In this paper, we present a novel method to control a rigidly connected location on the vehicle, such as a point on the implement in case of agricultural tasks. Agricultural robots are transforming modern farming by enabling precise and efficient operations, replacing humans in arduous tasks while reducing the use of chemicals. Traditionally, path-following algorithms are designed to guide the vehicle's center along a predefined trajectory. However, since the actual agronomic task is performed by the implement, it is essential to control a specific point on the implement itself rather than the vehicle's center. As such, we present in this paper two approaches for achieving the control of an offset point on the robot. The first approach adapts existing control laws, initially intended for the rear axle's midpoint, to manage the desired lateral deviation. The second approach employs backstepping control techniques to create a control law that directly targets the implement. We conduct realworld experiments, highlighting the limitations of traditional approaches for offset point control, and demonstrating the strengths and weaknesses of the proposed methods.

We propose a new approach to control the shape of deformable objects with robots. Specifically, we consider a fixed-length elastic linear object lying on a 2D workspace. Our main idea is to encode the object's deformation behavior in an offline constant Jacobian matrix. To derive this Jacobian, we use geometric deformation modeling and combine recent work from the fields of deformable object control and multirobot systems. Based on this Jacobian, we then propose a robotic control law that is capable of driving a set of shape features on the object toward prescribed values. Our contribution relative to existing approaches is that at run-time we do not need to measure the full shape of the object or to estimate/simulate a deformation model. This simplification is achieved thanks to having abstracted the deformation behavior as an offline model. We illustrate the proposed approach in simulation and in experiments with real deformable linear objects.

During the off-road path following of a wheeled mobile robot in presence of poor grip conditions, the longitudinal velocity should be limited in order to maintain safe navigation with limited tracking errors, while at the same time being high enough to minimize travel time. Thus, this paper presents a new approach of online speed fluctuation, capable of limiting the lateral error below a given threshold, while maximizing the longitudinal velocity. This is accomplished using a neural network trained with a reinforcement learning method. This speed modulation is done side-by-side with an existing model-based predictive steering control, using a state estimator and dynamic observers. Simulated and experimental results show a decrease in tracking error, while maintaining a consistent travel time when compared to a classical constant speed method and to a kinematic speed fluctuation method.

In the last few years, fleets of mobile robots have received increased interest in agriculture with the development of master/slaves control approaches. This paper proposes on the contrary a planning strategy enabling to generate beforehand the trajectory of each robot. For that, the fleet is considered as a single mobile entity with its steering and speed constraints. An admissible trajectory for this virtual entity, including maneuver phases, is generated to cover the shape of a given field. This one is next used to plan the trajectories of the actual robots. A panel of actual fields with different fleets of robots enables to highlight the relevance of the strategy proposed.

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This paper proposes a new approach for online control law gains adaptation, through the use of neural networks and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to optimize the behavior of the robot with respect to an objective function. The neural network considered takes as input the current observed state as well as its uncertainty, and provides as output the control law gains. It is trained, using the CMA-ES algorithm, on a simulator reproducing the vehicle dynamics. Then, it is tested in real conditions on an agricultural mobile robot at different speeds. The transferability of this method from simulation to a real system is demonstrated, as well as its robustness to environmental changes, such as GPS signal degradation or ground variation. As a result, path following errors are reduced, while ensuring tracking stability.

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International audience

Fault Detection and Isolation (FDI) is a crucial task to ensure greater autonomy of mobile robots. This paper looks at the different techniques that serve the FDI process of mobile robots. In a first time, studied faults and characteristics that FDI methods must satisfy are listed. Then, 14 FDI methods are explained. They belong to 4 categories: Model-based, Knowledge-bases, Data-based and Material Redundancy approaches. Conditions of application, drawbacks and advantages are defined for each of them. A comparison between two methods (EKF and UKF) is made in simulation. The detection and isolation rate is good for GPS, IMU and Odometers faults but unacceptable for slip, blocked wheel and free wheel faults. This work is a first step toward designing a hybrid method to monitor the maximum of likely faults on mobile robots in real-time.

