Space-Time Conflict Spheres for Constrained Multi-Agent Motion Planning

TL;DR

This paper introduces a novel space-time conflict resolution method for multi-agent motion planning using sphere-based trajectory discretization, achieving scalable, feasible solutions that handle static and dynamic obstacles effectively.

Contribution

It presents a new sphere-based discretization and conflict search approach that combines scalability with theoretical guarantees, improving multi-agent motion planning in complex environments.

Findings

Matches state-of-the-art runtime and solution quality

Improves success rate in spatially constrained scenarios

Handles both static and dynamic obstacles effectively

Abstract

Multi-agent motion planning (MAMP) is a critical challenge in applications such as connected autonomous vehicles and multi-robot systems. In this paper, we propose a space-time conflict resolution approach for MAMP. We formulate the problem using a novel, flexible sphere-based discretization for trajectories. Our approach leverages a depth-first conflict search strategy to provide the scalability of decoupled approaches while maintaining the computational guarantees of coupled approaches. We compose procedures for evading discretization error and adhering to kinematic constraints in generated solutions. Theoretically, we prove the continuous-time feasibility and formulation-space completeness of our algorithm. Experimentally, we demonstrate that our algorithm matches the performance of the current state of the art with respect to both runtime and solution quality, while expanding upon the abilities of current work through accommodation for both static and dynamic obstacles. We evaluate our algorithm in various unsignalized traffic intersection scenarios using CARLA, an open-source vehicle simulator. Results show significant success rate improvement in spatially constrained settings, involving both connected and non-connected vehicles. Furthermore, we maintain a reasonable suboptimality ratio that scales well among increasingly complex scenarios.