Workspace-Based Motion Planning for Quadrupedal Robots on Rough Terrain

Abstract

Legged robots have demonstrated high potential when dealing with rough terrain, for which an efficient motion planner becomes crucial. This article presents a novel approach for quadrupedal robot motion planning on rough terrain that is both conceptually straightforward and computationally efficient. Implementing the concept of workspace constitutes the cornerstone of this method: both body poses and swing-leg footholds are chosen within their corresponding workspace. A novel approach called the “cross-diagonal method” is developed to facilitate the search for new body poses. Based on the obtained body pose, the foothold for a swing leg selected within its foot workspace satisfies the reachability constraint automatically. The proposed motion planning scheme is integrated with an elevation mapping module and a state estimation module, enabling quadrupedal robots to travel through uneven terrains with high efficiency. The significance of this work is validated through simulation and physical experiments with a quadrupedal robot, which achieves high success rates in overcoming difficult terrains without prior knowledge of the environment. This approach offers the advantages of high computational efficiency, simplicity, and adaptability to different types of terrain, making it a promising solution for real-world applications.