International audience

In this paper, the problem associated with accurate control of a two-wheel steering mobile robot following a path is addressed thanks to a backstepping control strategy. This approach involves an observer to estimate the grip conditions, based on previous work, and the proposed control algorithm for the front axle. Since the significant parameters of the grip conditions are available from the observer, namely the sideslip angles and the cornering stiffnesses, it is then suitable to include them into an algorithm to control mobile robots and obtain a more accurate path tracking. This is made possible by gathering into a single backstepping approach both kinematic and dynamic models. This new point of view permits to take account of both kinematic and dynamic behaviors and grip parameters in the control law. The proposed approach is experimentally evaluated at different speeds and compared with two other state-of-the-art path tracking algorithms and evaluated for several values of lateral deviations.

Many agricultural tasks are known to be dangerous for human operators, the environment, and human health in general. The increasing pressure both on safety and on production levels motivates the development of new methodologies and technologies. The rising of off-road mobile robots for agricultural application appears to be a promising contribution to required innovations. It both permits to limit the exposure of people to hazardous products and to achieve difficult and repetitive tasks. Nevertheless, to be fully efficient, autonomous robots have to ensure a high level of accuracy, while carrying potentially heavy tools, possibly in harsh conditions. It is especially the case of spraying, for which accuracy is a key challenge for reducing environmental impacts. The use of huge robots for spraying might seem to be a straightforward solution, by simply automating existing machines. Nevertheless, a simple automation does not reduce directly the environmental impact of human activities (soil compaction, energy, reduction of the use of chemical products). Moreover, huge machines are not necessarily an advantage when considering safety aspects (rollover risk and maneuverability). As a result, a solution based on the cooperation of at least two mobile robots, moving from either side of a vine row, is investigated in this paper thanks to Ultra Wide Band (UWB) technology.

In this paper, the problems associated with accurate path tracking control in off-road conditions is addressed with model-based adaptive control. In particular, the estimation of grip conditions is investigated through the derivation of a new observer and by gathering kinematic and dynamic models into a single framework. This new reference point employs a unique observer regardless of the velocity of the robots. Previous approaches necessitated the switching of models depending upon the phenomena encountered as well as robot dynamics. The observer proposed here allows an accurate and reactive estimation of sliding. This permits to feed relevantly a control law based on an extended kinematic model, enabling accurate path tracking, even in harsh conditions and when facing significant dynamic effects such as spin around.

One of the most important points in path tracking applications, is the capability of the robot to track the desired path as accurately as possible. Results presented in the literature in on-road contexts are convincing, but the case of off-road mobile robots introduces other issues, like bad grip conditions or non-flat ground. These issues can lead to a lack of accuracy of the tracking, even more when we consider high speed. The quality of the tracking depends on the modelling and the control laws used, but it depends also on the path to follow and on the soil conditions. If some small errors may be acceptable, it is sometimes mandatory to have a limited error, in particular when avoiding an obstacle or when moving in a narrow pathway. The aim of the algorithm proposed in this paper is to determine the maximum velocity of the robot during the tracking, for the lateral error to stay below a desired limit. In order to achieve this, a predictive approach taking into account for the evolution of a dynamic model, control laws and actuators properties is proposed. It permits to predict the forthcoming tracking error and consequently to estimate the maximum velocity that the robot can reach in accordance with the maximum deviation allowed.

We describe an on-line observer that allows to monitor the soil-tire contact longitudinal stiffness of off-road vehicles equipped with low cost sensors. The knowledge of such a value is a milestone in the development of active security devices, or at least a mean to inform the driver about the longitudinal stability of his vehicle. Indeed, these vehicles are frequently subject to ground changes and high slopes, causing fatal losses of control - slipping, rollover, etc. - every year. The observer has been tested and validated on a grape harvester in various conditions and its output evaluated with respect to an estimation based on expensive but precise sensors. These experimental results show that the driver can be informed in real-time about the longitudinal stability of his vehicle and alerted about critical situations.

