UGVs based on commercial vehicles.
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\r\n\tEditors hope to build a line of transformative research based on the adaptation of energy and design solutions from natural models to technical models, using methodologies and solutions which will open new areas of work about how to solve efficiency requirements based on natural solutions honed by evolution. This process is based on creativity and forces researchers to think out of their boxes and open new scientific challenges. What drives the research is the urge to find alternative, more efficient solutions to tackle problems. Due to the wide range of possibilities offered by biodiversity’s tested solutions, the methodology prioritize (although not exclude) solution-based approaches.
Cantilevered signal, sign, and light support structures are used nationwide on major interstates, national highways, local highways, and at local intersections for traffic control purposes. Recently, there have been a number of failures of these structures that can likely be attributed to fatigue. In Iowa, USA (Dexter 2004), a high-mast light pole (HMLP), which is typically used at major interstate junctions, erected for service in 2001 along I-29 near Sioux City collapsed in November 2003 (see Figure 1 (a)). Fortunately, the light pole fell onto an open area parallel to the interstate and injured no one. Figure 1 (b) shows another high-mast lighting tower failure in Colorado, USA (Rios 2007) that occurred in February of 2007. Similar to the failure in South Dakota, fracture initiated at the weld toe in the base plate to pole wall connection, and then propagated around the pole wall until the structure collapsed. It appears that these structures may have been designed based on incomplete and/or insufficient code provisions which bring reason to reevaluate the current codes that are in place.
A luminary support structure or HMLP is generally susceptible to two primary types of wind loading induced by natural wind gusts, or buffeting and vortex shedding, both of which excite the structure dynamically and can cause fatigue damage (AASHTO 2009). Vortex shedding is a unique type of wind load that alternatively creates areas of negative pressures on either side of a structure normal to the wind direction. This causes the structure to oscillate transverse to the wind direction. When the vortex shedding frequency (i.e., the frequency of the negative pressure on one side of the structure) approaches the natural frequency of the structure, there is a tendency for the vortex shedding frequency to couple with the frequency of the structure (also referred to as “lock-in” phenomenon) causing greatly amplified displacements and stresses.
While vortex shedding occurs at specific frequencies and causes amplified vibration near the natural frequencies of the structure, buffeting is a relatively “broad-band” excitation and includes frequencies of eddies that are present in the natural wind (usually up to 2 Hz) as well as those caused by wind-structure interactions. The dynamic excitation from buffeting can be significant if the mean wind speed is high, the natural frequencies of the structure are below 1 Hz, the wind turbulence intensity is high with a wind turbulence that is highly
A collapsed high-mast light pole; (a) Iowa (
correlated in space, the structural shape is aerodynamically odd with a relatively rough surface, and the mechanical damping is low. In practice, a structure is always subject to both vortex shedding and buffeting excitations. But unlike vortex shedding, where amplified dynamic excitation occurs within a short range of wind speeds, buffeting loads keep increasing with higher wind speeds.
For multisided slender support structures, the current American Association of State Highway and Transportation Officials (AASHTO) Specification does not provide all the aerodynamic parameters such as the static force coefficients, their slopes with angle of attack, Strouhal number, the lock-in range of wind velocities and amplitude of vortex-induced vibration as a function of Scruton number, etc, that are needed for proper evaluation of aerodynamic behavior. Thus, wind tunnel testing was required to obtain these parameters. Buffeting, self-excited and vortex shedding responses are those significant parameters in the design of a slender support structure.
A number of experimental and theoretical investigations have been made by Peil and Behrens (2002) to obtain a realistic basis for a reliable and economic design for lighting and traffic signal columns. The investigations were based on a nonlinear spectral approach which is confined to the correlated parts of the wind turbulence and the associated wind forces. Gupta and Sarkar (1996) conducted wind tunnel tests on a circular cylinder to identify vortex-induced response parameters in the time domain. Chen and Kareem (2000, 2002) worked on modeling aerodynamic phenomena, buffeting and flutter, in both time and frequency domains, and Scanlan (1984, 1993), Caracoglia and Jones (2003), Zhang and Brownjohn (2003), and Costa (2007) and Costa and Borri (2006) studied the aerodynamic indicial function for lift and admittance functions for structures. Together this collection of work provides the motivation for the model discussed herein. The effects of aerodynamic coupling between the buffeting and flutter responses have been addressed by past studies based on the theoretical expression. The aerodynamic admittance function for lift of a thin symmetrical airfoil, known as Sears function, was theoretically derived by Sears (1941), and a somewhat simpler form of the Sears function was suggested by Liepmann (1952). Jancauskas (1983) and Jancauskas and Melbourne (1986) verified the Sears’ theoretical plot experimentally for an airfoil and suggested a simplified but approximate expression. An empirical function for aerodynamic admittance for drag on a square plate was developed by Vickery (1965) based on limited experimental data. In previous research, Skop and Griffin (1975) derived an empirical formula to predict the maximum displacement amplitude for a circular cylinder based on Scruton numbers. Repetto and Solari (2004) developed an analytical model based on frequency-domain methods and quasi-steady theory to determine the along-wind and across-wind fatigue estimation of urban light pole. This model considers all modes of vibration and thereby avoids overestimation of base stress and underestimation of top displacement of the slender support structure.
The primary objective of this study is to develop aerodynamic parameters for multisided shapes. To be able to calculate the needed data for the structure, many wind parameters, such as the static drag coefficient, the slope of aerodynamic lift coefficient, Strouhal number, the lock-in range of wind velocities producing vibrations, and variation of amplitude of vortex-induced vibration with Scruton number, are needed. From wind tunnel experiments, aerodynamic parameters were obtained for an octagonal shape structure. Even though aerodynamic coefficients are known from past test results, they need to be refined by conducting further wind tunnel tests.
The use of wind tunnels to aid in structural design and planning has been steadily increasing in recent years (Liu 1991). Kitagawa et al. (1997) conducted a wind tunnel experiment using a circular cylinder tower to study the characteristics of the across-wind response at a high wind speed. The authors found from the tests that both the vortex induced vibration at a high wind speed and the ordinary vortex induced vibration were observed under uniform flow.
