Online Omnidirectional Jumping Trajectory Planning for Quadrupedal Robots on Uneven Terrains

TL;DR

This paper presents a real-time omnidirectional jumping trajectory planning framework for quadruped robots on uneven terrains, incorporating environmental perception, kinodynamic constraints, and an accelerated evolutionary optimizer, validated on real robots.

Contribution

A novel cascade online optimization framework for omnidirectional jumping that integrates trajectory generation, kinodynamic constraints, and environmental perception for quadruped robots.

Findings

Jump trajectories generated in ~0.1 seconds with warm start.

Successfully validated on two quadruped robots on uneven terrains.

Framework extended to humanoid robots.

Abstract

Natural terrain complexity often necessitates agile movements like jumping in animals to improve traversal efficiency. To enable similar capabilities in quadruped robots, complex real-time jumping maneuvers are required. Current research does not adequately address the problem of online omnidirectional jumping and neglects the robot's kinodynamic constraints during trajectory generation. This paper proposes a general and complete cascade online optimization framework for omnidirectional jumping for quadruped robots. Our solution systematically encompasses jumping trajectory generation, a trajectory tracking controller, and a landing controller. It also incorporates environmental perception to navigate obstacles that standard locomotion cannot bypass, such as jumping from high platforms. We introduce a novel jumping plane to parameterize omnidirectional jumping motion and formulate a tightly coupled optimization problem accounting for the kinodynamic constraints, simultaneously optimizing CoM trajectory, Ground Reaction Forces (GRFs), and joint states. To meet the online requirements, we propose an accelerated evolutionary algorithm as the trajectory optimizer to address the complexity of kinodynamic constraints. To ensure stability and accuracy in environmental perception post-landing, we introduce a coarse-to-fine relocalization method that combines global Branch and Bound (BnB) search with Maximum a Posteriori (MAP) estimation for precise positioning during navigation and jumping. The proposed framework achieves jump trajectory generation in approximately 0.1 seconds with a warm start and has been successfully validated on two quadruped robots on uneven terrains. Additionally, we extend the framework's versatility to humanoid robots.