Five different positions and joint variables.
Inverse kinematics of serial or parallel manipulators can be computed from given Cartesian position and orientation of end effector and reverse of this would yield forward kinematics. Which is nothing but finding out end effector coordinates and angles from given joint angles. Forward kinematics of serial manipulators gives exact solution while inverse kinematics yields number of solutions. The complexity of inverse kinematic solution arises with the increment of degrees of freedom. Therefore it would be desired to adopt optimization techniques. Although the optimization techniques gives number of solution for inverse kinematics problem but it converses the best solution for the minimum function value. The selection of suitable optimization method will provides the global optimization solution, therefore, in this paper proposes quaternion derivation for 5R manipulator inverse kinematic solution which is later compared with teachers learner based optimization (TLBO) and genetic algorithm (GA) for the optimum convergence rate of inverse kinematic solution. An investigation has been made on the accuracies of adopted techniques and total computational time for inverse kinematic evaluations. It is found that TLBO is performing better as compared GA on the basis of fitness function and quaternion algebra gives better computational cost.
Kinematic chain may consist of rigid/flexible links which are connected with joints or kinematics pair permitting relative motion of the connected bodies. In case of manipulator kinematics it can be categorized into forward and inverse kinematics. Forward kinematics for any serial manipulator is easy and mathematically simple to resolve but in case of inverse kinematics there is no unique solution, generally inverse kinematics gives multiple solutions. Hence, inverse kinematics solution is very much problematic and computationally expensive. For real time control of any configuration manipulator will be expensive and generally it takes long time. Forward kinematics of any manipulator can be understand with translation of position and orientation of end effector from joint space to Cartesian space and opposite of this is known as inverse kinematics. It is essential to calculate preferred joint angles so that the end effector can reach to the desired position and also for designing of the manipulator. Various industrial applications are based on inverse kinematics solutions. In real time environment it is obvious to have joint variables for fast transformation of end effector. For any configuration of industrial robot manipulator for n number of joints the forward kinematics will be given by,
where θi = θ(t), i = 1, 2, 3, …, n and position variables by yj = y(t), j = 1, 2, 3, …, m.
Inverse kinematics for n number of joints can be computed as,
Inverse kinematics solution of robot manipulators has been considered and developed different solution scheme in last recent year because of their multiple, nonlinear and uncertain solutions. There are different methodologies for solving inverse kinematics for example iterative, algebraic and geometric etc.  proposed inverse kinematic solution on the basis of quaternion transformation. [20–36] have proposed application of quaternion algebra for the solution of inverse kinematics problem of different configurations of robot manipulator.  presented a quaternion method for the demonstrating kinematics and dynamics of rigid multi-body systems.  presented analytical solution of 5-dof manipulator considering singularity analysis.  presented quaternion based kinematics and dynamics solution of flexible manipulator.  proposed detailed derivation of inverse kinematics using exponential rotational matrices. On the other hand, after numerous surveys on conventional analytical and other Jacobian based inverse kinematics are quite complex as well as computationally exhaustive those are not exactly well suitable for the real time applications. Because of the above-mentioned reasons, various authors adopted optimization based inverse kinematic solution.
