A hybrid optimization framework for UAV reconnaissance mission planning. (November 2022) - Liu, Wei; Zhang, Tao; Huang, Shengjun; Li, Kaiwen
Abstract: Applying unmanned aerial vehicles (UAVs) in military reconnaissance missions arises in recent years. It refers to using finite UAVs to reconnoiter some enemy targets with limited battery power, aiming at collecting valuable information as much as possible. While existing methods like heuristics and intelligent optimization methods have been widely applied, their performance on large-scale reconnaissance mission planning problems (RMPPs) is still unsatisfactory. This study proposes a hybrid optimization framework, namely, EA-DRL, to improve the optimization effect based on a problem decomposition strategy. In detail, the RMPP is decomposed into a target selection subproblem and a path planning subproblem. An evolutionary algorithm and a deep reinforcement learning method are proposed to solve them, respectively. Updating solutions to these two subproblems iteratively eventually completes the optimization of the given RMPP. Experimental results on different types of RMPP instances show great effectiveness and strong generalization ability of the proposed framework EA-DRL. Highlights: A decomposition strategy for UAV reconnaissance mission planning problems (RMPPs). A hybridization of evolutionary algorithms and deep reinforcement learning methods. A useful method which can be extended for solving multi-objective RMPPs. An efficient population initialization method.