The aim of a mobile robot path tracking algorithm is to ensure that the desired path is followed as accurately as possible. This problem has been intensively studied in literature with satisfactory results in on-road context. Nevertheless, performances may be depreciated when the expected ideal conditions are no longer satisfied, as it is the case when moving off-road: in such a context, bad grip conditions together with actuator saturations may generate significant perturbations, especially at high speed. Beyond a lack of accuracy, instabilities (such as spin around or non-controllability) may arise. This paper proposes an adaptive and predictive approach in order to preserve the path tracking stability thanks to the modulation of the robot velocity. Relying on the on-line observation of the grip conditions and the reference path properties, the maximal velocity admissible in a near future is computed and applied, if necessary, instead of the desired speed. A steering angle control law, designed to be independent of the robot speed, acts in parallel. The capabilities of this algorithm are tested through actual experiments with a mobile off-road platform.

In this paper a control strategy for a mobile robot enabling to track a manually driven vehicle or a moving target is proposed in the context of natural environment. In such a context, the motion does not meet classical assumptions usually proposed for mobile robots, since the terrain geometry is not necessarily flat and wheels are subject to sliding. As a result, in order to preserve the accuracy of tracking, it appears necessary to account for such phenomena in the control law. Several observer-based approaches have already been developed in the framework of path following in off-road conditions, but suffer from several limitations. In particular, the velocity should not be null, which appears to be an important drawback in the proposed application: the tracking of a non-autonomous vehicle indeed imposes possible stops. In this paper, a new observation strategy is proposed allowing to avoid non-observable situations (null velocity). This permits to achieve an accurate vehicle tracking whatever its velocity, its trajectory and the grip conditions.

Mobile robotics constitutes a promising way to reduce the environmental impact of agricultural activities while preserving the level of production to satisfy the growing population demand. In previous work, a formation control law, accurate despite typical off-road conditions (low grip, terrain irregularities, etc), based on a nonlinear observer-based adaptive control has been presented. Satisfactory advanced results have been reported in lateral servoing but inaccuracies in longitudinal regulation have been noticed. In this paper a new predictive approach dedicated to both longitudinal and lateral servoing is proposed. We consider mobile robots made of a single body accounting for bad grip conditions thanks to an observer based approach. After presenting the modelling of the formation and the control law use to maintain longitudinal and lateral deviations with respect to the reference trajectory, we introduce the predictive approach on velocity. Full-scale experiments demonstrate the performance of the proposed approach.

Off-road mobile robotics may have important interest in many fields of application such as agriculture or surveillance. In this paper, the control of a fleet of wheeled mobile robots, equipped with RTK-GPS sensors and communicating through WiFi, is investigated. The focus is particularly set on the control of a formation of several robots with respect to a reference trajectory, previously learned or computed off-line. Non-linear exact transformations permit to achieve a laterally and longitudinally decoupled model; from which the control of steering angle and velocity are derived separately in order to ensure the desired formation shape. Since the control of lateral distance to the reference trajectory is based on other works, only the longitudinal control is detailed in this paper. It is based on an adaptive and predictive control algorithm, in order to account for both sliding and actuator delays. The experimental results demonstrate the capabilities of the proposed approach.

This paper proposes an algorithm dedicated to off-road mobile robot path tracking at high speed. In order to ensure a high accuracy, a predictive and adaptive approach is developed to face the various perturbations due to this context (mainly the bad grip conditions and the terrain geometry). The control law is based on previous work, and requires the knowledge of sideslip angles, which cannot be directly measured. As a result, an observer based on two levels of modeling (kinematic and dynamic) is proposed to ensure a relevant and fast estimation. If the kinematic part is independent from the terrain geometry, the dynamic model used in this paper requires to take explicitly into account the influence of the terrain geometry on mobile robot dynamic. It is achieved by the introduction of the lateral robot inclination, which is on-line estimated via a Kalman filter and integrated into the dynamical model. The advantages of the proposed contribution to path tracking control are investigated through full-scale experiments achieved at high speed (up to 6m/s) on an uneven and grass field.