Optimization techniques are fruitful for solve inverse kinematics problem for different configurations of manipulator as well as spatial mechanisms. Conventional approaches such as Newton-Raphson can be used for nonlinear kinematic problems and predictor corrector type methods can compute differential problem of manipulator. But major drawback of these methods are Singularity or ill condition which converse to local solutions. Moreover, when initial guessing is not accurate then the method becomes unstable and does not converse to optimum solution. Therefore, recently developed metaheuristic techniques can be used to overcome the conventional optimization drawbacks. Literature survey shows the efficiency of these metaheuristic algorithms or bi-inspired optimization techniques are more convenient to achieve global optimum solutions. The major issue with these nature inspired algorithms is framing of objective function. Even these algorithms are direct search algorithms which do not require any gradient or differentiation of objective function. The comparison of the metaheuristic algorithm with heuristic algorithms is based on the convergence rate as it has been proved that the convergence of heuristic-based techniques is slower. Therefore, to adopt metaheuristic techniques such as GA, BBO, teachers learner based optimization (TLBO), ABC, ACO etc. will be suitable for enhancing the convergence rate and yielding global solution. From literature survey the teaching learning based optimization (TLBO) is similar to swarm based optimization in which the impact of learning methods from teacher to student and student to student has been highlighted. Wherein, the population or swarm is represented by group of students and these students gain knowledge from either teacher or students. If these student gain knowledge from teacher then it is called as teachers phase similarly when students learns form student then it is student phase. The output is considered as result or grades of students. Therefore, number for number of subjects resembles the variables of the function and grades or results gives fitness value, [5, 6]. There are numerous other population centered methods which have been effectively applied and shown efficiency . However, all algorithms are not suitable for complex problem as proved by Wolpert and Macready. On the other hand, evolutionary strategy (ES) based methods such as GA, BBO etc. gives better results for various problems and these methods are also population based metaheuristic [16, 28]. Moreover  proposed inverse kinematic solution of redundant manipulator using modified genetic algorithm considering joint displacement (
On the other hand, the use of optimization algorithms is not new in the field of multi-objective and NP-hard problem to arrive at a very reasonable optimized solution, the TLBO algorithm have not been tried to solve an inverse kinematics problems and trajectory of joint variables for robot manipulator. Moreover, computational cost for yielding the inverse kinematics solution with adopted algorithms has been compared without any specialized tuning of concern parameters. Therefore, the key purpose of this work is focused on minimizing the Euclidian distance of end effector position based resolution of inverse kinematics problem with comparison of GA and TLBO obtained solution for 5R robot manipulator. The results of all algorithm are computed from inverse kinematics equations and obtained resultant error for data statistics. In other words, end-effector coordinates utilized as an input for joint angle calculations. At the end 4th order spline formula is considered for generation of end effector trajectory and analogous joint angles of robotic arm using TLBO, GA and quaternion. The sectional organization of the paper henceforth is as follows: Section 2 pertains to the mathematical modeling of the 5R robot manipulator and detail derivation of forward and inverse kinematics of 5R manipulator using quaternion algebra. In Section 3 discuss about the inverse kinematic objective function formulation for 5R manipulator. The experimental results as obtained from simulations are discussed elaborately in Section 5.
2. Quaternion vector approach for mathematical modeling
This section deals with mathematical modeling of quaternion vector algebra and application for the derivation of inverse kinematics equations. Quaternion vector methods are fruitful for both rotation and translation of a point, line, etc. with references to origin coordinate system irrespective of homogeneous transformation matrix. The Interpolation of series of rotations and translations are quite complex using Euler’s angle method. In other words, the variables lies in isotropic space which is nothing but sphere surface topology and complex in nature. A brief formulation of quaternion mathematics is given in this section for assessment of references and to create background for mathematical derivation of inverse kinematic.
2.1. Rotation and translation from quaternion
The above discussions gives the importance of quaternions and the necessity of it. The quaternion rotation and translation are lies in four dimensional space therefore it is quite difficult to represent here or to imagine. Figure 1 represents the rotation through quaternion and Eq. (3) describes rotation of a point in a space mathematically.
The 4-dimensional space, imagination of fourth axis is quite complex. Therefore, in Figure 1 a unit distance point around axis (X, Y, and Z) is given and which traces a circle. When this rotation circle is projected on a plane then the point P1 can be seen rotated through angle
Now two quaternions can be represented on the basis of above discussed concept. If there is subsequent rotation of two quaternion
Now pure translations
Quaternion transform can be given by,
Therefore, an expression for the inverse of a quaternion can be given as,
Similarly vector transformation multiplication can be given as
2.2. Quaternion derivation for 5R manipulator kinematics
The configuration and base coordinate frame attachment of 5R manipulator is given in Figure 2(a) and MATLAB plot of 5R manipulator is presented in (b). Where
Now quaternion for successive transformation of each joint can be calculated from the Eq. (3) as follows,
Inverse of a dual quaternion can be calculated by Eq. (8),
Where in case of 5R manipulator arm n = 5. Now calculating quaternion vector products using Eq. (20)
Now calculating vector pair of quaternion using Eq. (29), to solve the inverse kinematics problem, the transformation quaternion of end effector of robot manipulator can be defined as
Now using Eq. (29),
Therefore, all the joint variables can be calculated by equating quaternion vector products and quaternion vector pairs i.e.
3. Inverse kinematic solution scheme
In this section optimization algorithms are selected for computation of inverse kinematics solution of 5R manipulator. However, there are various types of optimization algorithms existed and can produce the desired IK solution, the major necessity is to achieve global optimum solution with fast convergence rate. Therefore, selection of appropriate optimization algorithm is important for fitness evaluations and GA is so far best known tool, but on the other hand TLBO has also proven its efficiency and performance. Finally selection of optimization algorithms has been made on global searching point, computational cost and quality of the result.
