Decentralised schedule for mobile robots in Industry 4.0: an adaptive path planning strategy selection mechanism under dynamic environments

Path planning is essential for mobile robots to operate efficiently in uncertain environments. As the number of robots increases, centralised approaches struggle to provide feasible solutions within real-time constraints. To address this, an adaptive hybrid strategy-based decentralised path planning algorithm is proposed to solve the real-time path planning problem through decentralised computing. First, various path planning strategies are introduced, including a neural computing planning strategy, a dynamic search planning strategy, and a cluster coordination planning strategy. Then, an intelligent strategy selection mechanism with an adaptive strategy adjustment factor is designed, allowing each robot to dynamically select optimal planning strategies based on their current state and ensuring the planned paths exhibit greater flexibility and adaptability. Finally, the results of the ablation experiment indicate that all three strategies effectively enhance the navigation capabilities of robots in a decentralised mode. The algorithm comparison experiment demonstrates that the proposed algorithm achieves a higher task completion rate and a lower detour percentage in various environments. The decision response experiment shows that our approach has an average decision-making time of approximately 60 ms, which meets the real-time requirements for decentralised path planning of mobile robots in most scenarios.