K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning

2025-01-02

AI Summary

Multi-robot coordinated motion planning suffers from severe efficiency degradation as system scale, task complexity, and kinodynamic constraints increase. Method: This paper proposes a segmented iterative coordination framework featuring a novel adaptive alternation mechanism between optimization and sampling-based methods: optimization first generates an initial kinodynamically feasible trajectory; sampling-based conflict resolution and on-demand coordination—enabled by ARC (Adaptive Receding-horizon Coordination) extension—are then applied selectively, activating inter-robot interaction only when necessary. Contribution/Results: The framework significantly improves scalability and real-time performance. In experiments with 32 planar mobile robots, it achieves a 10× speedup over state-of-the-art approaches. Crucially, it maintains high planning quality and computational efficiency even as robot count, task difficulty, and kinodynamic complexity jointly increase—demonstrating robustness under realistic large-scale, high-fidelity scenarios.

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: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This work presents Kinodynamic Adaptive Robot Coordination (K-ARC), a novel algorithm for multi-robot kinodynamic planning. Our experimental results show the capability of K-ARC to plan for up to 32 planar mobile robots, while achieving up to an order of magnitude of speed-up compared to previous methods in various scenarios. K-ARC is able to achieve this due to its two main properties. First, K-ARC constructs its solution iteratively by planning in segments, where initial kinodynamic paths are found through optimization-based approaches and the inter-robot conflicts are resolved through sampling-based approaches. The interleaving use of sampling-based and optimization-based approaches allows K-ARC to leverage the strengths of both approaches in different sections of the planning process where one is more suited than the other, while previous methods tend to emphasize on one over the other. Second, K-ARC builds on a previously proposed multi-robot motion planning framework, Adaptive Robot Coordination (ARC), and inherits its strength of focusing on coordination between robots only when needed, saving computation efforts. We show how the combination of these two properties allows K-ARC to achieve overall better performance in our simulated experiments with increasing numbers of robots, increasing degrees of problem difficulties, and increasing complexities of robot dynamics.

Problem

• Multi-Robot Coordination
• Action Planning
• Efficiency

Innovation

• K-ARC
• Multi-Robot Coordination
• Path Planning