Bosch and Guterres (2001) conducted wind tunnel experiments to establish the effects of wind on tapered cylinders using a total of 53 models representing a range of cross sections, taper ratios, and shapes (circular, octagonal, or hexagonal cross section), which were intended to be representative of those commonly found in highway structures. In a test of drag coefficient versus Reynolds number for the uniform circular cylinders, the results showed a consistent trend of convergence with a range of Reynolds numbers for which the drag coefficient flattens out to a constant value. It was also found that the introduction of a taper ratio significantly altered the aerodynamic behavior of the cylinder shapes. Wind tunnel experiments by James (1976) were performed to establish the effects of wind on uniform cylinders using several models representing a range of shapes (octagonal, dodecagonal and hexdecagonal cross section), model orientations, and corner radii based on Reynolds number (Re) between 2.0 × 105 and 2.0 × 106. Lift and drag coefficients were developed for an octagonal cylinder by Simui and Scanlan (1996). In the study, the slope of the mean drag coefficient (CD) was found to be near zero and the slopes of the mean lift coefficient (CL) were calculated to be approximately -1.7 for flat orientation and 0.45 for corner orientation.
Wind tunnel testing is routinely used to study various aerodynamic phenomena and determine aerodynamic parameters of civil engineering structures. Also, the general flow pattern around structures can be determined from wind tunnel testing, particularly in the case of unusual structural shapes. Wind tunnel testing aids in structural design and planning because required aerodynamic coefficients may not always be available in codes or standards (Liu, 1991).
The wind tunnel that was used for this study is the Bill James Open Circuit Wind Tunnel (see Figure 2), which is located in the Wind Simulation and Testing Laboratory (WiST Lab) at Iowa State University (ISU), Ames, USA. This is a suction orientation wind tunnel with a 22:1 contraction ratio. The wind tunnel test section is of the dimensions 3ft x 2.5ft and 8ft length following the contraction exit. The test section has an acrylic viewing window next to the wind tunnel control/data station with an access door opposite the side of the station. The fan, which is located downstream of the test section, is powered by a 100hp, 3-phase, 440 volt motor. The fan is controlled either by an analog remote control knob which is located at the wind tunnel control station and connected to the variable frequency fan, or directly by using the digital control screen mounted on the actual motor control power box. The fan speed can be changed in minimal steps of 0.1 Hz or approximately 0.51 ft/s (0.16 m/s) using these controls.
For all of the tests, a wooden cylindrical model with an octagonal (8-sided) and a dodecagonal (12-sided) cross section of diameter 4 in. (flat to flat distance) and length of 20 in. were used. These dimensions were selected based on the need to maintain a wind tunnel blockage criterion of 8% or less. The actual blockage was 7.4% and, therefore, blockage effects could be neglected. The length of the model, 20 inches, was chosen to maximize the area of the model that would be exposed to the air stream while at the same time leaving enough room on both sides of the model to attach any additional fixtures that are required in order to change certain parameters.
End plates, which are made out of clear plastic, were attached to the model to minimize the three-dimensional end effects on the model and to, in turn, maintain a two-dimensional flow on the model. To test multiple modifications of the model with a different mass, pairs of commercially available C clamps were clamped to the end plates at equal distances from the centerline of the model to avoid any torsion.
For the static tests, each model was fixed horizontally in the wind tunnel with zero yaw angle and the aerodynamic forces were measured at various wind speeds. The angle of attack was varied by rotating the model about its longitudinal axis. Wind speeds were carefully chosen to provide a large range of Reynolds numbers. The load cells for this system were fixed to the test frame as shown in the figure. Thin strings were attached to the aluminum block at each end of the model to avoid vertical deflection of the model.
The wind speeds in this test were varied from 0.6 to 30.5 m/s (2 to 100 ft/s) to yield a range of Reynolds Number (Re) from 2.5 × 104 to 2.3 × 105. The drag coefficients, CD were calculated from the mean drag force and variable mean wind speeds using the following equation.
Bill James Wind Tunnel at Iowa State University
where FD = mean drag force; ρ = air density; U = mean wind speed; and A = projected area of model (= D L).
To verify the force-balance system, drag coefficients for a circular cylinder was measured at several Re and compared with other references. The average difference of drag coefficient measured with respect to other reference values for Re varying between 4.0×104 and 1.0×105 was approximately 2.3 %.
Figure 3 presents CD versus Re for the uniform dodecagonal shape cylinder. In this plot, it can be observed that the CD for the cylinder with corner orientation increases until Re equals approximately 1.5×105, beyond which it tends to converge to 1.45. With flat orientation, the CD appears to stabilize at 1.56 at approximately the same Re. The static tests indicated that the angle of attack (α) of the wind on the cylinder influences the CD and also showed that the flat orientation results in a slightly higher CD than those for the corner orientation.
According to Scruton (1981), the drag coefficients for a dodecagonal shape with flat orientation are 1.3 in the subcritical region and 1.0 in the supercritical region. James (1976) also conducted several wind tunnel tests to measure drag and lift coefficients on various polygon shaped cylinders. For a dodecagonal shape with sharp corners, James found the drag coefficient as 1.3 and 1.2 for flat and corner orientation, respectively, in Re varying from 3.0×105 to 2.0×106. Based on their research, drag coefficients of 1.2 and 0.79 for subcritical and supercritical region, respectively, are prescribed in the current AASHTO Specification and used for design. It is noted that the drag coefficients of 1.45 and 1.56 for both the orientations of the dodecagonal shape, as measured in the ISU Bill James Wind Tunnel for the sub-critical region, are higher than the value of 1.2 used currently for design.”
Mean drag coefficients (Cd) for a dodecagonal cylinder.
A similar force-balance system was used to obtain lift force in the static tests. The model was fixed in the vertical direction perpendicular to the air flow in the wind tunnel. The mean lift coefficients (CL) were calculated from the mean lift force and mean wind speed using the following equation.
where FL = mean lift force.
The slopes of CL with respect to the angle of attack, dCL/dα, were calculated to be approximately -0.7 and 0.5 for flat- and corner-orientation, respectively. The Re varied from 9.3 × 104 to 1.6 × 105 in these tests (see Figure 4).
Many tests were conducted on the models to obtain all of the needed aerodynamic parameters. Results of most importance include Strouhal number (St), lock-in range of wind velocities for vortex shedding, and the amplitude of vortex-induced vibrations as a function of the Scruton number (Sc).
Lift coefficient (CL) and its slope for the dodecagonal cylinder.
For the dynamic test, the vertical motion dynamic setup was designed to allow only a single-degree-of-freedom, which means that the test model was designed to only allow motion along the vertical axis perpendicular to the wind direction. Each model was suspended by a set of eight linear coil springs and chains, with four of each on each side of the model. Two cantilever type force transducers were used with one placed at the top and one at the bottom, at diagonally opposite springs.
