Obstacle-Aided Trajectory Control of a Quadrupedal Robot Through Sequential Gait Composition

Modeling and controlling legged robot locomotion on terrains with densely distributed large rocks and boulders are fundamentally challenging. Unlike traditional methods, which often consider these rocks and boulders as obstacles and attempt to find a clear path to circumvent them, in this study, we aim to develop methods for robots to actively utilize interaction forces with these “obstacles” for locomotion and navigation. To do so, we studied the locomotion of a quadrupedal robot as it traversed a simplified obstacle field with 12 different gaits and discovered that with each gait, the robot could passively converge to a distinct orientation. A compositional return map explained this observed passive convergence and enabled prediction of the steady-state orientation angles for each quadrupedal gait. We experimentally demonstrated that with these predictions, a legged robot could effectively generate the desired shape of trajectories among large, slippery obstacles, simply by switching between different gaits. Our study offered a novel method for robots to exploit traditionally-considered “obstacles” to achieve agile movements on challenging terrains.