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

TL;DR

K-ARC is a novel multi-robot kinodynamic planning algorithm that combines optimization and sampling methods, enabling efficient coordination of up to 32 robots with significant speed improvements over prior approaches.

Contribution

This paper introduces K-ARC, which uniquely integrates iterative segment planning with combined optimization and sampling techniques for enhanced multi-robot coordination.

Findings

K-ARC plans for up to 32 robots in various scenarios.

Achieves up to tenfold speed-up compared to previous methods.

Effective in complex, dynamic multi-robot environments.

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.