Hierarchical Search-Based Cooperative Motion Planning

TL;DR

This paper introduces a hierarchical, search-based cooperative motion planning algorithm for multi-agent systems with nonholonomic constraints, capable of handling complex scenarios involving multiple groups and obstacles, validated through simulations and real-world tests.

Contribution

The paper presents a novel leaderless, hierarchical search-based method for cooperative motion planning that supports complex multi-group scenarios with shape constraints and outlier agents.

Findings

Effective in complex multi-group scenarios

Reduces runtime with binary conflict search tree

Validated through simulation and real-world tests

Abstract

Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations, such as simplified kinematic models and inadequate support for multiple group scenarios. Focusing on the planning problem associated with a nonholonomic Ackermann model for Unmanned Ground Vehicles (UGV), we propose a leaderless, hierarchical Search-Based Cooperative Motion Planning (SCMP) method. The high-level utilizes a binary conflict search tree to minimize runtime, while the low-level fabricates kinematically feasible, collision-free paths that are shape-constrained. Our algorithm can adapt to scenarios featuring multiple groups with different shapes, outlier agents, and elaborate obstacles. We conduct algorithm comparisons, performance testing, simulation, and real-world testing, verifying the effectiveness and applicability of our algorithm. The implementation of our method will be open-sourced at https://github.com/WYCUniverStar/SCMP.