Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models

2025-02-05

AI Summary

This work addresses the challenge in multi-robot motion planning (MRMP) where diffusion models struggle to simultaneously ensure collision-free, kinematically feasible, and efficient sampling. We propose the first joint planning framework that embeds implicit constraint optimization directly into the diffusion sampling process. Our core innovation is a constraint-projected diffusion mechanism: a differentiable implicit projection layer enforces collision avoidance and kinematic feasibility, while gradient-guided sampling enables end-to-end verifiable trajectory generation. Furthermore, we introduce the first comprehensive MRMP benchmark covering diverse scene densities, obstacle configurations, and robot dynamics. Experiments demonstrate a 27% improvement in success rate under complex scenarios, significantly outperforming both classical and state-of-the-art learning-based planners in planning efficiency, and enabling real-time coordinated navigation for over ten robots.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the environment. Despite this promise, applying diffusion models to motion planning remains challenging due to their difficulty in enforcing critical constraints, such as collision avoidance and kinematic feasibility. These limitations become even more pronounced in Multi-Robot Motion Planning (MRMP), where multiple robots must coordinate in shared spaces. To address this challenge, this work proposes Simultaneous MRMP Diffusion (SMD), a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories. Additionally, the paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints. Experimental results show SMD consistently outperforms classical and learning-based motion planners, achieving higher success rates and efficiency in complex multi-robot environments.

Problem

• Multi-Robot Motion Planning
• Diffusion Models Constraints
• Collision-Free Kinematic Trajectories

Innovation

• Multi-Robot Motion Planning
• Constrained Diffusion Models
• Collision-Free Trajectories