Fast Global Motion Planning for Dynamic Legged Robots

Abstract

This work presents a motion planning algorithm for legged robots capable of constructing long-horizon dynamic plans in real-time. Many existing methods use models that prohibit flight phases or even require static stability, while those that permit these dynamics often plan over short horizons or take minutes to compute. The algorithm presented here resolves these issues through a reduced-order dynamical model that handles motion primitives with stance and flight phases and supports an RRT-Connect framework for rapid exploration. Kinematic and dynamic constraint approximations are computed efficiently and validated with a whole-body trajectory optimization. The algorithm is tested over challenging terrain requiring long planning horizons and dynamic motions in seconds - an order of magnitude faster than existing methods. The speed and global nature of the planner offer a new level of autonomy for legged robot applications.