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

2026-03-26

AI Summary

This work addresses the computational challenges posed by combinatorial explosion in cooperative task sequencing and multi-agent pathfinding (CTS-MAPF) by proposing a hierarchical framework, CTS-PLL. The approach integrates configuration space modeling with an agent locking detection and release mechanism, and innovatively combines complete local replanning with an anytime optimization strategy based on Large Neighborhood Search (LNS). Evaluated across both sparse and dense scenarios, CTS-PLL consistently achieves higher success rates, improved solution quality, and enhanced robustness compared to existing methods, while maintaining competitive computational efficiency. The practical viability of the framework is further validated through real-world robotic experiments, demonstrating its effectiveness in physical multi-agent coordination tasks.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Collaborative Task Sequencing
• Multi-Agent Path Finding
• Collision Avoidance
• Combinatorial Complexity

Innovation

• Collaborative Task Sequencing
• Multi-Agent Path Finding
• Lock Agents Detection
• Anytime Planning
• Large Neighborhood Search