Conflict-Based Lazy Search for Fast Multi-Manipulator Planning

2026-07-05

AI Summary

This work addresses the inefficiency of real-time motion planning for multiple robotic arms operating in cluttered workspaces by proposing the CBLS algorithm, which integrates the Conflict-Based Search (CBS) framework with a precomputed sparse roadmap and a Lazy Edge-based A* (LEA*) strategy. By employing a lazy edge evaluation mechanism, CBLS substantially reduces the computational overhead of single-arm path planning while preserving vertex optimality and enhancing edge evaluation efficiency. Experimental results demonstrate that, compared to conventional CBS and RRT-Connect approaches, CBLS achieves superior solution speed and overall planning performance in multi-arm coordination tasks, effectively alleviating the computational bottlenecks inherent in multi-agent path planning.

Abstract

Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do. However, real-time planning for multiple manipulators in a cluttered workspace still poses significant challenges for planning algorithms. This article proposes a new planning algorithm called Conflict-Based Lazy Search (CBLS) for multimanipulator planning. CBLS is built on Conflict-Based Search (CBS), an efficient multiagent pathfinding (MAPF) algorithm that has shown an order of magnitude speedup over previous approaches [1], [2]. CBS addresses MAPF by solving many single-agent pathfinding (SAPF) problems. Thus, its planning time directly depends on the efficiency of the SAPF algorithm adopted. Our CBLS algorithm enhances CBS with precomputation and lazy search. First, a lazily evaluated graph with controlled sparsity is precomputed for a single manipulator. Second, we propose the Lazy Edged-based A* (LEA*) for efficient SAPF. Since edge evaluation is the computational bottleneck of manipulator planning, LEA* uses lazy search and an edge queue to reduce the number of edge evaluations. We show that LEA* is optimally vertex efficient and has improved edge efficiency compared to A*. We apply the proposed CBLS to multi-manipulator planning problems and show its superior performance by comparing it with CBS and a sampling-based algorithm, namely, RRT-Connect.

Problem

• multi-manipulator planning
• real-time planning
• cluttered workspace
• computational efficiency
• pathfinding

Innovation

• Conflict-Based Search
• Lazy Search
• Multi-Manipulator Planning
• Edge Evaluation
• LEA*