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.

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.

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.