3.1. Optimization approach to solve inverse kinematics
Any Optimization algorithms which are capable of solving various multimodal functions can be implemented to find out the inverse kinematic solutions. The fitness function is given by the Eq. (46) fitness function F(x). Each individual represents a joint variable solution of the inverse kinematic problem for adopted population based metaheuristic algorithm. All individuals moving in D-dimensional search space and sharing the information to find out best fitness value of the function. Each individual contains set of joint angles (
For the optimization of joint angle rotation of robot manipulator, one can define objective function or fitness function from joint angle rotation difference and other can be defined from end effector position displacement. These are known as joint angle error and positional function method [3, 7, 10, 29].
3.1.1. Position based function
The current position of the manipulator is described by (39):
Desired position of end effector can be denoted by (40):
Current position of end effector will be compared with the desired position
3.1.2. Joint angle error
Corresponding joint error can be given by the difference between current set of joint variables to the final required angles.
Therefore using square norm the objective function can be given as
Subjected to joint limits
Now overall error minimization can be given by using Eqs. (42) and (45),
4. Results and discussions
TLBO and GA has been used to compute the inverse kinematics of 5-R manipulator and comparison of obtained results has been made on the basis of quality and performance. Table 1 gives the five random position of end effector and respective inverse kinematics solutions. Current work is performed in MATLAB R2013a. The data sets are obtained from Eq. (34) through (38). The data sets are generated using quaternion vector based inverse kinematics equations as given in Table 2. These generated data sets are used to compare the IK solution through adopted GA and TLBO. In Table 3, comparative evaluations of fitness function and obtained joint variables through TLBO and GA is presented.
|P1(−76.09, 54.36, −61.94)||84.559||77.518||101.74||30.616||38.697|
|P2(89.69, 192.55, 90.87)||84.791||97.25||130.44||50.771||36.428|
|P3(−4.24, 94.08, 97.55)||18.384||78.688||35.234||77.708||34.889|
|P5(−184.33, −43.21, 8.27)||39.177||107.13||97.052||65.672||15.374|
|SN||Position of joints determined through quaternion algebra|
|Positions||TLBO joint angles||Function value|
|P1(−76.09, 54.36, −61.94)||86.598||72.165||72.165||40.894||30.459||0|
|P2(89.69, 192.55, 90.87)||83.874||69.895||69.895||39.607||30.76||0|
|P3(−4.24, 94.08, 97.55)||84.512||70.427||70.427||39.909||30.686||0|
|P5(−184.33, −43.21, 8.27)||87.818||73.181||73.181||41.469||30.364||0|
|P1(−76.09, 54.36, −61.94)||60.619||49.504||58.384||62.281||27.903||0|
|P2(89.69, 192.55, 90.87)||88.293||34.091||14.439||15.241||51.738||0|
|P3(−4.24, 94.08, 97.55)||55.004||49.274||63.942||47.842||33.633||0|
|P5(−184.33, −43.21, 8.27)||25.669||70.588||31.341||66.807||52.884||0|
This work does not use special tuning of various parameters of GA and TLBO algorithm. In future research the sensitivity analysis can be performed to achieve better results. From Table 3, TLBO generated solutions for the position 4 is better as compared to GA in account of fitness function evaluation. There are different distance based norms, one of them is Euclidean distance norm and which is used here for minimum distance between the end effector positions. If the distance between two points reached to 0 or less than 0.001 than the evolutions of fitness function can be reached best or global minimum value. It is clear that the obtained fitness value is less than the defined distance norm so adopting these algorithms are fruitful and qualitative.
Figures 3–7 signify the best fitness function value and analogous joint variables for position 1. These figures show efficiency of adopted algorithms for IK solution of 5-R manipulator. The convergence of objective function evaluation lies to zero error for GA and TLBO algorithms while for position 4, GA yields 0.013 error. It means that GA is less performing as compared to TLBO. From Figures 8–12, the results obtained through GA shows in terms of convergence and histogram graph and the obtained joint angles are in radian which is later converted into degree and given in Table 3. The GA results are obtained through MATLAB toolbox and that shows the zero convergence in single run. Figures 8–12, it can be seen that the generated solutions for joint angles are multiple for single position and similarly there are multiple fitness function evaluations. The best fitness function achieved here using the termination criteria and the corresponding joint variables has taken for comparison.