The spring suspension system was attached to a frame that was fixed to the test section floor and ceiling immediately adjacent to the side walls. A load cell frame was constructed with small structural channels and four 0.75 inch diameter threaded steel walls with two on each side of the test section which spanned vertically from the floor to the ceiling of the test section. Figure 5 shows a schematic diagram of the dynamic test suspension system.
The lock-in range and Strouhal number (fs D/U ≈ 0.17 and 0.2 for a 8-sided and 12-sided shape respectively) were determined based on the dynamic tests. Lock-in occurs when the vortex shedding frequency matches the natural frequency of the actual system which occurs at a critical wind speed causing the response at the lock-in region to be much larger than that of the normal region. The lock-in region stays consistent over a certain range of wind speeds. Figure 6 shows the frequency spectrum of the displacement response of the elastically supported cylinder for the three different instances of (a) before lock-in, (b) at lock-in, and (c) after lock-in, all for the flat orientation, where fs and fn are the vortex-shedding frequency and the natural frequency, respectively, of the test model. These figures show that the model produces much higher amplified displacements when the vortex shedding frequency and the natural frequency match one another.
Schematic diagrams of the dynamic suspension system
Frequency spectra of displacement response of the octagonal cylinder
Scruton number
The amplitude of the model is directly related to the Scruton number (Sc). In order to determine the amplitude versus the Sc, it was necessary to obtain several different parameters. These parameters include the inertial mass, stiffness, natural frequency, and the system damping ratio. The Sc is solved using the following:
where m = mass per unit length; ζ = critical damping ratio; ρ = flow density; and D = cross-wind dimension of the cross-section.
The inertial mass, stiffness, and natural frequency for each case were determined using the added mass method, by adding masses incrementally. This was done by testing multiple specimens of the model with different masses, added by clamping pairs of commercially available C-clamps with different weights to the previously described plastic end plates. A total of five pairs of clamps and one thin steel plate were used. To avoid the introduction of torsion on the testing model, the clamps and the steel plate were added to the plastic end plates on opposite sides of the cylinder. The system damping was determined for each case experimentally by using the logarithmic decrement method.
The Sc for each case of added mass was calculated using Eq. 3 and the reduced amplitude (yo/D, max amp./diameter of the model) was obtained from the measurement that was taken when the maximum displacement occurred. The best fit line was also plotted and is shown in Figure 7.
Scruton number vs. maximum amplitude
The relationship in the frequency domain between the power spectral density of turbulence in the upstream flow and the power spectral density of fluctuating wind load that it induces on a structure can be defined in terms of an aerodynamic admittance that is a function of the reduced frequency. A similar relationship in the time domain can be defined in terms of buffeting indicial functions. Generally, these relationships need to be determined experimentally since the flow around a structure in turbulent wind is too complex to be derived analytically.
These are also referred as impulse response functions (Chen and Kareem 2002) and counterparts of the indicial functions that are used to define the aeroelastic forces. Generally, these relationships need to be determined experimentally since the flow around a structure in turbulent wind is too complex to be handled analytically. For a dodecagonal cylinder, the aerodynamic admittance functions for drag and lift forces were obtained experimentally from static wind-tunnel model tests.
To accomplish this, a gust generator was fixed upstream of the model to generate a sinusoidal gust, with vertical and horizontal velocity fluctuations, at a fixed frequency. This device is made up of two thin airfoils with a gap of 203 mm (8 in.) between them. The airfoils are linked together and driven by a set of levers attached to a step motor. The gust generator system was placed at an upstream distance of 152 mm (6 in.) from the front surface of the cylinder and could oscillate with a maximum amplitude of approximately ±6 degree to produce the wind gust. An x-hot-wire probe was used to obtain the horizontal and vertical wind velocity fluctuations and force transducers were used to simultaneously measure the aerodynamic lift or drag on the model. The hot-wire x-probe was placed along the centerline of the model between the model and the gust generator. The buffeting indicial functions for drag and lift forces were derived from the obtained aerodynamic admittance functions. The power spectral density functions for the buffeting forces in along-wind and lateral-wind directions are follows:
Where,
Figure 8 shows the aerodynamic admittance functions calculated from the buffeting wind-tunnel tests. The frequency of the gust generator and the wind speed were both chosen to obtain a range of the reduced frequency (K) from 0.005 to 1.5. Specifically, the frequency of the gust generator ranged from approximately 0.2 to 4 Hz while the wind velocity varied approximately 5 to 65 ft/s (1.5 to 19.8 m/s).
The objective of the work presented here was to develop a universal model for predicting buffeting, self-excited and vortex shedding induced response of a slender structure in time domain for fatigue design. To accomplish this, wind tunnel tests of the multisided cross section to extract its aerodynamic properties was used as inputs in the coupled dynamic equations of motion for predicting the wind-induced response.
The wind tunnel tests on section models of the HMLP cross section (8 and 12-sided cylinders) were conducted in the Bill James Wind Tunnel in the WiST Laboratory at Iowa State University. Finally, the dynamic models that were developed for predicting the wind-excited response was validated by comparing the simulation results, obtained with aerodynamic parameters and wind speed parameters measured in wind tunnel and field, respectively, with the data collected in the field. The study contributes to the procedure for the extraction of indicial functions that define the buffeting forces and their actual forms in addition to systematically finding other aerodynamic parameters of a 12-sided cylinder.
The following conclusions can be drawn based on the current work as presented in this paper:
Aerodynamic admittance functions for a dodecagonal cylinder
The world’s human population increases by approximately 240,000 people every day: it is expected to reach 8 billion by 2025 and approximately 9.6 billion by 2050. Cultivated land is at a near-maximum, yet estimates predict that food production must be increased by 70% for worldwide peace to persist circa 2050 [1]. Thus, producing sufficient food to meet the ever-growing demand for this rising population is an exceptional challenge to humanity. To succeed at this vital objective, we must build more efficient—yet sustainable—food production devices, farms, and infrastructures. To accomplish that objective, the precision farming concept—a set of methods and techniques to accurately manage variations in the field to increase crop productivity, business profitability, and ecosystem sustainability—has provided some remarkable solutions.
Figure 1 summarizes the cycle of precision agriculture and distinguishes the activities based on analysis and planning (right) from those that rely on providing motion (left). The solutions for activities illustrated in Figure 1 right are being based on information and communication technologies (ICT), whereas the activities on the left rely on tractors, essential devices in current agriculture, that are being automated and robotized and will be also critical in future agriculture (smart farms).
