{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-scalable-post-processing-pipeline-for-large","title":"A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT","arxiv_id":"2506.16748","date":"2025-06-20","proceeding":null,"authors":["Arjo Chakravarty","Michael X. Grey","M. A. Viraj J. Muthugala","Mohan Rajesh Elara"],"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.","url_abs":"https://arxiv.org/abs/2506.16748v1","url_pdf":"https://arxiv.org/pdf/2506.16748v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-scalable-post-processing-pipeline-for-large","repo_url":"https://github.com/arjo129/pypibt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}