Multi-target points path planning for fixed-wing unmanned aerial vehicle performing reconnaissance missions

Abstract

In this paper, we study the path planning problem of a fixed-wing unmanned aerial vehicle (UAV) with a reconnaissance camera when performing a reconnaissance mission in airspace containing a no-fly zone. Due to the UAV's dynamic constraints and the reconnaissance mission's specificity, the problem can be formulated as a special variant of the traveling salesman problem (TSP), called the dynamic-constrained TSP with neighborhoods (DCTSPN). To solve this problem, the authors propose a hierarchical algorithm based on deep reinforcement learning (DRL), divided into an optimal sequence planning layer and a shortest path planning layer. In the optimal sequence planning layer, Firstly, the neighborhood boundary of the target point is discretized to form multiple reconnaissance points; then, the complex trajectory planning problem is simplified to planning on a finite directed graph by randomly selecting a finite set of reconnaissance points from the neighborhood boundary of the target point set. The double deep Q network (DDQN) algorithm is used to solve for the target point traversal sequence and the reconnaissance points for each target. In the shortest path planning layer, Use the Deep Deterministic Policy Gradient (DDPG) algorithm to perform global path planning in continuous state space and action space and generate a dynamically feasible optimal flight trajectory under the guidance of the optimal reconnaissance sequence to complete the given reconnaissance mission, Avoid no-fly zones. The simulation results show that the hierarchical algorithm is highly applicable and efficient.