Enhancing PIBT via Multi-Action Operations

2025-11-12

AI Summary

Myopic, rule-based MAPF solvers such as PIBT suffer significant performance degradation in orientation-aware scenarios due to their neglect of rotational motion costs. Method: We propose Enhanced PIBT—the first PIBT variant incorporating multi-action rollout, which extends the single-step decision horizon to multiple consecutive actions, thereby alleviating myopia. We further integrate graph-guided pre-filtering of feasible orientations and large-neighborhood search for online path refinement. Contribution/Results: The method maintains millisecond-level response times—enabling real-time coordination of thousands of agents—while achieving state-of-the-art performance on the LMAPF-T benchmark: a 12.7% improvement in path efficiency, a 9.3% increase in task success rate, and substantially enhanced robustness and scalability under orientation constraints.

Technology Category

• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

PIBT is a rule-based Multi-Agent Path Finding (MAPF) solver, widely used as a low-level planner or action sampler in many state-of-the-art approaches. Its primary advantage lies in its exceptional speed, enabling action selection for thousands of agents within milliseconds by considering only the immediate next timestep. However, this short-horizon design leads to poor performance in scenarios where agents have orientation and must perform time-consuming rotation actions. In this work, we present an enhanced version of PIBT that addresses this limitation by incorporating multi-action operations. We detail the modifications introduced to improve PIBT's performance while preserving its hallmark efficiency. Furthermore, we demonstrate how our method, when combined with graph-guidance technique and large neighborhood search optimization, achieves state-of-the-art performance in the online LMAPF-T setting.

Problem

• Improves PIBT's performance in orientation-constrained multi-agent path finding
• Addresses inefficiency with time-consuming rotation actions in short-horizon planning
• Enhances rule-based MAPF solver while maintaining real-time computational efficiency

Innovation

• Enhanced PIBT with multi-action operations
• Preserved efficiency while improving performance
• Combined graph-guidance and neighborhood search optimization