In this paper, an ATV (All-Terrain Vehicle) rollover prevention system is proposed. Dynamic instability evaluation is based on the on-line estimation and prediction of the Lateral Load Transfer (LLT) from a vehicle model based on two 2D representations. As off-road vehicles are considered, grip conditions have a large influence. They are here estimated relying on observation theory. Nevertheless, two main behaviours (over/under-steering) may be encountered pending on grip and vehicle configuration. Since only a low cost perception system can be considered in ATV applications, these two opposite dynamics cannot be explicitly discriminated. As a result, two observers are designed, according to the vehicle behaviour, to estimate on-line the terrain properties (grip conditions, global sideslip angle and bank angle) and a “supervisor” selects on-line the right observer. Next, a predictive control algorithm, based on the extrapolation of rider's action and the selected estimated dynamical state, allows the rollover risk to be anticipated, enabling to warn the pilot and to consider the implementation of active actions. Simulations and full-scale experimentations are presented to discuss the efficiency of the proposed solution.

In this article a wheeled mobile robot system is presented, designed for the investigation of vehicle properties at dynamic maneuvering. The proposed solution comprises two different aspects: the mechanical design and a system-on-chip control system. As many other differentially steered mobile robots, the vehicle has two coaxial driving wheels and a passive castor wheel. This freely moving unconstrained wheel is equipped with additional rotary encoders, while the wheel angular speeds are accurately determined by a hardware timer based measurement. Thus, improved vehicle speed estimation is achieved, including the detection and quantification of possible slip on the actuated wheels. These measurements are sufficiently accurate to derive of a simple case specific empirical tire model, based only on odometry data.

This paper is focused on the design of a control strategy for the path tracking of off-road mobile robots acting at high speed. In order to achieve high accuracy in such a context, uncertain and fast dynamics have to be explicitly taken into account. Since these phenomena (grip conditions, delays due to inertial and low-level control properties) are hardly measurable directly, the proposed approach relies on predictive and observer-based adaptive control techniques. In particular, the adaptive part is based on an observer loop, taking advantage of both kinematic and dynamic vehicle models. This multi-model based adaptive approach permits to adapt on-line the grip conditions (represented by cornering stiffnesses), enabling highly reactive sideslip angles observation and then accurate path tracking. The relevance of this approach is investigated through full scale experiments.

In mobile robot path tracking applications, an autonomous vehicle is steered to stay as close as possible to a desired path. If lateral wheel slip is an important variable, as it is the case at high speed and due to low tire-ground friction in off-road applications, limits of the steering actuators, the major input constraints of the system, have a major influence on the tracking control performance. This paper presents an algorithm to control the longitudinal velocity, a secondary control variable, of a mobile robot in order to respect the boundedness of the steering angle, and thus to improve the vehicle safety. The applicability of the algorithm has been verified through experiments with an off-road mobile robot.

Rollover is the principal cause of serious accidents for All-Terrain Vehicles (ATV), especially for light vehicles (e.g. quad bikes). In order to reduce this risk, the development of active devices, contributes a promising solution. With this aim, this paper proposes an algorithm allowing to predict the rollover risk, by means of an on-line estimation of a stability criterion. Among several rollover indicators, the Lateral Load Transfer (LLT) has been chosen because its estimation needs only low cost sensing equipment compared to the price of a light ATV. An adapted backstepping observer associated to a bicycle model is first developed, allowing the estimation of the grip conditions. In addition, the lateral slope is estimated thanks to a classical Kalman filter relying on measured acceleration and roll rate. Then, an expression of the LLT is derived from a roll model taking into account the grip conditions and the slope. Finally, the LLT value is anticipated by means of a prediction algorithm. The capabilities of this system are investigated thanks to full scale experiments with a quad bike.

