CSDO: Enhancing Efficiency and Success in Large-Scale Multi-Vehicle Trajectory Planning

TL;DR

This paper introduces CSDO, an efficient algorithm for large-scale multi-vehicle trajectory planning that combines homotopy class exploration with decentralized optimization, significantly improving success rates and computational efficiency.

Contribution

CSDO uniquely separates homotopy class search from trajectory refinement, integrating MAPF and decentralized QP to enhance large-scale multi-vehicle trajectory planning.

Findings

Achieves up to 95% success rate in large-scale scenarios

Reduces computation time to around one second

Outperforms existing algorithms in high-density environments

Abstract

This paper presents an efficient algorithm, naming Centralized Searching and Decentralized Optimization (CSDO), to find feasible solution for large-scale Multi-Vehicle Trajectory Planning (MVTP) problem. Due to the intractable growth of non-convex constraints with the number of agents, exploring various homotopy classes that imply different convex domains, is crucial for finding a feasible solution. However, existing methods struggle to explore various homotopy classes efficiently due to combining it with time-consuming precise trajectory solution finding. CSDO, addresses this limitation by separating them into different levels and integrating an efficient Multi-Agent Path Finding (MAPF) algorithm to search homotopy classes. It first searches for a coarse initial guess using a large search step, identifying a specific homotopy class. Subsequent decentralized Quadratic Programming (QP) refinement processes this guess, resolving minor collisions efficiently. Experimental results demonstrate that CSDO outperforms existing MVTP algorithms in large-scale, high-density scenarios, achieving up to 95% success rate in 50m $\times$ 50m random scenarios around one second. Source codes are released in https://github.com/YangSVM/CSDOTrajectoryPlanning.