CTS-PLL: A Robust and Anytime Framework for Collaborative Task Sequencing and Multi-Agent Path Finding

TL;DR

This paper presents CTS-PLL, a hierarchical framework for collaborative task sequencing and multi-agent path finding that improves robustness and solution quality through local re-planning and anytime refinement, validated by extensive benchmarks and real-world experiments.

Contribution

Introduces CTS-PLL, a novel hierarchical framework with lock agent mechanisms and LNS-based refinement for robust, efficient multi-agent planning in complex environments.

Findings

Higher success rates than existing methods

Improved solution quality in dense environments

Effective in real-world robot experiments

Abstract

The Collaborative Task Sequencing and Multi-Agent Path Finding (CTS-MAPF) problem requires agents to accomplish sequences of tasks while avoiding collisions, posing significant challenges due to its combinatorial complexity. This work introduces CTS-PLL, a hierarchical framework that extends the configuration-based CTS-MAPF planning paradigm with two key enhancements: a lock agents detection and release mechanism leveraging a complete planning method for local re-planning, and an anytime refinement procedure based on Large Neighborhood Search (LNS). These additions ensure robustness in dense environments and enable continuous improvement of solution quality. Extensive evaluations across sparse and dense benchmarks demonstrate that CTS-PLL achieves higher success rates and solution quality compared with existing methods, while maintaining competitive runtime efficiency. Real-world robot experiments further demonstrate the feasibility of the approach in practice.