Contact-Aware Motion Planning Among Movable Objects

TL;DR

This paper introduces a contact-aware motion planning framework that enables mobile robots to effectively interact with movable objects, expanding their operational capabilities beyond collision avoidance.

Contribution

The paper presents a novel optimization-based motion planning approach that incorporates contact as a constraint, improving robot navigation and object rearrangement tasks.

Findings

Expanded reachable space for robots in simulations

Significant improvement in success rates for NAMO and RAMO tasks

Feasible trajectories quickly deployable in real-world experiments

Abstract

Most existing methods for motion planning of mobile robots involve generating collision-free trajectories. However, these methods focusing solely on contact avoidance may limit the robots' locomotion and can not be applied to tasks where contact is inevitable or intentional. To address these issues, we propose a novel contact-aware motion planning (CAMP) paradigm for robotic systems. Our approach incorporates contact between robots and movable objects as complementarity constraints in optimization-based trajectory planning. By leveraging augmented Lagrangian methods (ALMs), we efficiently solve the optimization problem with complementarity constraints, producing spatial-temporal optimal trajectories of the robots. Simulations demonstrate that, compared to the state-of-the-art method, our proposed CAMP method expands the reachable space of mobile robots, resulting in a significant improvement in the success rate of two types of fundamental tasks: navigation among movable objects (NAMO) and rearrangement of movable objects (RAMO). Real-world experiments show that the trajectories generated by our proposed method are feasible and quickly deployed in different tasks.