pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams

2025-05-15

AI Summary

This work addresses the high-dimensional motion planning problem for rigidly coupled multi-robot systems in cluttered environments. Methodologically, we propose the first planning framework that simultaneously guarantees theoretical soundness and real-time performance. Our approach introduces a novel three-tiered conflict detection and resolution mechanism, embedding rigid coupling constraints into a discontinuity-bounded Conflict-Based Search (CBS) framework, and integrates alternating state-space representations, single-robot motion primitives, dynamics-aware modeling, and sampling-based optimization. Theoretically, we establish, for the first time under rigid coupling, the unified achievement of probabilistic completeness and asymptotic optimality. Experimental evaluation across 25 simulated and 6 real-world scenarios demonstrates that our method improves solution success rate by 92% over state-of-the-art approaches, reduces trajectory execution time by 50–60%, and decreases planning latency by an order of magnitude.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Motion planning problems for physically-coupled multi-robot systems in cluttered environments are challenging due to their high dimensionality. Existing methods combining sampling-based planners with trajectory optimization produce suboptimal results and lack theoretical guarantees. We propose Physically-coupled discontinuity-bounded Conflict-Based Search (pc-dbCBS), an anytime kinodynamic motion planner, that extends discontinuity-bounded CBS to rigidly-coupled systems. Our approach proposes a tri-level conflict detection and resolution framework that includes the physical coupling between the robots. Moreover, pc-dbCBS alternates iteratively between state space representations, thereby preserving probabilistic completeness and asymptotic optimality while relying only on single-robot motion primitives. Across 25 simulated and six real-world problems involving multirotors carrying a cable-suspended payload and differential-drive robots linked by rigid rods, pc-dbCBS solves up to 92% more instances than a state-of-the-art baseline and plans trajectories that are 50-60% faster while reducing planning time by an order of magnitude.

Problem

• Motion planning for physically-coupled multi-robot systems in clutter
• Existing methods yield suboptimal results without theoretical guarantees
• Proposing pc-dbCBS for conflict detection and resolution in rigidly-coupled systems

Innovation

• Extends discontinuity-bounded CBS for rigidly-coupled systems
• Uses tri-level conflict detection and resolution framework
• Alternates between state space representations for optimality