Abstract:

Heuristic algorithms are widely used in path planning for mobile units. However, in specific situations (e.g., Zhongshan Station in Antarctic), restrictions in communication bandwidth, available energy, and computing power require more efficiency and independence from the mobile units to achieve their path-planning tasks. This paper proposes an improved grey wolf-optimized path-planning algorithm and a federated learning mechanism to improve the path-planning task efficiency and reduce resource consumption. A design solution for a network-switching and distributed communication facility is presented, then used as the basis for a digital twin-sensing network. Experimental results show that the hardware platform functioned in compliance with the actual task requirements, that the new heuristic path planning algorithm outperformed other algorithms in its class, and that the federated learning mechanism improved the parameter setting efficiency in the planning algorithm. The proposed model demonstrably improved the path-planning efficiency of mobile units at the Antarctic research stations. Moreover, a series of simulations and field experiments at Zhongshan Station confirmed that the proposed algorithm achieved good performance in global heuristic path planning, planning cost evaluation, and regional dynamic path planning.

Key words:

path planning, heuristic algorithms, Zhongshan Station, federated learning