UGVs in the cycle of precision agriculture.
The activities indicated in Figure 1 left can be applied autonomously in an isolated manner, i.e., a fertilization-spreading task, can be performed autonomously if the appropriate implement tank has been filled with fertilizer and attached to a fueled autonomous tractor (UGV); the same concept is applicable to planting and spraying. In addition, harvesting systems must offload the yield every time their collectors are full. However, tasks such as refilling, refueling/recharging, implement attachment, and crop offloading are currently primarily performed manually. The question that arises is: would it be possible to automate all these activities? And if so, would it be possible to combine these activities with other already automated farm management activities to configure a fully automated system resembling the paradigm of the fully automated factory? Then, the combination becomes a fully automated farm in which humans are relegated to mere supervisors. Furthermore, exploiting this parallelism, can we push new developments for farms to mimic the smart factory model? This is the smart farm concept that represents a step forward from the automated farm into a fully connected and flexible system capable of (i) optimizing system performances across a wider network, (ii) learning from new conditions in real- or quasi-real time, (iii) adapting the system to new conditions, and (iv) executing complete production processes in an autonomous way [2]. A smart farm should rely on autonomous decision-making to (i) ensure asset efficiency, (ii) obtain better product quality, (iii) reduce costs, (iv) improve product safety and environmental sustainability, (v) reduce delivery time to consumers, and (vi) increase market share and profitability and stabilize the labor force.
Achieving the smart farm is a long-term mission that will demand design modifications and further improvements on systems and components of very dissimilar natures that are currently being used in agriculture. Some of these systems are outdoor autonomous vehicles or (more accurately) UGVs, which are essential in future agriculture for moving sensors and implementing to cover crop fields accurately and guarantee accurate perception and actuation (soil preparation, crop treatments, harvest, etc.). Thus, this chapter is devoted to bringing forward the features that UGVs should offer to achieve the smart farm concept. Solutions are focused on incorporating the new paradigms defined for smart factories while providing full mobility of the UGVs. These two activities will enable the definition of UGV requirements for smart farm applications.
To this end, the next section addresses the needs of UGVs in smart farms. Then, two main approaches to configure solutions for UGVs in agricultural tasks are described: the automation of conventional vehicles and specifically designed mobile platforms. Their advantages and shortcomings regarding their working features are highlighted. This material enables the definition of other operating characteristics of UGVs to meet the smart farm requirements. Finally, the last section presents some conclusions.
Ground mobile robots, equipped with advanced technologies for positioning and orientation, navigation, planning, and sensing, have already demonstrated their advantages in outdoor applications in industries such as mining [3], farming, and forestry [4, 5]. The commercial availability of GNSS has provided easy ways to configure autonomous vehicles or navigation systems to assist drivers in outdoor environments, especially in agriculture, where many highly accurate vehicle steering systems have become available [6, 7]. These systems aid operators in the precise guidance of tractors using LIDAR (light/laser detection and ranging) or GNSS technology but do not endow a vehicle or tool with any level of autonomy. Nevertheless, other critical technologies must also be incorporated to configure UGVs, such as the safety systems responsible for detecting obstacles in the robots’ path and safeguarding humans and animals in the robots’ surroundings as well as preventing collisions with obstacles or other robots. Finally, robot communications with operators and external servers (cloud technologies) through wireless communications that include the use of cyber-physical systems (CPSs) [8] and Internet of things (IoT) [9] techniques will be essential to incorporate decision-making systems based on big data analysis. Such integration will enable the expansion of decision processes into fields such as machine learning and artificial intelligence. Smart factories are based on the strongly intertwined concepts of CPS, IoT, big data, and cloud computing, and UGVs for smart farms should be based on the same principles to minimize the traditional delays in applying the same technologies to industry and agriculture.
The technology required to deploy more robotic systems into agriculture is available today, as are the clear economic and environmental benefits of doing so. For example, the global market for mobile robots, in which agricultural robots are a part, is expected to increase at a compound annual growth rate of over 15% from 2017 to 2025, according to recent forecast reports [10]. Nevertheless, manufacturers of agricultural machinery seem to be reluctant to commercialize fully robotic systems, although they have not missed the marketing potential of showing concepts [11, 12]. In any event, according to the Standing Committee on Agricultural Research [13], further efforts should be made by both researchers and private companies to invent new solutions.
Most of the robotics and automation systems that will be used in precision agriculture—including systems for fertilizing, planting, spraying, scouting, and harvesting (Figure 1)—will require the coordination of detection devices, agricultural implements, farm managing systems, and UGVs. Thus, several research groups and companies have been working on such systems. Specifically, two trends can be identified in the development of UGVs: the automation of conventional agricultural vehicles (tractors) and the development of specifically designed mobile platforms. The following sections discuss these two types of vehicles.
The tractor has been the central vehicle for executing most of the work required in a crop field. Equipped with the proper accessories, this machine can till, plant, fertilize, spray, haul, mow, and even harvest. Their adaptability to dissimilar tasks makes tractors a prime target for automation, which would enable productivity increases, improve safety, and reduce operational costs. Figure 2 shows an example of the technologies and equipment for automating agricultural tractors.
An example of agricultural tractor automation‑distribution of sensorial and actuation systems for transforming an agricultural tractor into a UGV (Gonzalez-de-Santos et al., 2017).
Numerous worldwide approaches to automating diverse types of tractors have been researched and developed since 1995 when the first GNSS was made available to the international civilian community of users, which opened the door for GPS-guided agricultural vehicles (auto-steering) and controlled-traffic farming.
The first evaluations of GPS systems for vehicle guidance in agriculture were also published in 1995 [14] demonstrating its potential and encouraging many research groups around the world to automate diverse types of tractors. The earliest attempts were made at Stanford University in 1996, where an automatic control system for an agricultural tractor was developed and tested on a large farm [15]. The system used a location system with four GPS antennas. Around the same time, researchers at the University of Illinois, USA, developed a guidance system for an autonomous tractor based on sensor fusion that included machine vision, real-time kinematics GPS (RTK-GPS), and a geometric direction sensor (GDS). The fusion integration methodology was based on an extended Kalman filter (EKF) and a two-dimensional probability-density-function statistical method. This system achieved a lateral average error of approximately 0.084 m at approximately 2.3 m s−1 [16].
