Traversing soft soils represents a major concern of planetary rover missions. In this paper, we present a new chassis mechanism capable of a crawling gait that enhances trafficability on soft soil while relying on as few actuators as possible. Articulated by two actuated joints, MARCEL is a four-wheeled rover chassis which name stands for Mobile Active Rover Chassis for Enhanced Locomotion. MARCEL's crawling leverages a continuous adjustment of the load distribution on the four wheels using an internal torque applied between two halves of the chassis by series elastic actuation. This allows the pressure on two wheels to be minimized while they are moving forward with the assistance of the chassis's articulated motion. As a result, the wheels can be propelled forward one pair after another while avoiding the bulldozing resistance of the sand. This crawling motion is tested experimentally and is shown to generate more drawbar pull than both rolling or using a mere “push-pull” locomotion. Its ability to extricate the rover from deep sand entrapment is also tested successfully. This will allow future missions to deal with unforeseen terrain properties or to venture in more challenging areas while minimizing design complexity.

Driving energy consumption plays a major role in the navigation of mobile robots in challenging environments, especially if they are left to operate unattended under limited on-board power. This paper reports on first results of an energy-aware path planner, which can provide estimates of the driving energy consumption and energy recovery of a robot traversing complex uneven terrains. Energy is estimated over trajectories making use of a self-supervised learning approach, in which the robot autonomously learns how to correlate perceived terrain point clouds to energy consumption and recovery. A novel feature of the method is the use of 1D convolutional neural network to analyse the terrain sequentially in the same temporal order as it would be experienced by the robot when moving. The performance of the proposed approach is assessed in simulation over several digital terrain models collected from real natural scenarios, and is compared with a heuristic inclination-based energy model. We show evidence of the benefit of our method to increase the overall prediction r2 score by 66.8% and to reduce the driving energy consumption over planned paths by 5.5%.

Generic control of wheel-on-leg robots on arbitrary uneven terrains is a challenging task due to the complexity of the robot dynamics, surface interactions, and environmental structures. This paper deals with the control of a wheel-on-leg robot with passive and active internal compliance that enables estimation of wheel-ground interaction forces. The proposed method is based on a continuous state space Q-learning approach that uses the contact forces estimates to learn, through trial and error, the appropriate control policy from a set of predefined behaviors. Without any prior knowledge of the ground geometry, the robot is able to react to unanticipated obstacles. The learned policy proves to be generic and allows the robot to negotiate complex obstacles that had not been considered during learning phase.

In this paper, we propose the use of compliant elements in the actuation of a wheel-legged robot in order to improve its locomotion properties on unknown and irregular terrains. Detection of the obstacles is achieved by a synergistic use of the structural compliances. The robot's capabilities to surmount steep obstacles is thus improved thanks to the inertia of the chassis and flexibility in postural control. In the proposed robot's kinematics, the four wheels are attached to the main body through vertical series elastic actuators (SEA) and with a passive horizontal compliant mechanism subject to a specific wheel speed control. The overall control relies on postural servoing and a local reactive loop which adapts the vertical forces applied by the SEA on each wheel according to the detected obstacle and the stability margin. The resulting system is evaluated with physical simulations for two case studies: a canonical steep obstacle on one wheel at a time and multiple random rough terrains.