Path reinforcement learning planning of nuclear power plant UAVs based on dynamic target probability distribution

Abstract

This work develops a path reinforcement learning planning algorithm for unmanned aerial vehicles (UAVs) based on the probability distribution of dynamic targets. The proposed algorithm enables the effective interception of aerial dynamic invasive targets into nuclear power plants. In accordance with the status information of the invasive target, the target probability distribution is calculated by the probability diffusion principle and the target's possible location is deduced based on the obtained distribution. On this basis, the action space based on path point transfer rules is established and the dynamic updating mechanism of the reward function based on the target probability distribution is designed. Using $Q$-learning for continuous path optimization, a UAV path planning framework based on the target probability distribution and reinforcement learning is constructed, so as to realize the UAV path planning. Simulation results indicate that the proposed method can enable the autonomous path planning of UAVs for intercepting aerial dynamic invasive targets into nuclear power plants when the target point changes.