Automation in outdoor applications (farming, surveillance, etc.) requires highly accurate control of mobile robots, at high speed, accounting for natural ground specificities (mainly sliding effects). In previous work, predictive control algorithms dedicated to All-Terrain Vehicle lateral stability was investigated. Satisfactory advanced simulation results have been reported but no experimental ones were presented. In this paper, the prevention of a real off-road mobile robot rollover is addressed. First, both rollover dynamic modeling and previous work on a Mixed observer designed to estimate on-line sliding phenomena for path tracking control are recalled. Then, this observer is here used to compute a rollover indicator accounting for sliding phenomena, from a low-cost perception system. Next, the maximum vehicle velocity, compatible with a safe motion over some horizon of prediction, is computed via Predictive Functional Control (PFC), and can then be applied, if needed, to the vehicle actuator to prevent from rollover. The capabilities of the proposed device are demonstrated and discussed thanks to real experimentation.

This paper addresses the problem of path generation and motion control for the autonomous maneuver of a farm vehicle with a trailed implement in headland. A reverse turn planner is firstly investigated, based on primitives connected together to easily generate the reference motion. Then, both steering and speed control algorithms are presented to accurately guide the vehicle-trailer system. They are based on a kinematic model extended with additional sliding parameters and on model predictive control approaches. Real world experiments have been carried out on a low friction terrain with an experimental mobile robot pulling a trailer. At the end of each row, the reverse turn is automatically generated to connect the next reference track, and the maneuver is autonomously performed by the vehicle-trailer system. Reported experiments demonstrate the capabilities of the proposed algorithms.

The necessity of decreasing the environmental impact of agricultural activities, while preserving in the same time the level of production to satisfy the growing population demand, requires to investigate new production tools. Mobile robotic can constitute a promising solution, since autonomous devices may permit to increase production level, while reducing pollution thanks to a high accuracy. In this paper, the use of several mobile robots for field treatment is investigated. It is here considered that they can exchange data through wireless communication, and a formation control law, accurate despite typical off-road conditions (low grip, terrain irregularities, etc), is designed relying on nonlinear observer-based adaptive control. The algorithm proposed in this paper is tested through advanced simulations in order to study separately its capabilities, as well as experimentally validated.

This paper addresses the problem of sliding parameter estimation and lateral control of an off-road vehicle-trailer system. The aim is to accurately guide the position of the trailer with respect to a planned trajectory, whatever ground conditions and trajectory shape. Relevant sliding parameter estimation is first proposed, based on the kinematic model of the system extended with side slip angles. Then, a vehicle steering control algorithm is presented to move away the vehicle from the reference trajectory in order for the trailer to achieve accurate path tracking. Reported experiments demonstrate the capabilities of the proposed algorithms.

This paper is focused on the problem of accurate and reliable path tracking control of a 4-wheels car-like mobile robot moving off-road at high speed. Dynamic and extended kinematic models that take into account the effects of wheel skidding are presented. Based on the extended kinematic model, an adaptive and predictive controller for path tracking is derived. This control law is combined to a stabilization algorithm of yaw motion, based on the dynamic model and the modulation of driven wheel forces. The overall control architecture is experimentally evaluated on a slipping terrain. Results demonstrate enhanced performances as the robot succeed in following the path at high speed, accurately and without loss of control.

The active devices dedicated to on-road vehicle stability cannot be applied satisfactorily in an off-road context, since the variability and the non-linear features of grip conditions can no longer be neglected. Specific solutions have then to be investigated. In this paper, the prevention of light all-terrain vehicle (ATV) rollover is addressed. First, a backstepping observer is designed in order to estimate online a rollover indicator accounting for sliding phenomena, from a low-cost perception system. Next, the maximum vehicle velocity, compatible with a safe motion over some horizon of prediction, is computed via predictive functional control (PFC), and can then be applied, if needed, to the vehicle actuator to prevent from rollover. The capabilities of the proposed device are demonstrated and discussed thanks to an advanced simulation testbed that has proved to supply results very close to experimental ones.

