An effective memetic algorithm for UAV routing and orientation under uncertain navigation environments - Xiang, Shang; Wang, Ling; Xing, Lining; Du, Yonghao (Memetic computing, 2021)
Navigation correction is usually frequently required by unmanned aerial vehicles (UAVs), especially under uncertain navigation environments. Although the UAV’s straight flights that connect navigation correction points can constitute a plan of navigation corrections, the underlying attitude orientations of the UAV when flying through the visited points are also required by appropriate steering motions. In this regard, a UAV routing and orientation problem (UAV-ROP) that minimizes the 3D flight distances of the UAV under navigational, steering and uncertain constraints, is introduced and proven NP-hard in this paper. To optimize the layered routing and orientations in the UAV-ROP simultaneously, an effective memetic algorithm is proposed in this paper. In the algorithm, the GA performs the outer loop for optimizing the route and the local search metaheuristic does the inner loop for optimizing the orientations. Also, a globally maintained knowledge base that records high-quality sub-routes is used to accelerate the inner optimization of the memetic algorithm. The highlight in addressing the UAV-ROP in this paper is a layered optimization idea in a memetic algorithm to fit the layered optimization requirements of the problem. Experiments on open-access datasets indicate that, the proposed memetic algorithm shows an excellent overall performance compared with other competitors, which is qualified to give an authenticated reliable route with orientations of the UAV despite uncertain navigation environments.