In this paper, we present a risk-aware motion planning framework for resilient navigation in occupancy grid maps. Navigating in unknown environments involves complex interactions with the surroundings that must be effectively managed by the robot. Recently, these interactions are frequently expressed as risk constraints, where risk is defined as potential threats that could hinder the robot from accomplishing its objectives. However, when the risk constraint involves nonlinearities, common numerical solvers are severely hampered in finding a feasible solution and are prone to failure. Therefore, we present a novel risk-aware navigation strategy based on motion primitives and the Nonlinear Model Predictive Control (NMPC) method to address nonlinear risk constraints within discrete maps. We demonstrate the effectiveness of our approach through a practical application of a robust risk assessment method that takes into account both the state of the environment and the state of the robot. In addition to enhancing the decision-making capabilities of the robot, our framework offers a more resilient motion planning process that enables the robot to navigate risky scenarios where standard optimizers are likely to fail and lead to dangerous trajectories.

General purpose simulators provide cheap training data to learn complex robotic skills. However, the transition from simulation to reality is often very challenging for the agent. One major issue is the delay on the physical robot that may deteriorate the performance of the deployed agent. Furthermore, once a successfully trained learning-based control policy is available, re-purposing the knowledge acquired by the agent to enable a structurally distinct agent to perform the same task is hazardous if done naively. In this work, we address the above issues with a single method, the DA-UNN (Delay Aware Universal Notice Network), which decomposes the knowledge into robot-specific and task-specific modules for fast transfer. Our framework deals with delays immanent to physical systems in order to improve sim2real transfer. We evaluate the efficiency of our approach using simulated and actual robots on a dynamic manipulation task where delay management is crucial.

International audience

International audience

Being able to learn and transfer skills from one agent to another is a fundamental feature in constructing even more intelligent behaviors. In this paper, we introduce a new kind of architecture and information pipeline that aims to enable the transmission of skills from one robot to one or several others. The Universal Notice Network (UNN) originality lies in the fact that it clearly distinguishes knowledge necessary to solve the task from the agent intrinsic perceptions and capabilities, hence increasing its reusability and its potential transmission to other agents. In various experiments, focusing on manipulation and comanipulation tasks in original environments, we demonstrate the capabilities of the proposed method that takes advantage of reinforcement learning algorithms and domain knowledge, such as forward geometric model and inverse kinematics. In particular, we show that a learned UNN through the interactions of an agent with its environment is transmissible to other agents, conserving a similar perfomance level.

International audience

In this paper, a multi-agent probabilistic optimization algorithm is applied to the problem of multi-vehicle coordination. The algorithm is known as “Probability Collectives” (PC) and has roots in Game Theory and Optimization theory. It is traditionally used for finding optimal solutions of NP-hard problems such as the travelling salesman problem. On the other end, the proposed PC formulation presented in this paper focuses on a minimal complexity implementation for solving the coordination problem in a time of the order of magnitude of 0.1. Besides time constraints, the emphasis in the design is put on ensuring that the algorithm always comes up with a feasible solution. Simulations show that both objectives are reached while having a decentralized algorithm, and flexible with respect to the type of situations it can deal with. Additional benefits of the PC algorithm include robustness to agent failure and the possibility to accommodate non-collaborative vehicles (market penetration of autonomous vehicles <; 100%).

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.

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.

This paper addresses platooning navigation as part of new transportation services emerging nowadays in urban areas. Platooning formation is ensured using a global decentralized control strategy supported by inter-vehicle communications. A large motion flexibility is achieved according to a manual guidance mode, i.e. the path to follow is inferred online from the motion of the manually driven first vehicle. For this purpose, a visual SLAM algorithm that relies on monocular vision is run on the lead vehicle and coupled with a trajectory creation procedure. Both the map and trajectory updates are shared online with the following vehicles and permit them to derive their absolute location with respect to a common reference trajectory from their current camera image. Full-scale experiments with two urban vehicles demonstrate the performance of the proposed approach.

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.

