Unified Safety-Critical Motion Planning for Connected Non-Holonomic Agents Using an Adaptive A* and Hybrid A* Integration

This paper presents a unified approach to safety-critical, multi-agent motion planning for connected autonomous robotic systems, seamlessly integrating the kinematic, dynamic, and safety constraints of individual agents, while reducing computational expense to ensure real-time applicability. By integrating Voronoi Cells with an adaptive blend of A* and Hybrid A* algorithms, the proposed combinational planner ensures the generation of feasible and executable trajectories, guaranteeing efficient and collision-free navigation of multiple agents in dynamically complex environments. An additional deadlock avoidance strategy is proposed to further enhance the safety layer. We demonstrate the effectiveness and robustness of our approach in terms of efficiency, collision avoidance, and deadlock resolution through simulations in diverse, randomly generated environments. The results show that the proposed method outperforms existing methods in terms of dynamic considerations and obstacle avoidance, making it a practical realtime motion planning approach for connected non-holonomic agents in complex environments.