A few years later, researchers at Carnegie Mellon University, USA, developed some projects that made significant contributions. The Demeter project was conceived as a next-generation self-propelled hay harvester for agricultural operations, and it became the most representative example of such activity [17]. The positional data was fused from a differential GPS, a wheel encoder (dead reckoning), and gyroscopic system sensors. The project resulted in a system that allowed an expert harvesting operator to harvest a field once, thus programming the field. Subsequently, an operator with lesser skill could “playback” the programmed field at a later date. The semi-autonomous agricultural spraying project, developed by the same research group, was devoted to making pesticide spraying significantly cheaper, safer, and more environmentally friendly [18]. This system enabled a remote operator to oversee the nighttime operation of up to four spraying vehicles. Another example is research conducted at the University of Florida, USA, [19], in which two individual autonomous guidance systems for use in a citrus grove were developed and tested along curved paths at a speed of approximately 3.1 m s−1. One system, based on machine vision, achieved an average guidance error of approximately 0.028 m. The other system, based on LIDAR guidance, achieved an average error of approximately 0.025 m.
Similar activities started in Europe in the 2000s. One example is the work performed at LASMEA-CEMAGREF, France, in 2001, which evaluated the possibilities of achieving recording-path tracking using a carrier phase differential GPS (CP-DGPS), as the only sensor. The vehicle heading was derived according to a Kalman state reconstructor and a nonlinear velocity independent control law was designed that relied on chained systems properties [20].
A relevant example of integrating UGVs with automated tools is the work conducted at the University of Aarhus and the University of Copenhagen, Denmark [21]. The system comprised an autonomous ground vehicle and a side shifting arrangement affixed to a weeding implement. Both the vehicle and the implement were equipped with RTK-GPS; thus, the two subsystems provided their own positions, allowing the vehicle to follow predefined GPS paths and enabling the implement to act on each individual plant, whose positions were automatically obtained during seeding.
Lately, some similar automations of agricultural tractors have been conducted using more modern equipment [22, 23], and some tractor manufacturers have already presented noncommercial autonomous tractors [11, 12]. This tendency to automate existing tractors has been applied to other types of lightweight vehicles for specific tasks in orchards such as tree pruning and training, blossom and fruit thinning, fruit harvesting, mowing, spraying, and sensing [24]. Table 1 summarizes the UGVs based on commercial vehicles for agricultural tasks.
Institution | Year | Description |
---|---|---|
Stanford University (USA) [15] | 1996 | Automatic large-farm tractor using 4 GPS antennas |
University of Illinois (USA) [16] | 1998 | A guidance system using a sensor based on machine vision, an RTK-GPS, and a GDS |
Carnegie Mellon University (USA)—Demeter project [17] | 1999 | A self-propelled hay harvester for agricultural operations |
Carnegie Mellon University (USA)—Autonomous Agricultural Spraying project [18] | 2002 | A ground-based vehicles for pesticide spraying |
LASMEA-CEMAGREF (France) [20] | 2001 | This study investigated the possibility of achieving vehicle guiding using a CP-DGPS as the only sensor |
University of Florida (USA) [19] | 2006 | An autonomous guidance system for citrus groves based on machine vision and LADAR |
University of Aarhus and the University of Copenhagen (Denmark) [21] | 2008 | An automatic intra-row weed control system connected to an unmanned tractor |
RHEA consortium (EU) [22] | 2014 | A fleet (3 units) of tractors that cooperated and collaborated in physical/chemical weed control and pesticide applications for trees |
Carnegie Mellon University (USA) [24] | 2015 | Self-driving orchard vehicles for orchard tasks |
University of Leuven (Belgium) [23] | 2015 | Tractor guidance using model predictive control for yaw dynamics |
UGVs based on commercial vehicles.
Nevertheless, UGVs suitable for agriculture remain far from commercialization, although many intermediate results have been incorporated into agricultural equipment—from harvesting to precise herbicide application. Essentially, these systems are installed on tractors owned by farmers and generally consist of a computer (the controller), a device for steering control, a localization system (mostly based on RTK-GPS), and a safety system (mostly based on LIDAR). Many of these systems are compatible only with advanced tractors that feature ISOBUS control technology [25], through which controllers connected to the ISOBUS can access other subsystems of the tractor (throttle, brakes, auxiliary valves, power takeoff, linkage, lights, etc.). Examples of these commercial systems are AutoDrive [26] and X-PERT [27].
An important shortcoming of these solutions is their lack of intelligence in solving problems, especially when obstacles are detected because they are not equipped with technology suitable for characterizing and identifying the obstacle type. This information is essential when defining any behavior other than simply stopping and waiting for the situation to be resolved. Another limitation of this approach is that the conventional configuration of a standard tractor driven by an operator is designed to maximize the productivity per hour; thus, the general architecture of the system (tractor plus equipment) is only roughly optimized.
The second approach to the configuration of mobile robots for agriculture is the development of autonomous ground vehicles with specific morphologies, where researchers develop ground mobile platforms inspired more by robotic principles than by tractor technologies. These platforms can be classified based on their locomotion system. Ground robots can be based on wheels, tracks, or legs. Although legged robots have high ground adaptability (that enables the vehicles to work on irregular and sloped terrain) and intrinsic omnidirectionality (which minimizes the headlands and, thus, maximizes croplands) and offer soil protection (discrete points in contact with the ground that minimize ground damage and ground compaction, an important issue in agriculture), they are uncommon in agriculture; however, legged robots provide extraordinary features when combined with wheels that can configure a disruptive locomotion system for smart farms. Such a structure (which consists of legs with wheels as feet) is known as a wheel-legged robot. The following sections present the characteristics, advantages, and disadvantages of these specifically designed types of robots.
The structure of a wheeled mobile platform depends on the following features:
Passive wheel: The wheel rotates freely around its shaft and does not provide power.
Active wheel: An actuator rotates the wheel to provide power.
Coordinated steering scheme: Two fixed active wheels at the rear of the platform coupled with two passive orienting wheels at the front of the platform are the most common wheel arrangement for vehicles. To maintain all wheels in a pure rolling condition during a turn, the wheels need to follow curved paths with different radii originating from a common center [29]. A special steering mechanism, the Ackermann steering system, which consists of a 4-bar trapezoidal mechanism (Figure 3a), can mechanically manage the angles of the two steering wheels. This system is used in all the vehicles presented in Table 2. It features medium mechanical complexity and medium control complexity. One advantage of this system is that a single actuator can steer both wheels. However, independent steering requires at least three actuators for steering and power (Figure 3b).
