Contact-Implicit Trajectory Optimization using an Analytically Solvable Contact Model for Locomotion on Variable Ground

Abstract

This paper presents a novel contact-implicit trajectory optimization method using an analytically solvable contact model to enable planning of interactions with hard, soft, and slippery environments. Specifically, we propose a novel contact model that can be computed in closed-form, satisfies friction cone constraints and can be embedded into direct trajectory optimization frameworks without complementarity constraints. The closed-form solution decouples the computation of the contact forces from other actuation forces and this property is used to formulate a minimal direct optimization problem expressed with configuration variables only. Our simulation study demonstrates the advantages over the rigid contact model and a trajectory optimization approach based on complementarity constraints. The proposed model enables physics-based optimization for a wide range of interactions with hard, slippery, and soft grounds in a unified manner expressed by two parameters only. By computing trotting and jumping motions for a quadruped robot, the proposed optimization demonstrates the versatility for multi-contact motion planning on surfaces with different physical properties.

in IEEE Robotics and Automation Letters

References in corpus (3)

• Contact-Implicit Trajectory Optimization using Orthogonal Collocation
• Learning natural locomotion behaviors for humanoid robots using human knowledge
• Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification

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• Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning