This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected to multiple threats. A cost function is first formulated to convert the path planning into an optimization problem that incorporates requirements and constraints for the feasible and safe operation of the UAV. SPSO is then used to find the optimal path that minimizes the cost function by efficiently searching the configuration space of the UAV via the correspondence between the particle position and the speed, turn angle and climb/dive angle of the UAV. To evaluate the performance of SPSO, eight benchmarking scenarios have been generated from real digital elevation model maps. The results show that the proposed SPSO outperforms not only other particle swarm optimization (PSO) variants including the classic PSO, phase angle-encoded PSO and quantum-behave PSO but also other state-of-the-art metaheuristic optimization algorithms including the genetic algorithm (GA), artificial bee colony (ABC), and differential evolution (DE) in most scenarios. In addition, experiments have been conducted to demonstrate the validity of the generated paths for real UAV operations. Source code of the algorithm can be found at https://github.com/duongpm/SPSO.

中文翻译：

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基于球形矢量粒子群算法的安全性增强无人机航路规划

本文提出了一种新的算法，称为基于球形矢量的粒子群优化算法（SPSO），以解决复杂环境下面临多种威胁的无人机的路径规划问题。首先制定成本函数，以将路径规划转换为优化问题，该优化问题包含了对无人机的可行和安全操作的要求和约束。然后，通过粒子位置与无人机的速度，转弯角和爬升/俯冲角之间的对应关系，有效地搜索无人机的配置空间，然后使用SPSO来找到使成本函数最小的最佳路径。为了评估SPSO的性能，已经从真实的数字高程模型图生成了八个基准测试方案。结果表明，提出的SPSO不仅优于其他粒子群优化（PSO）变体，包括经典PSO，相角编码PSO和量子行为PSO，而且还优于其他最新的元启发式优化算法，包括遗传算法（GA），人工蜂群（ABC）和差异进化（DE）在大多数情况下。另外，已经进行了实验以证明所产生的路径对于真实的UAV操作的有效性。该算法的源代码可以在https://github.com/duongpm/SPSO中找到。和大多数情况下的差分进化（DE）。另外，已经进行了实验以证明所产生的路径对于真实的UAV操作的有效性。该算法的源代码可以在https://github.com/duongpm/SPSO中找到。和大多数情况下的差分进化（DE）。另外，已经进行了实验以证明所产生的路径对于真实的UAV操作的有效性。该算法的源代码可以在https://github.com/duongpm/SPSO中找到。