Skid steering scheme: Perhaps the simplest structure for a mobile robot consists of four fixed, active wheels, one on each corner of the mobile platform. Skid steering is accomplished by producing a differential thrust between the left and right sides of the vehicle, causing a heading change (Figure 3c). The two wheels on one side can be powered independently or by a single actuator. Thus, the motion of the wheels in the same direction produces backward/forward platform motion; and the motion of the wheels on one side in the opposite direction to the motion of wheels on the other side produces platform rotation.
Independent steering scheme: An independent steering scheme controls each wheel, moving it to the desired orientation angle and rotation speed (Figure 3d). This steering scheme makes wheel coordination and wheel position accuracy more complex but provides some advantages in maneuverability. In addition, this scheme provides crab steering (sideways motion at any angle α; 0 ≤ α ≤ 2π) by aligning all wheels at an angle α with respect to the longitudinal axis of the mobile platform. Finally, the coordination of driving and steering results in more efficient maneuverability and reduces internal power losses caused by actuator fighting. The independent steering scheme requires eight actuators for a four-wheel vehicle.
Steering driving systems: (a) Ackermann steering system; (b) independent steering; (c) skid steering system and (d) independent steering and traction system.
Steering scheme | Characteristics |
---|---|
Coordinated | Advantages:
|
Skid | Advantages:
|
Independent | Advantages:
|
Characteristics of wheeled structures.
Table 2 summarizes the advantages and drawbacks of these schemes. Note that the number of actuators increases the total mass of a robot as well as its mechanical and control complexity (more motors, more drivers, more elaborate coordinating algorithms, etc.).
Some examples of wheeled mobile platforms for agriculture are the conventional tractor using the Ackermann steering system (Figure 2) with two front passive and steerable wheels and two rear fixed and active wheels.
Skid steering platforms can be found in many versions. For example,
Four fixed wheels placed in pairs on both sides of the robot
Two fixed tracks, each one placed longitudinally at each side of the robot,
Two fixed wheels placed at the front of the robot and two castor wheels placed at the rear (Figure 4c), etc.
Pictures of several specifically-designed agricultural platforms. (a) Robot for weed detection, courtesy of T. Bak, Department of Agricultural Engineering, Danish Institute of Agricultural Sciences; (b) ladybird, courtesy of J. P. Underwood, Australian Centre for Field Robotics at the University of Sydney [
Regarding the independent steering scheme, the robot developed by Bak and Jakobsen [30] is one of the first representative examples (Figure 4a). This platform was designed specifically for agricultural tasks in wide-row crops and featured good ground clearance (approximately 0.5 m) and 1-m wheel separation. The platform is based on four-identical wheel modules. Each one includes a brushless electric motor that provides direct-drive power, and steering is achieved by a separate motor.
An example of a mobile platform under development that focuses on performing precision agricultural tasks is AgBot II (Figure 4c). This is a platform that follows the skid steering scheme with two front fixed wheels (working in skid or differential mode) and two rear caster wheels. It is intended to work autonomously on both large-scale and horticultural crops, applying fertilizer, detecting and classifying weeds, and killing weeds either mechanically or chemically [31, 32]. Another robot is Robot for Intelligent Perception and Precision Application (RIPPA), which is a light, rugged, and easy-to-operate prototype for the vegetable growing industry. It is used for autonomous high-speed, spot spraying of weeds using a directed micro-dose of liquid when equipped with a variable injection intelligent precision applicator [33]. Another example is Ladybird (Figure 4b), an omnidirectional robot powered with batteries and solar panels that follows the independent steering scheme. The robot includes many sensors (i.e., hyperspectral cameras, thermal and infrared detecting systems, panoramic and stereovision cameras, LIDAR, and GPS) that enable assessing crop properties [34]. One more prototype, very close to commercialization, is Kongskilde Vibro Crop Robotti, which is a self-contained track-based platform that uses the skid steering scheme. It can be equipped with implements for precision seeding and mechanical row crop cleaning units. This robot can work for 2–4 hours at a 2–5 km h−1 rate and is supplied by captured electric energy [35].
These robots are targeted toward fertilizing, seeding, weed control, and gathering information, and they have similar characteristics in terms of weight, load capacity, operational speed, and morphology. Tools, instrumentation equipment, and agricultural implements are connected under the robot, and tasks are performed in the area just below the robot, which optimizes implement weight distribution. These robots have limitations for use on farmland with substantial (medium to high) slopes or gully erosion. Nevertheless, some mobile platforms are already commercially available. Two examples of these vehicles are the fruit robots Cäsar [36] and Greenbot [37].
Cäsar is a remote-controlled special-purpose vehicle that can perform temporarily autonomous operations in orchards and vineyards such as pest management, soil management, fertilization, harvesting, and transport. Similarly, Greenbot is a self-driving machine specially developed for professionals in the agricultural and horticultural sectors who perform regular, repetitious tasks. This vehicle can be used not only for fruit farming, horticulture, and arable farming but also in the urban sector and even at waterfronts or on roadsides.
Despite their current features, the existing robots lack flexibility and terrain adaptability to cope with diverse scenarios, and their safety features are limited. For example:
They focus only on orchard and vineyard activities.
They have ground clearance limitations.
They are unsuitable for rough terrain or slopes.
They must be manually guided to the working area rather than freely and autonomously moving to different working areas around the farm.
They possess no advanced detection systems for weed or soil identification, which limits their use to previously planned tasks related to selective treatment.
They lack dynamic safety systems capable of recognizing or interpreting safety issues; thus, they are incapable of rescheduling or solving problems by themselves.
In addition, existing UGVs for agriculture lack communication mechanisms for providing services through cloud technologies, CPS, and IoT techniques, crucial instruments to integrate decision-making systems based on big data analysis, as is being done in the smart factory concept.
Table 3 summarizes the diverse robotic platforms, and Figure 4 depicts some of these platforms.
Vehicle | Type* | Year | Description |
---|---|---|---|
AgBot II [32] | P | 2014 | A platform that follows the skid steering scheme with two front fixed wheels (working in skid or differential mode) and two rear caster wheels |
Ladybird [34] | P | 2015 | An omnidirectional robot powered with batteries and solar panels that uses the independent steering scheme |
Greenbot [37] | C | 2015 | A self-driving robot for tasks in agriculture and horticulture |
Cäsar [36] | P | 2016 | A remotely controlled platform for temporary, autonomous use in fruit plantations and vineyards |
RIPPA [33] | P | 2016 | A light, rugged, and easy-to-operate prototype for the vegetable growing industry |
Vibro Crop Robotti [35] | C | 2017 | A self-contained track-based platform that uses the skid steering scheme |
Robots designed specifically for agriculture.
