Unmanned System Asynchronous Task Planning Based on Partially Observable Monte Carlo Tree Search Algorithm

Unmanned systems are profoundly reshaping social lifestyles and modes of warfare. In the field of dynamic planning for unmanned systems, the environment is first abstracted as a topological network composed of nodes and edges. Second, for the variable step time advancement problem of asynchronous planning, a novel asynchronous planning algorithm, namely, a partially observable Monte Carlo tree search algorithm in the semi-Markov environment (SPOMCP) is proposed. The innovation is that the topological network is transformed into a sub-goal graph with the simplest information representation, and enabling rapid policy optimization based on a variable step time advancement mechanism. Through theoretical analysis, it is proven that SPOMCP can generate the optimal strategies, and the computational complexity is exponentially correlated with the number of sub-goal nodes. Finally, simulation experiments demonstrate that SPOMCP outperforms the benchmark algorithm in terms of performance, with less than 89.18% of the benchmark algorithm＇s computation time, resulting in higher average rewards.