Mobile robot control on uneven and slippery ground: An adaptive approach based on a multi-model observer

International audienceThis 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 in 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

Méthode de fusion multi-capteurs pour des opérations de suivi de lignes de culture et de traversabilité

Conférence AXEMA-EURAGENG 2017, Paris, FRA, 25-/02/2017 - 25/02/2017International audiencePrecision agriculture vehicles need autonomous navigation in cultures to carry out their tasks, such as planting, maintenance and harvesting in cultures such as vegetable, vineyard, or horticulture. The detection of natural objects like trunks, grass, leaf, or obstacles in front of vehicle in crop row is crucial for safe navigation. Sensors such as LiDAR devices or Time Of Flight cameras (TOF), allow to obtain geometric data in natural environment, using information of an Inertial Measurement Unit (IMU), for measurement accuracy. Fusion of geometric information with a color camera data improves the natural object identification, using some color classification technique, such as Support Vector Machine (SVM), considering two object classes, either solid objects such as crop or tree branch, and other elements like grass, leaf and soil. Agricultural vehicles can use these geometric and colorimetric data in real time, to follow crop rows and detect obstacles while executing various precision agriculture operations. In this application, perception sensors embedded on a light mobile robot were used to detect and identify natural objects in agricultural crops, working in various fields, with or without soil perturbation, with different speeds and several vegetation levels to achieve crop row tracking tasks, from a desired lateral deviation between robot and crop line, or traversability operations which consisted to take a decision in vehicle navigation, according to the size and nature of the detected objects, in front of vehicle. The vehicle could cross or avoid the object, or it must stop, for big solid obstacles

Preserving stability of huge agriculture machines with internal mobilities: Application to a grape harvester

International audienceThis paper proposes an algorithm for estimating on-line the rollover risk of huge machine moving on natural ground. The approach is based on the reconstruction of lateral load transfer thanks to an observer, able to take into account terrain specificities (grip conditions and geometry). Capabilities are tested through experiments on a grape harvester

Off-road mobile robot control: An adaptive approach for accuracy and integrity

International audienceThis paper proposes an algorithm dedicated to the control of off-road mobile robots at high speed. Based on adaptive and predictive principles, it first proposes a control law to preserve a high level of accuracy in the path tracking problem. Next, the dynamic model used for grip condition estimation is considered to address also robot integrity preservation thanks to the velocity limitation

Rollover prevention system dedicated to ATVs on natural ground

In this paper, an algorithm dedicated to light ATVs, which estimates and anticipates the rollover, is proposed. It is based on the on-line estimation of the Lateral Load Transfer (LLT), allowing the evaluation of dynamic instabilities. The LLT is computed thanks to a dynamical model split into two 2D projections. Relying on this representation and a low cost perception system, an observer is proposed to estimate on-line the terrain properties (grip conditions and slope), then allowing to deduce accurately the risk of instability. Associated to a predictive control algorithm, based on the extrapolation of riders action, the risk can be anticipated, enabling to warn the pilot and to consider the implementation of active actions

High-speed mobile robot control in off-road conditions: a multi-model based adaptive approach

International audienceThis 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

On-line estimation of a stability metric including grip conditions and slope: Application to rollover prevention for all-terrain vehicles

International audienceRollover 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

Dual back-stepping observer to anticipate the rollover risk in under/over-steering situations. Application to ATVs in off-road context

International audienceIn this paper an ATV (All-Terrain Vehicle) rollover prevention system is proposed. It is based on the online estimation and prediction of the Lateral Load Transfer (LLT), allowing the evaluation of dynamic instabilities. Using a vehicle model based on two 2D representations, the LLT can be estimated and predicted. As we consider off road vehicle, grip conditions must be encountered and are here estimated thanks to observation theory. Nevertheless, two main behaviours (over/under-steering) may be encountered pending on grip, and vehicle configuration. Because of the low cost sensor, these two opposite dynamics cannot be explicitly discriminated. As a result, two observers are used according to the vehicle behaviour. Based on a bicycle model and a low cost perception system, they estimate on-line the terrain properties (grip conditions, global sideslip angle and bank angle). A "supervisor" selects on-line the right observer. Associated to a predictive control algorithm, based on the extrapolation of rider's action and the selected estimated dynamical state, the risk can be anticipated, enabling to warn the pilot and to consider the implementation of active actions. Simulations and full-scale experimentations are presented to discuss about the efficiency of the proposed solution

Algorithmes pour la commande d’une formation de robots mobiles

International audienceLa nécessité de diminuer l'impact sur l'environnement des activités agricoles, tout en préservant le niveau de production pour satisfaire les demandes de population en croissance, exige l'étude de nouveaux outils de production. Des robots mobiles peuvent constituer une solution prometteuse, puisque des dispositifs autonomes peuvent permettre d'accroître les niveaux de production, en préservant l'environnement en raison de leur grande précision. Si le contrôle automatique d'une machine seule offre des améliorations significatives, le contrôle d'une flotte modulaire de véhicules permet d'augmenter les espaces couverts sans utiliser de grandes machines qui provoquent un important compactage du sol. Pour être efficace, le contrôle de plusieurs robots autonomes doit être très précis et capable de gérer les différentes configurations rencontrées pendant une tâche agricole. Pour réaliser ces objectifs, les lois de contrôle simples utilisées dans le contexte routier ne sont pas appropriées dans des conditions tout terrain. Dans cet article, la commande en formation de plusieurs robots est envisagée dans le cadre du suivi de trajectoire. On propose des lois de commande adaptatives et prédictives qui prennent en compte les faibles conditions d'adhérence pour préserver l'exactitude de positionnement global et relatif. Elle permet alors de gérer une formation dans l'environnement naturel, indépendamment des conditions du sol, de la trajectoire à suivre et de la forme choisie pour la formation. Dans une première partie, on considère le modèle du mouvement de formation, en tenant compte des faibles conditions d'adhérence. Dans une deuxième partie, un observateur dédié à l'évaluation en ligne des conditions d'adhérences est conçu. Il permet de calculer les variables manquantes exigées pour un contrôle précis. Finalement, en se référant au modèle et à l'observateur, des lois de commande pour l'asservissement latéral et angulaire sont proposées. Il est basé sur une erreur mélangeant des écarts relatifs et absolus et permet la gestion du contrôle adaptatif. Les capacités de l'approche développée sont alors examinées à travers des expériences à échelle réelle

Automatic guidance of an off-road mobile robot with a trailer: Application to the control of agricultural passive towed implements

International audienceThis paper presents the study of both steering and speed control algorithms of an off-road mobile robot in order to accurately guide, forward or backward, the position of its trailer with respect to a planned trajectory, whatever ground conditions and trajectory shape. The proposed algorithms are based on an extended kinematic model of the system, accounting for sliding effects with additional sliding parameters. An observer is developed to obtain a relevant on-line estimation of these parameters. An original steering control algorithm is then proposed, considering the implement as an independent virtual vehicle: A first control law calculates the direction of the linear velocity vector at the hitch point that would ensure the convergence of this virtual vehicle to the planned trajectory. Next, a reference angle between the tractor and the implement leading to such a velocity vector is inferred, and finally a second control law is designed to stabilize the actual tractor implement angle on this reference angle. The capabilities of the proposed algorithms are investigated through full-scale experiments