An Enhanced Hybrid Metaheuristic for Hierarchical Scheduling in 4WIDS Multi-robot Systems under Confined Environments

Multi-robot systems have emerged as a transformative paradigm for industrial automation. However, deploying these systems in dense, dynamic environments like ultra-dense warehouses and Ro-Ro terminals remains challenging due to simplified motion constraints, idealized models, and the tight coupling of task assignment, trajectory planning, and conflict resolution under strict spatiotemporal constraints. To address these problems, we propose a hierarchical scheduling framework for four-wheel independent drive/steering robot groups in confined environments. Firstly, at the task assignment layer, we introduce an enhanced hybrid metaheuristic for task assignment that integrates particle swarm optimization with a genetic algorithm, augmented by a problem-specific fitness function and adaptive mutation strategies to prevent premature convergence. Secondly, at the path planning layer, we develop a kinematics-aware conflict-based search path planner integrating motion primitives with improved A* node expansion strategies, where adaptive heuristic weighting and bidirectional search acceleration are introduced to ensure computational tractability. Simulations in a near-realistic confined environment show that the proposed hierarchical scheduling algorithm reduces total execution cost by 11.0% compared to the advanced particle swarm genetic algorithm, demonstrating its superior performance in multi-robot coordination. Furthermore, field tests conducted at the Ro-Ro Terminal of Yantai Port have fully validated the feasibility of this framework for multi-robot coordination in real-world scenarios. This work lays a theoretical and practical foundation for next-generation multi-robot coordination in constrained logistics ecosystems.