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An effective memetic algorithm for UAV routing and orientation under uncertain navigation environments
Memetic Computing ( IF 3.3 ) Pub Date : 2021-05-14 , DOI: 10.1007/s12293-021-00334-9
Shang Xiang , Ling Wang , Lining Xing , Yonghao Du

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.



中文翻译:

不确定导航环境下无人机航路和定向的有效模因算法

无人机通常经常需要进行导航校正,尤其是在不确定的导航环境下。尽管连接导航校正点的无人飞行器的直航可以构成导航校正的计划,但是适当的转向运动也要求无人飞行器在通过访问点时飞行时的基本姿态取向。在这方面,本文介绍了一种在导航,操纵和不确定性约束条件下使无人机的3D飞行距离最小化的无人机航路和定向问题(UAV-ROP),并证明了NP-hard的有效性。为了同时优化UAV-ROP中的分层路由和方向,提出了一种有效的模因算法。在算法中 GA执行外部循环以优化路线,而局部搜索元启发式方法执行内部循环以优化方向。此外,记录了高质量子路径的全球维护知识库用于加速模因算法的内部优化。本文解决UAV-ROP的重点是模因算法中的分层优化思想,以适应问题的分层优化需求。在开放访问数据集上的实验表明,与其他竞争者相比,所提出的模因算法具有出色的整体性能,尽管导航环境不确定,但该算法能够给出经过验证的,具有无人机方向的可靠路线。记录高质量子路由的全球维护知识库用于加速模因算法的内部优化。本文解决UAV-ROP的重点是模因算法中的分层优化思想,以适应问题的分层优化需求。在开放访问数据集上的实验表明,与其他竞争者相比,所提出的模因算法具有出色的整体性能,尽管导航环境不确定,但该算法能够给出经过验证的,具有无人机方向的可靠路线。记录高质量子路由的全球维护知识库用于加速模因算法的内部优化。本文解决UAV-ROP的重点是模因算法中的分层优化思想,以适应问题的分层优化需求。在开放访问数据集上的实验表明,与其他竞争者相比,所提出的模因算法具有出色的整体性能,尽管导航环境不确定,但该算法能够给出经过验证的,具有无人机方向的可靠路线。本文解决UAV-ROP的重点是模因算法中的分层优化思想,以适应问题的分层优化需求。在开放访问数据集上的实验表明,与其他竞争者相比,所提出的模因算法具有出色的整体性能,尽管导航环境不确定,但该算法能够给出经过验证的,具有无人机方向的可靠路线。本文解决UAV-ROP的重点是模因算法中的分层优化思想,以适应问题的分层优化需求。在开放访问数据集上的实验表明,与其他竞争者相比,所提出的模因算法具有出色的整体性能,尽管导航环境不确定,但该算法能够给出经过验证的,具有无人机方向的可靠路线。

更新日期:2021-05-14
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