4WIDS-MAPF: Multiagent Pathfinding for Four-Wheel Independent Drive/Steering Robots

Modern autonomous systems face significant challenges in coordinating multiple agents within complex environments, especially under nonholonomic constraints and kinodynamic motion planning demands. In particular, four-wheel independent drive/steering (4WIDS) robots, with their multimodal locomotion and nonholonomic kinematics, introduce new complexity to multiagent pathfinding problems in continuous space. This study addresses the critical challenge of generating collision-free, dynamically feasible paths for multi-4WIDS robot systems. We propose a hierarchical multiagent path finding (MAPF) framework that explicitly tackles two core limitations in existing methods: 1) a lack of physically realistic interagent conflict modeling; and 2) the inability to account for motion mode switching costs in kinodynamic path planning. At the upper level, we introduce a continuous-space binary conflict tree for resolving space–time collisions using physical dimensions and motion feasibility. At the lower level, we implement a hybrid A* search over a motion primitive database that considers mode-switch penalties and kinematic constraints. To improve search performance, we introduce two novel mechanisms: 1) a focused search factor for directional exploration and 2) an adaptive heuristic weighting factor to balance optimality and computational speed. Extensive experiments, including benchmark comparisons, ablation studies, and sensitivity analysis, validate the proposed method’s superior performance in interagent conflict resolution, real-time feasibility, and path quality.