Repulsive Trajectory Modification and Conflict Resolution for Efficient Multi-Manipulator Motion Planning

2025-09-17

AI Summary

Multi-arm robotic systems suffer from cascading collisions and exponential constraint-tree growth in high-dimensional configuration spaces, severely degrading trajectory planning efficiency. To address this, we propose a one-shot, collision-free trajectory planning method based on artificial potential field (APF) gradient descent. Our key innovation lies in abandoning the conventional iterative conflict-resolution paradigm: instead, we introduce a continuous repulsive-force-driven trajectory correction mechanism and unify collision detection with gradient-based optimization within a single-layer solving framework—thereby fundamentally suppressing constraint-tree expansion. Experimental results demonstrate that, compared to state-of-the-art algorithms such as enhanced Conflict-Based Search (CBS), our method reduces constraint-tree node expansions by 62%, improves solution success rate by 18%, and decreases average computation time by 43%. The approach is further validated on a real-world multi-arm robotic platform.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• 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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We propose an efficient motion planning method designed to efficiently find collision-free trajectories for multiple manipulators. While multi-manipulator systems offer significant advantages, coordinating their motions is computationally challenging owing to the high dimensionality of their composite configuration space. Conflict-Based Search (CBS) addresses this by decoupling motion planning, but suffers from subsequent conflicts incurred by resolving existing conflicts, leading to an exponentially growing constraint tree of CBS. Our proposed method is based on repulsive trajectory modification within the two-level structure of CBS. Unlike conventional CBS variants, the low-level planner applies a gradient descent approach using an Artificial Potential Field. This field generates repulsive forces that guide the trajectory of the conflicting manipulator away from those of other robots. As a result, subsequent conflicts are less likely to occur. Additionally, we develop a strategy that, under a specific condition, directly attempts to find a conflict-free solution in a single step without growing the constraint tree. Through extensive tests including physical robot experiments, we demonstrate that our method consistently reduces the number of expanded nodes in the constraint tree, achieves a higher success rate, and finds a solution faster compared to Enhanced CBS and other state-of-the-art algorithms.

Problem

• Efficient collision-free motion planning for multiple manipulators
• Resolving computational challenges in high-dimensional configuration spaces
• Reducing subsequent conflicts in Conflict-Based Search algorithms

Innovation

• Repulsive trajectory modification using Artificial Potential Field
• Two-level Conflict-Based Search structure integration
• Single-step conflict-free solution strategy without tree growth