterility (CMS) sources and their potential use for hybrid development. At the end, this chapter also enlisted salient achievement by the government and non-government institutes and briefly described the future perspective for improvement of rapeseed-mustard in India.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/75542",risUrl:"/chapter/ris/75542",signatures:"Subhash Chand, Om Prakash Patidar, Rajat Chaudhary, Ranjit Saroj, Kailash Chandra, Vijay Kamal Meena, Omkar M. Limbalkar, Manoj Kumar Patel, Priya P. Pardeshi and Prashant Vasisth",book:{id:"9686",title:"Brassica Breeding and Biotechnology",subtitle:null,fullTitle:"Brassica Breeding and Biotechnology",slug:null,publishedDate:null,bookSignature:"Prof. A. K. M. Aminul Islam, Prof. Mohammad Anwar Hossain and Prof. A. K. M. Mominul Islam",coverURL:"https://cdn.intechopen.com/books/images_new/9686.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83968-697-9",printIsbn:"978-1-83968-696-2",pdfIsbn:"978-1-83968-698-6",editors:[{id:"191072",title:"Prof.",name:"A. K. M. Aminul",middleName:null,surname:"Islam",slug:"a.-k.-m.-aminul-islam",fullName:"A. K. M. Aminul Islam"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Origin",level:"1"},{id:"sec_3",title:"3. Distribution",level:"1"},{id:"sec_4",title:"4. Breeding approaches in rapeseed-mustard",level:"1"},{id:"sec_4_2",title:"4.1 Abiotic stresses",level:"2"},{id:"sec_4_3",title:"Table 2.",level:"3"},{id:"sec_5_3",title:"4.1.2 Salt stress",level:"3"},{id:"sec_6_3",title:"4.1.3 Heat stress",level:"3"},{id:"sec_7_3",title:"4.1.4 Low temperature stress",level:"3"},{id:"sec_9_2",title:"4.2 Biotic stresses",level:"2"},{id:"sec_9_3",title:"4.2.1 Alternaria blight",level:"3"},{id:"sec_10_3",title:"4.2.2 White rust",level:"3"},{id:"sec_11_3",title:"4.2.3 Sclerotinia rot",level:"3"},{id:"sec_12_3",title:"4.2.4 Insect (Aphid)",level:"3"},{id:"sec_14_2",title:"4.3 Oil quality improvement",level:"2"},{id:"sec_15_2",title:"4.4 Hybrid breeding",level:"2"},{id:"sec_15_3",title:"Table 3.",level:"3"},{id:"sec_17_2",title:"4.5 Pre-breeding",level:"2"},{id:"sec_19",title:"5. Biotechnological approaches",level:"1"},{id:"sec_19_2",title:"5.1 Anther culture",level:"2"},{id:"sec_20_2",title:"5.2 Somaclonal variation",level:"2"},{id:"sec_21_2",title:"5.3 Protoplast culture",level:"2"},{id:"sec_22_2",title:"5.4 Transgenic plants",level:"2"},{id:"sec_23_2",title:"5.5 -Omics approaches",level:"2"},{id:"sec_24_2",title:"5.6 Genomics",level:"2"},{id:"sec_25_2",title:"5.7 Transcriptomics",level:"2"},{id:"sec_26_2",title:"5.8 Proteomics",level:"2"},{id:"sec_27_2",title:"5.9 Metabolomics",level:"2"},{id:"sec_29",title:"6. Achievements",level:"1"},{id:"sec_30",title:"7. Future outlook and strategy",level:"1"},{id:"sec_34",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Jat RS, Singh VV, Sharma P, Rai PK. Oilseed Brassica in India: Demand, supply, policy perspective and future potential. OCL. 2019; 26: 8'},{id:"B2",body:'MAFW. 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International Journal of Farm Sciences. 2018; 8 (2): 109-113'},{id:"B71",body:'Singh D, Mehta R. Studies on breeding brown sarson. I. Comparison of F1’s and their parents. Indian J. Genet. Pl. Breed. 1954; 14: 74-77'},{id:"B72",body:'Chand S, Chandra K, Khatik CL. Varietal Release, Notification and Denotification System in India. In Plant Breeding-Current and Future Views. 