The proposed work is performed in dual core system with 4 GB RAM computer. It has been observe that the convergence of the solution for GA is taking less computation time as compared to TLBO and quaternion algebra. Corresponding joint angles trajectory using 4th order cubic spline is presented in Figure 13. Using inverse kinematic solution joint variables are used to calculate the joint space trajectory for TLBO and GA as presented in Figures 14 and 15. Final time has been taken
In this paper, the work discourses the problem associated to the optimization of positional and angular error of end effector using TLBO and GA for 5R robot manipulator. Metaheuristic algorithms like PSO, GA, ABC, etc. have been used in various field of industrial robotics but the most critical issue is to solve inverse kinematic problem for any configuration of robot manipulators. Most of the optimization approach are being used for numerical solution but it has been observed that the numerical solutions does not yield solution when the manipulator is in ill-conditioned besides this it has also been observed that classical optimization methods converge in local minima. Therefore in this work global optimization method like TLBO and GA is adopted and after analyzing the results it can be concluded that adopted optimization algorithms convergence rate is higher and complexity does not increase with the manipulator configuration. Although many researchers are tried to obtain global solution but the computations cost are more in the problem henceforth overcoming the problem of computational cost with quaternion objective function.
The adopted algorithms are very much appropriate for constrained and unconstrained problems. To estimate the effectiveness of considered algorithms, comparison has been made with quaternion algebra. Table 3 gives the comparative results of adopted algorithm and proposed quaternion solutions of 5-R manipulator. This work considered forward and inverse kinematic equations for preparing the objective function for TLBO and GA. These adopted algorithms has shown the potential of getting faster convergence and yielding global optimum solution for the stated problem. In future the tuning of various parameters of GA and TLBO can be considered so as to avoid trapping in local minimum point. Even the hybridization of these algorithms may be proposed and adopt for the IK problems.
Ahuactzin J, Gupta K. Completeness Results for a Point-to-Point Inverse Kinematics Algorithm. Detroit, MI: IEEE. Vol. 2. p. 1526-1531
Albert F, Koh S, Tiong S, Chen C. Inverse Kinematic Solution in Handling 3R Manipulator via Real-Time Genetic Algorithm. IEEE. p. 1-6
Ayyıldız M, Çetinkaya K. Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator. Neural Computing and Applications. 2015. DOI: 10.1007/s00521-015-1898-8
Chapelle F, Bidaud P. A Closed Form for Inverse Kinematics Approximation of General 6R Manipulators Using Genetic Programming. 2001; 4:3364-3369
Chen C, Her M, Hung Y, Karkoub M. Approximating a robot inverse kinematics solution using fuzzy logic tuned by genetic algorithms. The International Journal of Advanced Manufacturing Technology. 2002; 20:375-380. DOI: 10.1007/s001700200166
Dulęba I, Opałka M. A comparison of Jacobian-based methods of inverse kinematics for serial robot manipulators. International Journal of Applied Mathematics and Computer Science. 2013. DOI: 10.2478/amcs-2013-0028
Eiben A, Smith J. Introduction to Evolutionary Computing. New York: Springer; 2003
Funda J, Taylor R, Paul R. On homogeneous transforms, quaternions, and computational efficiency. IEEE Transactions on Robotics and Automation. 1990; 6:382-388. DOI: 10.1109/70.56658
Galicki M. Control-based solution to inverse kinematics for mobile manipulators using penalty functions. Journal of Intelligent and Robotic Systems. 2005; 42:213-238. DOI: 10.1007/s10846-004-7196-9
Gan D, Liao Q, Wei S. Dual quaternion-based inverse kinematics of the general spatial 7R mechanism. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2008; 222:1593-1598. DOI: 10.1243/09544062jmes1082
Geradin M, Cardona A. Kinematics and dynamics of rigid and flexible mechanisms using finite elements and quaternion algebra. Computational Mechanics. 1988; 4:115-135. DOI: 10.1007/bf00282414