This paper deals with platooning navigation in the context of innovative solutions for urban transportation systems. More precisely, the case of a manually driven vehicle leading a convoy of automated ones is considered. Vehicle localization relies solely on monocular vision: a 3D map of the environment is built beforehand from reference video sequences, and then used to derive vehicle absolute location from the current camera image. The 3D vision map presents however distortions w.r.t. a metric world, but these latter can be shown to be locally homogeneous. They can then be accurately corrected via a 1-dim. function evaluated with a nonlinear observer relying on odometric data. Next, the platoon reference trajectory is built as a B-Spline curve extended on-line via local optimization from the successive locations of the lead vehicle, and a global decentralized control strategy, supported by intervehicle communication, is designed to achieve accurate platooning with no oscillation within the convoy. Experimental results, carried out with two urban vehicles, demonstrate the capabilities of the proposed approach.

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.

Environment, sustainable development as well as new transportation service emergence in urban areas are major concerns. Consequently, studies are currently intended to automate electric vehicles designed for applications in free access. An additional functionality that appears very attractive is vehicle platooning. In order to avoid oscillations within the fleet when completing this task, a global control strategy, supported by inter-vehicle communications, is investigated. Vehicle absolute localization is then needed and is here derived from monocular vision. These data are however expressed in a virtual vision world, slightly distorted with respect to the actual metric one. It has previously been shown that such a distortion can accurately be corrected on-line in different ways, considering telemetric or odometric data. These strategies have here been refined in order to provide optimal corrections. A comparative study, supported by simulations and full-scale experiments, is reported to exhibit benefits and performances of proposed approaches.

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.

Automated electric vehicles for public use constitute a promising very efficient and environment-friendly “urban transportation system”. An additional functionality that could enhance this transportation service is vehicle platooning. In order to avoid inter-distance oscillations within the platoon, a global control strategy, supported by inter-vehicle communications, is investigated. Vehicle localization in an absolute frame is needed and is derived here from monocular vision. The vision data is however expressed in a virtual world, slightly distorted with respect to the actual metric one. It is shown that such a distortion can accurately be corrected by designing a nonlinear observer that relies on odometric data. A global decentralized control strategy, relying on nonlinear control techniques, can then be designed to achieve accurate vehicle platooning. Simulations and full-scale experiments demonstrate the performance of the proposed approach.

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.

Various "Urban Transportation Systems" are currently in developing, in order to put forward solutions to congestion and pollution in dense areas. Autonomous electric vehicles in free-access can be seen as an attractive approach, in view of the large flexibility that can be expected. One instrumental functionality linked to this solution is platoon motion: several autonomous vehicles accurately follow the trajectory of a manually driven first vehicle, with pre-specified inter-distances. A global decentralized platoon control strategy, supported by inter-vehicle communications and relying on nonlinear control techniques is here proposed. Each vehicle is controlled with respect to the same smooth reference trajectory, inferred on-line from the motion of the first vehicle via B-spline optimization. Experimental results, carried out with four urban vehicles, demonstrate the capabilities of the proposed approach.

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.

To address traffic saturation in cities, new ldquourban transportation systemsrdquo, based on electric vehicles in free-access, are in developing. One necessary functionality of such systems is their ability to move in a platoon fashion. A global decentralized platoon control strategy, supported by inter-vehicle communications, is addressed in this paper, relying on nonlinear control techniques. The main interest in a global approach is that servoing error accumulation can be avoided, whatever the platoon length. However, absolute vehicle localization is then required. In urban applications, cameras are realistic sensors, but localization is supplied in a 3D visual virtual world, slightly distorted w.r.t. the actual metric one. To enable accurate guidance, local corrections to the visual world are here computed from the data supplied by a laser rangefinder mounted on the second vehicle, and then shared with the whole platoon. Full-scale experiments demonstrate the performance of the proposed approach.

