This article focuses on the design of a robust model predictive control law for constrained discrete-time time-varying systems with additive uncertainties. The proposed solution to the control problem is a tube-based MPC ensuring robustness and constraints fulfilment. Reachable sets are calculated online taking into account the system dynamics by means of an adaptive local control law and additive uncertainties. The proposed method represents a trade-off between small conservativeness and efficient real-time execution. This approach is applied to solve the trajectory tracking problem of a mobile robot. Simulation results provide a comparison between the tube-based MPC scheme and established motion control algorithms, showing the efficient execution and satisfactory behaviour of the proposed controller.

SUMMARY: In this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.

This paper presents a control law for the trajectory tracking of mobile robots under slip conditions and subject to both system constraints and varying dynamics. First, a control law is obtained based on a Lyapunov function to guarantee closed-loop asymptotic stability, resulting in a set of feedback gains, one for each extreme model realization. On-line computation is devoted to determine an adaptive feedback control law for the current realization of the state as a convex combination of the gains previously obtained. Simulations comparing the proposed control law with other strategies under slip conditions are provided. They show the satisfactory behavior of the proposed control strategy.

This manuscript evaluates through physical experiments a navigation architecture for off-road mobile robots. It has been implemented taking into account low-cost components and computational requirements while still being able to robustly address the required tasks. The visual odometry approach uses two consumer-grade cameras for estimating robot displacement and orientation (visual compass). One of these cameras is also used for slip estimation. The control scheme updates online the feedback gains taking into account the current slip. Furthermore, the setpoints are sent to two low-level controllers, PI controllers with anti-windup, properly tuned. In order to validate the proposed architecture, we have employed a tracked mobile robot in off-road conditions, gravel terrains, with a slip around 5%. The selected physical experiments show the right integration of the localization layer (visual odometry with visual compass) and the control layer with the slip compensation controller and the appropriate computation time (lesser than 0.2 s are employed at each sampling instant).