查看文章

In this paper we develop an RRT-based motion planner that achieved bounding in simulation with the LittleDog robot over extremely rough terrain. LittleDog is a quadruped robot that has 12 actuators, and a 36-dimensional state space; the task of bounding involves differential contstraints due to underactuation and motor limits, which makes motion planning extremely challenging. Rapidly-exploring Random Trees (RRTs) are well known for fast kinematic path planning in high dimensional configuration spaces in the presence of obstacles, but the performance of the basic RRT algorithm rapidly degrades with addition of differential constraints and increasing dimensionality. To speed up the planning we modified the basic RRT algorithm by (1) biasing the search in task space,(2) implementing the Reachability-Guided RRT, which dynamically changes the sampling region, and (3) by implementing a motion primitive which reduces the dimensionality of the problem. With these modifications, the planning algorithm succesfully generated plans over very rough terrain. Short trajectories were demonstrated to work open-loop on a real robot.