LSTP: Long Short-Term Motion Planning for Legged and Legged-Wheeled Systems

This article presents a hybrid motion planning and control approach applicable to various ground robot types and morphologies. Our two-step approach uses a sampling-based planner to compute an approximate motion, which is then fed to numerical optimization for refinement. The sampling-based stage finds a long-term global plan consisting of a contact schedule and sequence of keyframes, i.e., stable whole-body configurations. Subsequently, the optimization refines the solution with a short-term planning horizon to satisfy all nonlinear dynamics constraints. The proposed hybrid planner can compute plans for scenarios that would be difficult for trajectory optimization or sampling planner alone. We present tasks of traversing challenging terrain that requires discovering a contact schedule, navigating nonconvex obstacles, and coordinating many degrees of freedom. Our hybrid planner has been applied to three different robots: a quadruped, a wheeled quadruped, and a legged excavator. We validate our hybrid locomotion planner in the real world and simulation, generating behaviors we could not achieve with previous methods. The results show that computing and executing hybrid locomotion plans is possible on hardware in real time.