Rapid and Reliable Quadruped Motion Planning with Omnidirectional Jumping
This work addresses the challenge of enhancing mobility for legged robots in complex environments, though it appears incremental as it builds on existing planning methods with a focus on omnidirectional capabilities.
The authors tackled the problem of dynamic jumping motion planning for legged robots by developing a hierarchical framework that enables real-time, omnidirectional jumping, resulting in the Mini Cheetah Vision quadruped executing reliable jumps onto surfaces up to its hip height.
Dynamic jumping with legged robots poses a challenging problem in planning and control. Formulating the jump optimization to allow fast online execution is difficult; efficiently using this capability to generate long-horizon motion plans further complicates the problem. In this work, we present a hierarchical planning framework to address this problem. We first formulate a real-time tractable trajectory optimization for performing omnidirectional jumping. We then embed the results of this optimization into a low dimensional jump feasibility classifier. This classifier is leveraged to produce geometric motion plans that select dynamically feasible jumps while mitigating the effects of the process noise. We deploy our framework on the Mini Cheetah Vision quadruped, demonstrating the robot's ability to generate and execute reliable, goal-oriented plans that involve forward, lateral, and rotational jumps onto surfaces as tall as the robot's nominal hip height. The ability to plan through omnidirectional jumping greatly expands the robot's mobility relative to planners that restrict jumping to the sagittal or frontal planes.