Analysis and Optimization of Interpolation Points for Quadruped Robots Joint Trajectory

Abstract

Trajectory planning is the foundation of locomotion control for quadruped robots. This paper proposes a bionic foot-end trajectory which can adapt to many kinds of terrains and gaits based on the idea of trajectory planning combining Cartesian space with joint space. Trajectory points are picked for inverse kinematics solution, and then quintic polynomials are used to plan joint space trajectories. In order to ensure that the foot-end trajectory generated by the joint trajectory planning is closer to the original Cartesian trajectory, the distributions of the interpolation point are analyzed from the spatial domain to temporal domain. An evaluation function was established to assess the closeness degree between the actual trajectory and the original curve. Subsequently, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) for the points selection are used to obtain a more precise trajectory. Simulation and physical prototype experiments were included to support the correctness and effectiveness of the algorithms and the conclusions.