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.
Summary videos for the paper title "Online Omnidirectional Jumping Trajectory Planning for Quadrupedal Robots on Uneven Terrains"

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