Continuous-Space Multi-Agent Path Finding via Enhanced Prioritized Search With Ackermann Kinematic Constraints

Abstract

Multi-Agent Path Finding (MAPF) in complex environments remains challenging due to high computational complexity, frequent conflicts, and realistic motion constraints. Most existing methods focus on discrete spaces or idealized omnidirectional models, often neglecting or partially considering nonholonomic constraints, which limits their applicability to real-world robotic systems. This letter proposes a hierarchical continuous-space MAPF framework explicitly designed for Ackermann-steered robots, balancing computational efficiency, global coordination, and motion feasibility. In the path search layer, a spatiotemporal hybrid A* algorithm with an adaptive dynamic weighting factor improves the trade-off between computational cost and path quality, while a homotopy-group clustering mechanism provides structured agent grouping for conflict resolution. In the conflict resolution layer, a partial-order priority reconstruction and flexible priority-based dynamic adjustment strategy effectively reduce search space and conflict density. The trajectory optimization layer integrates a decentralized sequential quadratic programming method to ensure trajectory feasibility and smoothness. Comprehensive experiments, including ablation, comparative, and scalability studies, demonstrate that the proposed method achieves lower runtime, reduced execution cost, and better coordination than existing MAPF approaches, while maintaining strong scalability in high-density environments.