A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT

TL;DR

This paper introduces a scalable, hybrid multi-agent path planning pipeline combining PiBT with safety-aware smoothing, enabling real-time navigation of over 500 agents in large free-space environments with near-optimal trajectories.

Contribution

The work extends PiBT to continuous spaces and integrates a novel safety-aware smoothing technique, significantly improving scalability and path quality in large multi-agent systems.

Findings

Scales to over 500 agents in large environments

Outperforms existing methods in runtime

Produces near-optimal trajectories in sparse domains

Abstract

Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents, or rely on grid-world assumptions that do not generalize well to continuous space. In this work, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PiBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to 8-connected grids and selectively applies string-pulling based smoothing while preserving collision safety through local interaction awareness and a fallback collision resolution step based on Safe Interval Path Planning (SIPP). This design allows us to reduce overall path lengths while maintaining real-time performance. We demonstrate that our method can scale to over 500 agents in large free-space environments, outperforming existing any-angle and optimal methods in terms of runtime, while producing near-optimal trajectories in sparse domains. Our results suggest this framework is a promising building block for scalable, real-time multi-agent navigation in robotics systems operating beyond grid constraints.