Trajectory Optimization for Hybrid Wheeled-Legged Robots in Challenging Terrain

Abstract

Wheeled-legged robots are a promising solution for agile locomotion in challenging terrain, combining the speed of the wheels with the ability of the legs to cope with unstructured environments. This paper presents a trajectory optimization framework that allows wheeled-legged robots to navigate in challenging terrain, e.g., steps, slopes, gaps, while negotiating these obstacles with dynamic motions. The framework generates the robot’s base motion as well as the wheels’ positions and contact forces along the trajectory, accounting for the terrain map and the dynamics of the robot. The knowledge of the terrain map allows the optimizer to generate feasible motions for obstacle negotiation in a dynamic manner, at higher speeds. To take full advantage of the hybrid nature of wheeled-legged robots, driving and stepping motions are both considered in a single planning problem that can generate trajectories with purely driving motions or hybrid driving-stepping motions. The optimization is formulated as a Nonlinear Programming Problem (NLP) employing a phase-based parametrization to optimize over the wheels’ motion and contact forces. The reference trajectories are tracked by a hierarchical whole-body controller that computes the torque actuation commands for the robot. The motion plans are verified on the quadrupedal robot ANYmal equipped with non-steerable torque-controlled wheels in simulations and experimental tests. Agile hybrid motions are demonstrated in simulations with discontinuous obstacles, such as floating steps and gaps, at an average speed of 0.75 m/s.