Actively articulated locomotion systems such as hybrid wheel-legged vehicles are a possible way to enhance the locomotion performance of an autonomous mobile robot. In this paper, we address the control of the wheel-legged robot Hylos traveling on irregular sloping terrain. The redundancy of such a system is used to optimize both the balance of traction forces and the tipover stability. The general formulation of this optimization problem is presented, and a suboptimal but computationally efficient solution is proposed. Then, an algorithm to control the robot posture, based on a velocity model, is described. Finally, this algorithm is validated through simulations and experiments that show the capabilities of such a redundantly actuated vehicle to enhance its own safety and autonomy in critical environments.

Abstract. 200 words A biologically-inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e.g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulation. But the capacity of the model to work on a real robot platform had not been tested.This article presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cueguided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment -recognized as new contexts -, and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performances and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such brain-inspired meta-controller may provide an advancement for learning architectures in robotics.A biologically inspired meta-control navigation system for the Psikharpax rat robot 2