2020. IntechOpen'},{id:"B73",body:'Prakash S, Chopra VL. Synthesis of alloplasmic Brassica campestris as a new source of cytoplasmic male sterility. Plant breeding. 1988; 101 (3): 253'},{id:"B74",body:'Kirti PB, Narasimhulu SB, Mohapatra T, Prakash S, Chopra VL. Correction of chlorophyll deficiency in alloplasmic male sterile Brassica juncea through recombination between chloroplast genomes. Genetics Research. 1993; 62 (1): 11-14'},{id:"B75",body:'Kumawat G, Kumawat CK, Chandra K, Pandey S, Chand S, Mishra UN, Lenka D, Sharma R. Insights into Marker Assisted Selection and Its Applications in Plant Breeding. In Plant Breeding-Current and Future Views. 2020; IntechOpen'},{id:"B76",body:'Sharma SK and Singh R. Genetic resources of oilseed crops in India. In: Hegde DM (Ed).Changing Global Vegetable Oils Scenario: Issues and Challenges before India. Indian Society of Oilseed Research, DOR, Hyderabad. 2007; 1-16'},{id:"B77",body:'Watts A, Sankaranarayanan S, Raipuria RK, Watts A. Production and Application of Doubled Haploid in Brassica Improvement. In Brassica Improvement Springer, Cham. 2020; 67-84'},{id:"B78",body:'Jain RK, Sharma DR, Chowdhury JB.High frequency regeneration and heritable somaclonal variation in Brassica juncea. Euphytica. 1989; 40 (1-2): 75-81'},{id:"B79",body:'Anuradha G, Narasimhulu SB, Arunachalam V, Chopra VL. A comparative evaluation of somaclonal, gamma ray and EMS induced variation in Brassica juncea. Journal of Plant Biochemistry and Biotechnology. 1992; 1 (2): 105-108'},{id:"B80",body:'Walden R, Koncz C, Schell J. The use of gene vectors in plant molecular biology. Methods Mol. Cell. Biol. 1990; 1: 175-194'},{id:"B81",body:'Mukherjee AK, Mohapatra T, Varshney A, Sharma R, Sharma RP.Molecular mapping of a locus controlling resistance to Albugo candida in Indian mustard. Plant Breeding. 2001; 120 (6): 483-497'},{id:"B82",body:'Padmaja KL, Arumugam N, Gupta V, Mukhopadhyay A, Sodhi YS, Pental D, Pradhan AK. Mapping and tagging of seed coat colour and the identification of microsatellite markers for marker-assisted manipulation of the trait in Brassica juncea. Theoretical and Applied Genetics. 2005; 111 (1): 8-14'},{id:"B83",body:'Liu S, Huang H, Yi X, Zhang Y, Yang Q, Zhang C, Fan C, Zhou Y. Dissection of genetic architecture for glucosinolate accumulations in leaves and seeds of Brassica napus by genome-wide association study. Plant Biotechnology Journal. 2020; 18 (6): 1472-1484'},{id:"B84",body:'Kaur J, Akhatar J, Goyal A, Kaur N, Kaur S, Mittal M, Kumar N, Sharma H, Banga S, Banga SS. Genome wide association mapping and candidate gene analysis for pod shatter resistance in Brassica juncea and its progenitor species. Molecular Biology Reports. 2020; 1-2'},{id:"B85",body:'Cai G, Yang Q, Yang Q, Zhao Z, Chen H, Wu J, Fan C, Zhou Y. Identification of candidate genes of QTLs for seed weight in Brassica napus through comparative mapping among Arabidopsis and Brassica species. BMC genetics. 2012; 13 (1): 105'},{id:"B86",body:'Bisht NC, Gupta V, Ramchiary N, Sodhi YS, Mukhopadhyay A, Arumugam N, Pental D, Pradhan AK. Fine mapping of loci involved with glucosinolate biosynthesis in oilseed mustard (Brassica juncea) using genomic information from allied species. Theoretical and Applied Genetics. 2009; 118 (3): 413-421'},{id:"B87",body:'Couvreur TL, Franzke A, Al-Shehbaz IA, Bakker FT, Koch MA, Mummenhoff K. Molecular phylogenetics, temporal diversification, and principles of evolution in the mustard family (Brassicaceae). MolBiolEvol. 