Path planning remains a challenge for Unmanned Aerial Vehicles (UAVs) in dynamic environments with potential threats. In this paper, we have proposed a Deep Reinforcement Learning (DRL) approach for UAV path planning based on the global situation information. We have chosen the STAGE Scenario software to provide the simulation environment where a situation assessment model is developed with consideration of the UAV survival probability under enemy radar detection and missile attack. We have employed the dueling double deep Q-networks (D3QN) algorithm that takes a set of situation maps as input to approximate the Q-values corresponding to all candidate actions. In addition, the ε-greedy strategy is combined with heuristic search rules to select an action. We have demonstrated the performance of the proposed method under both static and dynamic task settings.

在具有潜在威胁的动态环境中，路径规划仍然是无人机的一个挑战。在本文中，我们基于全球情况信息提出了用于无人机路径规划的深度强化学习（DRL）方法。我们选择了STAGE Scenario软件来提供模拟环境，在该环境中，要考虑到在敌方雷达探测和导弹攻击下的无人机生存概率来开发态势评估模型。我们采用了双重双深度Q网络（D3QN）算法，该算法采用一组情境图作为输入来近似于与所有候选动作对应的Q值。此外，将ε贪心策略与启发式搜索规则结合起来以选择一个动作。