He G, Hongming G, Zhang G, Wu L. Using Adaptive Genetic Algorithm to the Placement of Serial Robot Manipulator. Islamabad: IEEE; 2006. p. 1-6
Huang H, Xu S, Hsu H. Hybrid Taguchi DNA swarm intelligence for optimal inverse kinematics redundancy resolution of six-DOF humanoid robot arms. Mathematical Problems in Engineering. 2014; 2014:1-9. DOI: 10.1155/2014/358269
Husty M, Pfurner M, Schröcker H. A new and efficient algorithm for the inverse kinematics of a general serial 6R manipulator. Mechanism and Machine Theory. 2007; 42:66-81. DOI: 10.1016/j.mechmachtheory.2006.02.001
Kalra P, Mahapatra P, Aggarwal D. On the Solution of Multimodal Robot Inverse Kinematic Functions using Real-coded Genetic Algorithms. IEEE. 2003; 2:1840-1845
Kennedy J, Eberhart R, Shi Y. Swarm Intelligence. San Francisco: Morgan Kaufmann Publishers; 2001
Khatami S, Sassani F. Isotropic Robotic Design Optimization of Manipulators Using a Genetic Algorithm Method. Vancouver: IEEE; 2002. p. 562-567
Kim S, Kim J. Optimal Trajectory Planning of a Redundant Manipulator Using Evolutionary Programming. Nagoya: IEEE; 1996. p. 738-743
Korein B. Techniques for generating the goal-directed motion of articulated structures. IEEE Computer Graphics and Applications. 1982; 2:71-81. DOI: 10.1109/mcg.1982.1674498
Kucuk S, Bingul Z. Inverse kinematics solutions for industrial robot manipulators with offset wrists. Applied Mathematical Modelling. 2014; 38:1983-1999. DOI: 10.1016/j.apm.2013.10.014
Liu S, Zhu S. An optimized real time algorithm for the inverse kinematics of general 6R robots. In: Control and Automation, 2007. ICCA 2007. IEEE International Conference on. IEEE, Guangzhou. 2007. pp. 2080–2084
Nearchou A. Solving the inverse kinematics problem of redundant robots operating in complex environments via a modified genetic algorithm. Mechanism and Machine Theory. 1998; 33:273-292. DOI: 10.1016/s0094-114x(97)00034-7
Parker J, Khoogar A, Goldberg D 1989, Inverse kinematics of redundant robots using genetic algorithms. Proceedings of IEEE International Conference on Robotics and Automation
Pham D, Castellani M, Fahmy A. Learning the Inverse Kinematics of a Robot Manipulator using the Bees Algorithm. Daejeon: IEEE; 2008. p. 493-498
Piazzi A, Visioli A. A Global Optimization Approach to Trajectory Planning for Industrial Robots. Grenoble: IEEE. 1997; 3:1553-1559
Rajpar A, Zhang W, Jia D. Object Manipulation of Humanoid Robot Based on Combined Optimization Approach. Harbin: IEEE; 2007. p. 1148-1153
Rao R, Savsani V, Vakharia D. Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design. 2011; 43:303-315. DOI: 10.1016/j.cad.2010.12.015
Rocke D, Michalewicz Z. Genetic algorithms + data structures = evolution programs. Journal of the American Statistical Association. 2000; 95:347. DOI: 10.2307/2669583
Sun L, Lee R, Lu W, Luk L. Modelling and simulation of the intervertebral movements of the lumbar spine using an inverse kinematic algorithm. Medical & Biological Engineering & Computing. 2004; 42:740-746. DOI: 10.1007/bf02345206
Tabandeh S, Clark C, Melek W 2006, A Genetic Algorithm Approach to solve for Multiple Solutions of Inverse Kinematics using Adaptive Niching and Clustering. In: IEEE, pp. 1815–1822
Vrongistinos K, Wang Y, Hwang Y. Quaternion smoothing on three-dimensional kinematics data. Medicine & Science in Sports & Exercise. 2001; 33:S84. DOI: 10.1097/00005768-200105001-00480
Wang L, Chen C. A combined optimization method for solving the inverse kinematics problems of mechanical manipulators. IEEE Transactions on Robotics and Automation. 1991; 7:489-499. DOI: 10.1109/70.86079
Wolpert D, Macready W. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation. 1997; 1:67-82. DOI: 10.1109/4235.585893
Xu D, Acosta Calderon C, Gan J. An analysis of the inverse kinematics for a 5-DOF manipulator. International Journal of Automation and Computing. 2005; 2:114-124. DOI: 10.1007/s11633-005-0114-1
Zoric N, Lazarevic M, Simonovic A. Multi-body kinematics and dynamics in terms of quaternions: Langrange formulation in covariant form – Rodriguez approach. FME Transactions. 2010; 38:19-28
Zu D. Efficient inverse kinematic solution for redundant manipulators. JME. 2005; 41:71. DOI: 10.3901/jme.2005.06.071