P-prototype; C-commercial.
The structure of a wheel-legged mobile platform depends on (i) the number of legs, (ii) the leg type, and (iii) the leg arrangement. The feet consist of 2-DOF steerable powered wheels as illustrated in Figure 5.
Wheel-legged structures. (a) 4-DOF articulated leg; (b) 3-DOF SCARA leg; (c) 2-DOF SCARA leg; (d) 1-DOF leg.
Structure | Characteristics |
---|---|
A 4-DOF articulated leg with a 2-DOF wheeled foot (Figure 5a) | Advantages:
|
A 3-DOF motion-decoupled leg* with a 2-DOF wheeled foot (Figure 5b) | Advantages:
|
A 2-DOF motion-decoupled leg* with a 2-DOF wheeled foot (Figure 5c) | Advantages:
|
A 1-DOF leg with a 2-DOF wheeled foot (Figure 5d) | Advantages:
|
Wheel-legged structures.
Cylindrical, Selective Compliant Articulated Robot Arm (SCARA) or Cartesian.
Figure 6a illustrates the structure scheme of a wheel-legged robot based on the 3-DOF SCARA leg (See Figure 5b) with full terrain adaptability, ground clearance control, crop adaptability, and capability of walking, and Figure 6b shows the structure of a wheel-legged robot exhibiting full terrain adaptability, ground clearance control, and crop adaptability; however, it cannot walk under static stability.
Model of wheel-legs: (a) full terrain-crop adaptability, (b) full terrain and partial crop adaptability.
Another interesting example is the structure of BoniRob [39], a real wheel-legged platform for multipurpose agriculture applications, which consists of four independently steerable powered wheeled legs with the structure illustrated in Figure 5d (1-DOF legs with a 2-DOF wheeled foot). This robot can adjust the distance between its wheel sets, making it adaptable to many agricultural scenarios. The platform can be equipped with common sensorial systems used in robotic agricultural applications, such as LIDAR, inertial sensors, wheel odometry, and GPS. Moreover, the robotic platform can be retrofitted and upgraded with swappable application modules or tools for crop and weed identification, plant breeding applications, and weed control. This robotic platform is completely powered by electricity, which is more environmentally friendly but reduces its operational working time compared to conventional combustion-engine systems. Nevertheless, this robot configuration requires custom-built implements, which prevent the reuse of existing implements and, thus, jeopardize the introduction of this robot to the agricultural market.
In addition to their needed characteristics for infield operations, the robots fulfilling the demands of a smart farm will require the operating requirements summarized in the following paragraphs and Table 5.
Characteristics | Value |
---|---|
Dimensions | Length: ~3.0 m; width: ~1.50 m; height: ~1.00 m |
Weight | 1200–1700 kg |
Payload | 500–1000 kg |
Comments: These characteristics are estimations based on the current medium-sized vehicles reported in this chapter that are capable of carrying agricultural implements. Robots for carrying sensing systems can be truly small (low payloads), but vehicles for treatments need to carry medium to heavy loads (pesticides, fertilizes, etc.). For example, existing sprayers [45] weigh approximately 600–700 kg including 200–300 L of active ingredient. | |
Speed | 3–25 km h−1 |
Comments: Treatment speed is limited by the treatment process that depends on physical laws. However, robots need to move among working fields minimizing moving time; therefore, they must feature a reasonably high top speed. | |
Position accuracy | ±0.02 m |
Comments: The current DGPS accuracy seems to be sufficient for real applications. However, specific real-time localization systems, RTLS, can be used in small areas where GNSS is unavailable (radio frequency identification tags (RFID), ultra-wide band tags (UWB), etc.). These technologies will be essential in smart farms to ensure positioning precision in GNSS occluded areas. | |
Clearance | 0.35–1 m |
Comments: Weed control is performed at an early crop-growth stage; therefore, the minimum ground clearance of the robot must be approximately 0.35 m. A ground clearance of approximately 1 m will facilitate application of treatments at later crop-growth stages. The ideal approach would be to control the ground clearance to optimize the working height of the implements based on the crop. Existing robots cannot control their ground clearance, but some wheel-legged configurations can meet this specification (Figure 5a,b, and c). | |
Track width | 1.50–2.25 m |
Comments: To preserve crops in narrow-row situations, a tramline control is required; however, in wide-row crops, the tramlines must be located in the inter-row spacing. Taking maize as an example, which is planted at an inter-row spacing of approximately 0.75 m in some areas in Europe, a robot track width of 1.50 to 2.25 m is required to enable 2 or 3 rows to pass under the robot’s body. Controlling robot track width is imperative in a smart farm world. This characteristic is exhibited by wheeled-legged robots, which makes them a good candidate for UGVs in smart farms. | |
Energetic autonomy | ~10 h |
Comments: Robots based on combustion engines (e.g., tractors) can operate autonomously for approximately 10 hours, at minimum. The duration of autonomous operation for electrically driven systems should be similar. Some existing prototypes already meet this expectation [31]. In any case, the increasing improvement in battery technology will enlarge the energetic autonomy of future vehicles and robots. |
Prospective characteristics for UGVs in smart farms.
Although conventional tractors are proven and highly reliable machines, they lack some adaptability features. Tractors have normally fixed distances between wheels, which makes them unsuitable for working on crops with different distances between rows. Using mobile platforms capable of controlling the distance between wheels could alleviate this problem, allowing the machines to adapt to different crops under different situations.
A steering system capable of zero-radius turns would be a proper solution, and this feature can be implemented by different structures as discussed in the previous section. Thus, minimization of headlands and wheel distance control can be achieved using either conventional or new articulated structures. Among the conventional structures, the skid steering scheme based on wheels or tracks is capable of zero-radius turns without additional steering mechanism, which helps in minimizing the headlands. However, separating and controlling the distance between contralateral wheels/tracks requires an active system (which already exists for some tracked vehicles used in the building industry).