This paper addresses the problem of path generation and motion control for the autonomous maneuvers of a farm vehicle in headland. A reverse turn planner is firstly investigated, based on primitives connected together to easily generate the reference motion. Then, both steering and speed control algorithms are presented to accurately guide the vehicle. They are based on a kinematic model extended with additional sliding parameters and on model predictive control approaches. Real world experiments have been carried out on a low adherent terrain with an experimental mobile robot. At the end of each row, the reverse turn is automatically generated to connect the next reference track, and the maneuver is autonomously performed by the vehicle. Reported experiments demonstrate the capabilities of the proposed algorithms.

Accurate control of high-speed mobile robots moving off-road constitutes a challenging robotic issue: numerous time-varying dynamic phenomena (and first of all, sliding effects) are no longer negligible and must explicitly be taken into account in control design, in order to ensure high accuracy path tracking. Since these phenomena are hardly measurable at a reasonable cost, they have to be estimated on-line. A multi-model based observer is here proposed, in order to supply on-line tire cornering stiffnesses (i.e. grip conditions) as well as mobile robot sideslip angles. It takes part of the complementarity between kinematic and dynamic mobile robot models, in order to significantly decrease the number of required robot inertial parameters (since their values are sometimes difficult to obtain). Full scale experiments demonstrate that the proposed observer can supply reactive and reliable sideslip angle estimates, so that high accuracy path tracking can still be achieved, whatever grip conditions and vehicle velocity.

Control of robotic systems subject to friction phenomena is an important issue since growing demands on accuracy require elimination of friction disturbances. If several models-e.g., friction model, rigid-body dynamics-are required to describe the behavior with high precision, each model requires the knowledge of numerous parameters (perhaps time-varying) as well as an increased number of signals and sensors. In order to tackle this double limitation, an observer is proposed, addressing both the problem of velocity reconstruction and friction estimation in the joint of an inverted pendulum. Firstly, an adaptive two-level velocity observer is defined to reconstruct relevant unmeasured velocities, using estimation of the friction model error. Secondly, the observer is exploited for model-based friction compensation. Capabilities of the algorithm proposed are demonstrated by means of experiments on the Furuta pendulum.

Lateral rollover is the leading cause of fatal accidents in light all-terrain vehicles (e.g. quad bikes), especially in the agricultural area. The estimation and prediction of hazardous situations are preliminary steps in the design of active security devices. If numerous metrics have already been defined for on-road vehicles, few approaches are suitable for fast motions in a natural environment (mainly due to tire/ground contact specificity and variability). This paper proposes an algorithm dedicated to the estimation and prediction of one metric, namely lateral load transfer (LLT), in order to anticipate rollover situations on an irregular and natural ground. It is based on a vehicle dynamic model, used jointly with a backstepping observer. It allows to take into account tire/ground contact nonlinearities and variability, which impact the rollover tendency. The efficiency of the metric is investigated through advanced simulations and full scale experiments on a Kymco quad bike.

In this paper, automatic path tracking of a four-wheel-steering vehicle in presence of sliding is addressed. The attractive feature of such a steering system is that, despite of sliding phenomena, both lateral and angular deviations can be explicitly controlled. Indeed, previous research has demonstrated that high-precision path tracking on a low grip terrain can be achieved with two-wheel-steering vehicles. However, in this case, only the lateral deviation is kept satisfactorily close to zero, the angular deviation is non null in order to compensate for sliding effects. In this paper, previous adaptive control laws are extended to the case of four-wheel-steering mobile robots with the aim to servo both lateral and angular deviations. Relying on an extended kinematic model, a backstepping control approach, which considers successively front and rear steering control, has been designed. Real world experiments have been carried out on a low adherent terrain with a four-wheel-steering vehicle equipped with a single RTK-GPS. This demonstrates the capabilities of the proposed control law and its robustness in real all-terrain conditions.

