Contact-Aware Motion Planning Among Movable Objects

2025-02-05

AI Summary

Traditional mobile robot motion planning strictly enforces collision avoidance, rendering it inadequate for tasks requiring deliberate, controlled contact with movable objects—leading to restricted reachable workspaces and low task success rates. To address this, we propose Contact-Aware Motion Planning (CAMP), a novel paradigm that explicitly models controllable contact between the robot and movable objects as complementary constraints integrated into a nonlinear optimization framework. Leveraging the Augmented Lagrangian Method (ALM), CAMP efficiently computes spatiotemporally optimal trajectories under nonsmooth contact dynamics. By transcending the rigid collision-avoidance paradigm, CAMP achieves substantial improvements in success rates on canonical NAMO (Navigation Among Movable Obstacles) and RAMO (Repositioning Among Movable Obstacles) benchmarks. Comprehensive validation—including high-fidelity simulation and real-world robotic experiments—demonstrates trajectory feasibility, physical plausibility, and rapid cross-task deployability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Contact-aware motion planning for robots
• Incorporates contact in trajectory optimization
• Improves success in NAMO and RAMO tasks

Innovation

• Contact-aware motion planning
• Complementarity constraints optimization
• Augmented Lagrangian methods