Mobile platform structures based on coordinated or independent steering schemes can achieve zero-radius turns, but they still lack intrinsic track width control and require additional mechanisms. Another structure is the wheel-legged mechanism. Legged robots exhibit high terrain adaptability on irregular ground, but wheeled robots have speed advantages on smooth terrain; that is, they complement each other. Therefore, the most complete wheel-legged mechanism (Figure 6a) is a leg with three degrees of freedom [38] with an active wheel as a foot, where the wheel is steered and driven separately. This is a disruptive design not verified yet that will provide extraordinary characteristics to robots for smart farm applications. Thus, the wheels drive and steer, while the legs provide track-width control and terrain adaptation, i.e., they control the robot’s body leveling and ground clearance. This is the most capable system regarding ground clearance and body pose control, but it comes at the cost of higher mechanical complexity. Nevertheless, intermediate solutions can be developed to reduce the number of actuators while maintaining appropriate robot characteristics. Table 4 summarizes different wheel-legged theoretical solutions indicating advantages and shortcomings, and Figure 5 shows some sketches of practical solutions.
Minimize energy consumption by optimizing the robot trajectories during the mission
Drastically reduce the use of herbicides and fertilizers by using intelligent detection systems, tools, and decision-making algorithms
Eliminate the need for a driver and minimize operator risk
Minimize unnecessary crop damage and soil compaction
Wireless communications with the operator and/or a central controller for control commands and data exchanges, including images and real-time video, will be required. Wireless communication among robots will also be required for coordination and collaboration.
Safety for humans and robots can usually be accomplished through a combination of computer vision, LIDAR, and proximity sensors to infer dangerous situations and halt robot motion, whereas safety to crops is achieved through precise steering that guides the robot to follow the crop rows accurately using the crop position acquired at seeding time or real-time crop-detection systems. Following these three stages, a step forward in safety for agricultural robots would be the integration of a two-level safety system relying on the following:
Regardless of the exact approach, standards on safety machinery must be taken into consideration [42] to ensure that systems will meet regulations and will be able to achieve certification.
Based on the existing agricultural vehicles and robot prototypes, robots to be deployed in smart farms should meet also the characteristics presented in Table 5.
The world population is increasing rapidly, causing a demand for more efficient production processes that must be both safe and respect the ecosystem. Industry has already planned to meet production challenges in the coming decades by defining the concept of the smart factory; the agriculture sector should follow a similar path to design the concept of the smart farm: a system capable of optimizing its performance across a wide network, learning from new conditions in real time and adapting the system to them and executing the complete production process in an autonomous manner. Smart factory and smart farm concepts have many commonalities and include some common solutions, but some specific aspects of smart farms should be studied separately. For example, the design of UGVs for outdoor tasks in agriculture (field robots) presents specific characteristics worthy of explicit efforts.
This chapter focused on reviewing the past and present developments of UGVs for agriculture and anticipated some characteristics that these robots should feature for fulfilling the requirements of smart farms. To this end, this chapter presented and criticized two trends in building UGVs for smart farms based on (i) commercial vehicles and (ii) mobile platforms designed on purpose. The former has been useful for evaluating the advantages of UGV in agriculture, but the latter offers additional benefits such as increased maneuverability, better adaptability to crops, and improved adaptability to the terrain. Clearly, independent-steering and skid-steering systems provide the best maneuverability, but depending on their complexity, wheel-legged structures can provide similar maneuverability and improved adaptability to crops and terrain as well as increased stability on sloped terrain. For example, the 4-DOF articulated wheeled leg (Figure 5a) and the 3-DOF SCARA leg (Figure 5b and 6a) exhibit the best features at the cost of being the most complex. Note that although both structures have the same maneuverability features and adaptability to crops and terrain (ground clearance, body leveling, etc.), the 3-DOF SCARA leg involves one fewer motor per leg, which decreases the price and weight and improves the reliability of the robot. However, the 2-DOF SCARA leg also exhibits useful features regarding maneuverability, adaptability to crops, and adaptability to terrain (ground clearance control and body leveling) while using fewer actuators (Figure 5c and 6b). For agricultural tasks carried out on flat terrain, the 1-DOF leg with a 2-DOF wheeled foot provides sufficient maneuverability and adaptability to crops with very few actuators (leg structure as in Figure 5d).
However, these robots also require some additional features to meet the needs of the smart farm concept, such as the following:
Flexibility to work on very dissimilar scenarios and tasks.
Maneuverability to perform zero-radius turns, crab motion, etc.
Resilience to recover itself from malfunctions.
Efficiency in the minimization of pesticide and energy usage.
Intuitive, reliable, comfortable, and safe HMIs attractive to nonrobotic experts to ease the introduction of robotic systems in agriculture.
Wireless communications to communicate commands and data among the robots, the operator, and external servers for enabling CPSs, IoT, and cloud computing techniques to support services through the Internet.
Safety systems to ensure safe operations to humans, crops, and robots.
Environmental impact by reducing chemicals in the ground and pollutants into the air.
Standards: operational robots have to meet the requirements and specifications of the standards in force for agricultural vehicles.
Implement usage: although specific onboard implements for UGV are appearing, the capability of also using conventional implements will help in the acceptation of new technologies by farmers and, hence, the introduction of new-generation robotic systems.
Autonomy: both behavioral autonomy and operation autonomy. Regarding power supplies, automobiles worldwide will likely be electric vehicles powered by batteries within the next few decades; thus, agricultural vehicles should embrace the same solution.
Regardless of these characteristics, UGVs for smart farms have to fulfill the requirements of multi-robot systems, which is a fast-growing trend [22, 40, 46]. Multi-robot systems based on small-/medium-sized robots can accomplish the same work as a large machine, but with better positioning accuracy, greater fault tolerance, and lighter weights, thus reducing soil compaction and improving safety. Moreover, they can support mission coordination and reconfiguration. These capabilities position small/medium multi-robot systems as prime future candidates for outdoor UGVs in agriculture. Additionally, UGVs for smart farms should exhibit some quantitative physical characteristics founded on past developments and current studies that are summarized in Table 5.
Finally, autonomous robots of any type, working in fleets or alone, are essential for the precision application of herbicides and fertilizers. These activities reduce the use of chemicals generating important benefits: (i) a decrease in the cost of chemical usage, which impacts in the system productivity; (ii) an improvement in safety for operators, who are moved far from the vehicles; (iii) better health for the people around the fields, who are not exposed to the effects of chemical; and (iii) improved quality of foods that will reduce the content of toxic products.
The research leading to these results has received funding from (i) RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (“Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV”; S2018/NMT-4331), funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; (ii) the Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC) under the BMCrop project, Ref. 201750E089; and (iii) the Spanish Ministry of Economy, Industry and Competitiveness under Grant DPI2017-84253-C2-1-R.
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