The lateral rollover of quad bikes represents a significant part of severe accidents in the field of agricultural work. The specificities of such vehicles (small wheelbase, track and weight, as well as high speed), together with the terrain configuration (off-road environment) prevent from describing rollover occurrence as it is proposed for car-like vehicles. In particular, sliding effects significantly affects the evaluation of the rollover risk. This paper proposes a rollover risk indicator dedicated to off-road vehicles, taking into account the environment properties and more particularly the grip condition and its variation. It is based on the prediction of the lateral load transfer relying on vehicles models including sliding effects. This indicator can be run on-line when the vehicle is moving. It allows anticipating a potential danger, and could then be used to design security systems. Performances of this indicator are demonstrated using the multibody dynamic simulation software Adams.

Most of active devices focused on vehicle stability concerns on-road cars and cannot be applied satisfactorily in an off-road context, since the variability and the non-linearities of the tire/ground contact are often neglected. In previous work, a rollover indicator devoted to light ATVs, accounting for these phenomena has been proposed. It is based on the prediction of the lateral load transfer. Such an indicator requires the online knowledge of the tire cornering stiffness, initially selected from a ground classes network. In this paper, an adapted backstepping observer, making only use of yaw rate measurement, is designed to improve specifically tire cornering stiffness estimation. Capabilities of such an observer are demonstrated and discussed through both advanced simulations and actual experiments.

Automatic devices dedicated to vehicle guidance in off-road conditions are necessarily confronted with sliding phenomenon, since it may considerably damage the accuracy of the following task. Control laws taking explicitly into account such a phenomenon have already been designed in previous work. They can actually improve the guidance accuracy. However their efficiency is highly dependent on the sliding parameters estimation (since these parameters cannot be provided by a direct measurement). In this paper, an observer-like estimator is designed, providing sideslip angles from a single exteroceptive sensor, namely a real time kinematic GPS (RTK-GPS). Improvements in guidance accuracy, with respect to previous estimation approaches, is demonstrated through full scale experiments, addressing agricultural applications

High-precision autofarming is rapidly becoming a reality with the requirements of agricultural applications. Lots of research works have been focused on the automatic guidance control of farm vehicles, satisfactory results have been reported under the assumption that vehicles move without sliding. But unfortunately the pure rolling constraints are not always satisfied especially in agriculture applications where the working conditions are rough and not expectable. In this paper the problem of path following control of autonomous farm vehicles in presence of sliding is addressed. To take sliding effects into account, a vehicle-oriented kinematic model is constructed in which sliding effects are introduced as additive unknown parameters of the ideal kinematic model. Based on backstepping method a stepwise procedure is proposed to design an adaptive controller in which time-invariant sliding effects are learned and compensated by parameter adaptations. It is theoretically proven that for the farm vehicles subject to sliding, the lateral deviation can be stabilized near zero and the orientation errors converge into a neighborhood near the origin. To be more robust to disturbances including external noises and unmodeled time-varying sliding components, the adaptive controller is refined by integrating Variable Structure Controllers (VSC) or projection mappings. Simulation results show that the proposed robust adaptive controllers can reject sliding effects and guarantee high path-following accuracy.

One of the major current developments in agricultural machinery aims at providing farm vehicles with automatic guidance capabilities. With respect to standard mobile robots applications, two additional difficulties have to be addressed: firstly, since farm vehicles operate on fields, sliding phenomena inevitably occurs. Secondly, due to large inertia of these vehicles, small delays introduced by low-level actuators may have noticeable effects. These two phenomena may lower considerably the accuracy of path following control laws. In this paper, a vehicle extended kinematic model is first built in order to account for sliding phenomena. These latter effects are then taken into account within guidance laws, relying upon nonlinear control techniques. Finally, a Model Predictive Control strategy is developed to reduce the effects induced by actuation delays and vehicle large inertia. Capabilities of this control scheme is demonstrated via full scale experiments carried out with a farm tractor, whose realtime localization is achieved relying uniquely upon a RTK GPS sensor.