2010; 27 (1): 55-71'},{id:"B88",body:'Augustine R, Majee M, Pradhan AK, Bisht NC. Genomic origin, expression differentiation and regulation of multiple genes encoding CYP83A1, a key enzyme for core glucosinolate biosynthesis, from the allotetraploid Brassica juncea. Planta. 2015; 241 (3): 651-665'},{id:"B89",body:'Heng S, Gao J, Wei C, Chen F, Li X, Wen J, Yi B, Ma C, Tu J, Fu T, Shen J. Transcript levels of orf288 are associated with the hau cytoplasmic male sterility system and altered nuclear gene expression in Brassica juncea. Journal of experimental botany. 2018; 69 (3): 455-466'},{id:"B90",body:'Bhattacharya S, Das N, Maiti MK. Cumulative effect of heterologous AtWRI1 gene expression and endogenous BjAGPase gene silencing increases seed lipid content in Indian mustard Brassica juncea. Plant Physiology and Biochemistry. 2016; 107: 204-213'},{id:"B91",body:'Savadi S, Naresh V, Kumar V, Bhat SR. Seed-specific overexpression of Arabidopsi s DGAT1 in Indian mustard (Brassica juncea) increases seed oil content and seed weight. Botany. 2016; 94 (3): 177-184'},{id:"B92",body:'Zhao N, Xu X, Wamboldt Y, Mackenzie SA, Yang X, Hu Z, Yang J, Zhang M. MutS HOMOLOG1 silencing mediates ORF220 substoichiometric shifting and causes male sterility in Brassica juncea. Journal of experimental botany. 2016; 67 (1): 435-444'},{id:"B93",body:'Zheng M, Zhang L, Tang M, Liu J, Liu H, Yang H, Fan S, Terzaghi W, Wang H, Hua W. Knockout of two Bna MAX 1 homologs by CRISPR/Cas9-targeted mutagenesis improves plant architecture and increases yield in rapeseed (Brassica napus L.). Plant biotechnology journal. 2020; 18 (3): 644-654'},{id:"B94",body:'Mihr C, Baumgärtner M, Dieterich JH, Schmitz UK, Braun HP. Proteomic approach for investigation of cytoplasmic male sterility (CMS) in Brassica.Journal of plant physiology. 2001; 158 (6): 787-794'},{id:"B95",body:'Mohammadi PP, Moieni A, Komatsu S. Comparative proteome analysis of drought-sensitive and drought-tolerant rapeseed roots and their hybrid F1 line under drought stress.Amino Acids. 2012; 43 (5): 2137-2152'},{id:"B96",body:'Yousuf PY, Ahmad A, Ganie AH, Iqbal M. Salt stress-induced modulations in the shoot proteome of Brassica juncea genotypes. Environmental Science and Pollution Research. 2016a; 23 (3): 2391-2401'},{id:"B97",body:'Yousuf PY, Ganie AH, Khan I, Qureshi MI, Ibrahim MM, Sarwat M, Iqbal M, Ahmad A. Nitrogen-efficient and nitrogen-inefficient Indian mustard showed differential expression pattern of proteins in response to elevated CO2 and low nitrogen. Frontiers in plant science. 2016b; 7: 1074'},{id:"B98",body:'Sinha S, Jha JK, Maiti MK, Basu A, Mukhopadhyay UK, Sen SK. Metabolic engineering of fatty acid biosynthesis in Indian mustard (Brassica juncea) improves nutritional quality of seed oil. Plant Biotechnology Reports. 2007; 1 (4): 185-197'},{id:"B99",body:'Kortesniemi M, Vuorinen AL, Sinkkonen J, Yang B, Rajala A, Kallio H. NMR metabolomics of ripened and developing oilseed rape (Brassica napus) and turnip rape (Brassica rapa). Food chemistry. 2015; 172: 63-70'},{id:"B100",body:'ICAR-Directorate of Rapeseed-Mustard Research, https://www.drmr.res.in accessed on 07.01.2021'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Subhash Chand",address:"subhashchand5415@gmail.com",affiliation:'
Division of Genetics, ICAR- Indian Agricultural Research Institute, India
Division of Genetics, ICAR- Indian Agricultural Research Institute, India
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