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

In order to solve problems of traffic saturation in cities, new alternative” Urban Transportation Systems” are based on electric vehicles in free-access. One necessary functionality of such systems is their ability to move in a platoon fashion. Platooning of these automatic guided vehicles, relying on RTK-GPS sensors and inter-vehicles communication, is addressed in this paper. More precisely, vehicles platoon is expected to follow a curved reference path. Relying on nonlinear control theory, lateral and longitudinal control are fully decoupled, and therefore addressed independently. To ensure passengers comfort, additional monitoring functions supervise our control system. Then, simulations followed by experiments carried out with urban vehicles, are presented.

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.

To solve problems of traffic saturation in cities, new alternative ''urban transportation systems" are based on electric vehicles in free-access. One necessary functionality of such systems is their ability to move in a platoon fashion. Platooning of these automatic guided vehicles, relying on RTK-GPS sensors and inter-vehicles communication, is addressed in this paper. The developed control law is based on a global control strategy; actually, it can take into account all the platoon state, and not only the immediate previous vehicle state. Distance here is understood as difference of curvilinear abscissa along a reference trajectory. Relying on nonlinear control theory, lateral and longitudinal control are fully decoupled, and therefore addressed independently. To ensure passengers comfort, additional monitoring functions supervise our control system. Then, experiment, carried out with urban vehicles, and simulations of long platoon, are presented.

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.

In this paper, a complete system for outdoor robot navigation is presented. It uses only monocular vision. The robot is first guided on a path by a human. During this learning step, the robot records a video sequence. From this sequence, a three dimensional map of the trajectory and the environment is built. When this map has been computed, the robot is able to follow the same trajectory by itself. Experimental results carried out with an urban electric vehicle are shown and compared to the ground truth.

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.

Visual servoing requires an object in the field of view of the camera, in order to control the robot evolution. Otherwise, the virtual link is broken and the control loop cannot continue to be closed. In this paper, a novel approach is presented in order to guarantee that the object remains in the field of view of the camera during the whole robot motion. It consists in tracking an iteratively computed trajectory. A position based modeling adapted to a moving target object is established, and is used to control the trajectory. A nonlinear decoupling approach is then used to control the robot. Experiments, demonstrating the capabilities of this approach, have been conducted on a Cartesian robot connected to a real time vision system, with a CCD camera mounted on the end effector of the robot.

Precision agriculture involves very accurate farm vehicle control along recorded paths, which are not necessarily straight lines. We investigate the possibility of achieving this task with CP-DGPS (carrier phase differential GPS) as the unique sensor. The vehicle heading is derived according to a Kalman state reconstructor, and a nonlinear velocity independent control law is designed, relying on chained systems properties. Field experiments, demonstrating the capabilities of our guidance system, are reported and discussed.

This paper proposes an analysis of the behavior of perhaps the simplest biped robot: the compass gait model. It has been shown previously that such a robot can walk down a slope indefinitely without any actuation. Passive motions of this nature are of particular interest since they may lead us to strategies for controlling active walking machines as well as to a better understanding of human locomotion. We show here that, depending on the parameters of the system, passive compass gait may exhibit 1-periodic, 2/sup n/-periodic and chaotic gaits proceeding from cascades of period-doubling bifurcations. Since compass equations are quite involved (they combine nonlinear differential and algebraic equations in a 4-dimensional space), our investigations rely, in part, on numerical simulations.

This paper deals with state feedback control of nonholonomic systems whose configuration space presents some singular points. The whole study is driven on the example of mobile robots equipped with several steering wheels. Linearizing dynamic feedback and time-varying feedback are successively considered to handle the tracking of a moving reference trajectory and the stabilization of the robots to a rest configuration, respectively. Both laws are designed in such a way that the systems singular configurations cannot definitely be met. Finally, a commutation procedure between the two laws is presented, in order to handle the very natural problem of tracking a reference trajectory ended by a rest configuration.

A hybrid strategy for the control of nonholonomic mobile robots is proposed and assessed, using both theoretical arguments and simulation experiments. The control law is a discontinuous state feedback that combines the advantages of time-invariant smooth feedback control far from the target and of time-varying smooth feedback control close to the target, while avoiding their respective drawbacks.>