In automatic guidance of agriculture vehicles, lateral control is not the only requirement. Lots of research works have been focused on trajectory tracking control which can provide high longitudinal-lateral control accuracy. Satisfactory results have been reported as soon as vehicles move without sliding. But unfortunately pure rolling constraints are not always satisfied especially in agriculture applications where working conditions are rough and not expectable. In this paper the problem of trajectory tracking control of autonomous farm vehicles in presence of sliding is addressed. To take sliding effects into account, two variables which characterize sliding effects are introduced into the kinematic model based on geometric and velocity constrains in presence of sliding. With linearization approximation a refined kinematic model is obtained in which sliding appears as additive unknown parameters to the ideal kinematic model. By integrating parameter adaptation technique with backstepping method, a stepwise procedure is proposed to design a robust adaptive controller. It is theoretically proven that for the farm vehicles subjected to sliding, the longitudinal-lateral deviations can be stabilized near zero and the orientation errors converge into a neighborhood near the origin. To be more realistic for agriculture applications, an adaptive controller with projection mapping is also proposed. Simulation results show that the proposed (robust) adaptive controllers can guarantee high trajectory tracking accuracy regardless of sliding.

Since Global Navigation Satellite systems are able to supply very accurate coordinates of a point (about 2 cm with a RTK GPS), such a sensor is very suitable to design vehicle guidance system. It is especially the case in agricultural tasks where a centimeter precision is often required (seeding, spraying,...). To answer to growing high precision agriculture principle demand, several control laws for automated vehicle guidance relying on this sensor have been developed. Such guidance systems are able to supply an acceptable steering accuracy as long as vehicle does not slide (path tracking on even ground with good adherence properties...), what alas inevitably occurs in agricultural tasks. Several principles are here presented to steer vehicle whatever properties of ground and path to be followed are. In this paper a new extended kinematic model with sliding accounted is presented which allows describing vehicle dynamics in all guidance conditions. Via this model a new nonlinear control law can be designed, which integrates sliding effects. Its capabilities are investigated through simulations and experimental tests.

When designing an accurate automated guidance vehicle system, a major problem is sliding and pseudo-sliding effects. It is especially the case in agricultural applications, where a five centimeters accuracy with respect to the desired trajectory is required, even if vehicles move on a slippery ground. Previous works have established that RTK GPS was a very suitable sensor to achieve automated guidance with such a high precision: several control laws have been designed for vehicles equipped with that sensor, and provide the expected guidance accuracy as long as vehicles do not slide. Further control developments have been previously proposed to take sliding into account: guidance accuracy in slippery environment has been shown to be preserved, except transiently at beginning/end of curves. In this paper, design of such a control law is first recalled and discussed. Model predictive control method is then applied in order to preserve guidance accuracy even during these transitions. Finally, the global control scheme is implemented, and improvements with respect to previous guidance laws are demonstrated through full-scale experiments.

Numerous agricultural applications require very accurate guidance of farm vehicles. Current works have established that RTK GPS was a very suitable sensor in order to meet the expected precision: several control laws have been designed for vehicles equipped with such a sensor, and satisfactory results have been achieved as long as vehicles do not slide. Nevertheless, in actual working conditions (sloping fields, entering into curves on a wet land, etc.), sliding inevitably occurs. In this paper, we design a nonlinear adaptive control law in order to preserve guidance precision in presence of sliding: realtime sliding estimation is used to correct vehicle evolution. Field experiments, demonstrating the capabilities of that control scheme are reported and discussed.

A very accurate vehicle guidance is required in numerous agricultural applications, as seeding, spraying, row cropping,... Accuracy in vehicle localization can be obtained in realtime from a RTK GPS sensor. Several control laws, relying on this sensor, have been previously designed and provide satisfactory results as long as vehicles do not slide. However, sliding has to occur in agricultural tasks (sloping fields, curves on a wet land, ...). The challenge addressed in this paper is to preserve vehicle guidance accuracy in such situations. A nonlinear adaptive control law is here designed. Simulation results and field experiments, demonstrating the capabilities of that control scheme, are reported and discussed.