基于球矢量粒子群算法的安全增强UAV路径规划
Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization
摘要：
针对无人机在复杂环境下受到多种威胁时的路径规划问题，提出了一种基于球矢量的粒子群优化算法（SPSO）。首先，制定成本函数，将路径规划转化为优化问题，该优化问题包括UAV的可行和安全操作的要求和约束。然后，SPSO被用来找到最优路径，通过有效地搜索无人机的配置空间，通过粒子的位置和无人机的速度，转弯角和爬升/俯冲角之间的对应关系，最小化的成本函数。为了评估SPSO的性能，从真实的数字高程模型地图中生成了八个基准场景。实验结果表明，该算法不仅优于经典粒子群优化算法、相位角编码粒子群优化算法和量子行为粒子群优化算法，而且优于遗传算法、人工蜂群算法和差分进化算法.此外，已进行了实验，以证明真实的无人机操作的路径生成的有效性。该算法的源代码可以在https://github.com/duongpm/SPSO上找到。
英文摘要：
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.
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