Roland Lenain

High accuracy path tracking for vehicles in presence of sliding: Application to farm vehicle automatic guidance for agricultural tasks

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

Model Predictive Control for Vehicle Guidance in Presence of Sliding: Application to Farm Vehicles Path Tracking

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.

Adaptive control for car like vehicles guidance relying on RTK GPS: rejection of sliding effects in agricultural applications

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.

Adaptable Robot Formation Control: Adaptive and Predictive Formation Control of Autonomous Vehicles

The ability to use cooperative small vehicles is of interest in many applications. From material transportation to farming operations, the use of small machines achieving small tasks, but able to work together to complete larger tasks, permits us to rely on a unique kind of vehicle. To be efficient, such a point of view requires the vehicles to be, at least partially, autonomous and their motion must be accurately coordinated for the tasks to be properly achieved. This article proposes a control framework dedicated to the accurate control of a fleet of mobile robots operating in formation. Decentralized control relying on interrobot communication has been favored. To ensure a high relative positioning, adaptive and predictive control techniques are considered, allowing us to account for the influence of several phenomena (such as dynamic perturbations or bad grip conditions) depreciating the relevance of classical approaches based on ideal robots and ideal contact conditions assumptions.

Robust Adaptive Control of Automatic Guidance of Farm Vehicles in the Presence of Sliding

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.

A rollover indicator based on the prediction of the load transfer in presence of sliding: application to an All Terrain Vehicle

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.

Adaptive and Predictive Path Tracking Control for Off-road Mobile Robots

A major problem in the design of control laws dedicated to mobile robots appears when the classical hypothesis of rolling without sliding wheels is violated. It is generally the case for off-road vehicles as adherence conditions are often not satisfactory and sliding can then cease to be negligible. Consequently theoretical performance is impaired and the vehicle is no longer accurately controlled. It is particularly harmful with respect to path tracking tasks, where a loss of accuracy in rough terrain can generate a hazardous situation. Previous work based on the assumption of rolling without sliding has shown very satisfactory results with respect to that task when sliding is not preponderant. It has also made it possible to pinpoint and study the effects of sliding when it appears to be non-negligible. To preserve path tracking accuracy with respect to this phenomenon, a new control law based on an extended kinematic model (updated on-line via an adaptive method) is proposed and discussed. Such control is very efficient when adherence conditions are constant, but overshoots can appear when an abrupt variation is recorded (which is especially the case at the beginning/ end of curves due to low level delays and inertial effects). A model predictive control approach is then added to limit such transient phases in cases where a curved path is followed. The paper is organized as follows: the extended kinematic model is presented as well as the observation of unmeasured parameters required to feed it. A nonlinear control law can then be designed and the results obtained are discussed. Finally, the model predictive control approach is integrated and the overall control scheme is presented. The capabilities of the approach described in this paper are then discussed through full scale experiments.

Trajectory tracking control of farm vehicles in presence of sliding

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.

Sideslip angles observer for vehicle guidance in sliding conditions: application to agricultural path tracking tasks

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

A rollover indicator based on a tire stiffness backstepping observer: Application to an All-Terrain Vehicle

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.