Path planning for unmanned aerial vehicles in surveillance tasks under wind fields

The optimal path planning for fixed-wing unmanned aerial vehicles (UAVs) in multi-target surveillance tasks (MTST) in the presence of wind is concerned. To take into account the minimal turning radius of UAVs, the Dubins model is used to approximate the dynamics of UAVs. Based on the assumption, the path planning problem of UAVs in MTST can be formulated as a Dubins traveling salesman problem (DTSP). By considering its prohibitively high computational cost, the Dubins paths under terminal heading relaxation are introduced, which leads to significant reduction of the optimization scale and difficulty of the whole problem. Meanwhile, in view of the impact of wind on UAVs' paths, the notion of virtual target is proposed. The application of the idea successfully converts the Dubins path planning problem from an initial configuration to a target in wind into a problem of finding the minimal root of a transcendental equation. Then, the Dubins tour is derived by using differential evolution (DE) algorithm which employs random-key encoding technique to optimize the visiting sequence of waypoints. Finally, the effectiveness and efficiency of the proposed algorithm are demonstrated through computational experiments. Numerical results exhibit that the proposed algorithm can produce high quality solutions to the problem.