An Optimal Motion Planning Framework for Quadruped Jumping

Abstract

This paper presents an optimal motion planning framework to generate versatile energy-optimal quadrupedal jumping motions automatically (e.g., flips, spin). The jumping motions via the centroidal dynamics are formulated as a 12dimensional black-box optimization problem subject to the robot kino-dynamic constraints. Gradient-based approaches offer great success in addressing trajectory optimization (TO), yet, prior knowledge (e.g., reference motion, contact schedule) is required and results in sub-optimal solutions. The new proposed framework first employed a heuristics-based optimization method to avoid these problems. Moreover, a prioritization fitness function is created for heuristics-based algorithms in robot ground reaction force (GRF) planning, enhancing convergence and searching performance considerably. Since heuristicsbased algorithms often require significant time, motions are planned offline and stored as a pre-motion library. A selector is designed to automatically choose motions with user-specified or perception information as input. The proposed framework has been successfully validated only with a simple continuously tracking PD controller in an open-source Mini-Cheetah by several challenging jumping motions, including jumping over a window-shaped obstacle with 30 cm height and left-flipping over a rectangle obstacle with 27 cm height. (Video ⋆

Controlling a One-Legged Robot to Clear Obstacles by Combining the SLIP Model with Air Trajectory Planning

“…Moreover, the efficiency of virtual model control has been validated for one-legged robots [ 15 , 16 ]. There are other ways to achieve legged robot hopping, such as ZMP [ 17 , 18 ], MPC [ 19 , 20 ], WBC [ 21 , 22 , 23 ], and trajectory optimization-based methods [ 24 , 25 , 26 ]. Moreover, artificial intelligence algorithms, such as reinforcement learning and deep reinforcement learning, are also applied to jumping control in legged robots [ 27 , 28 ].…”