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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Path Planning for UAVs for Maximum Information Collection
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Path Planning for UAVs for Maximum Information Collection

机译：无人机路径规划以获取最大信息

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摘要

Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path the objectives are to maximize the collected information (CI) from desired regions (DR), while avoiding flying over forbidden regions (FR) and reaching the destination. The path planning problem for a single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators: pull-to-desired-region (PTDR), push-from-forbidden-region (PFFR), and pull-to-final-point (PTFP). In addition to these newly proposed operators, standard mutation and crossover operators are used. The initial population seed-path is obtained by both utilizing the pattern search method and solving the traveling salesman problem (TSP). Using this seed-path the initial population of paths is generated by randomly selected heading angles. It should be emphasized that all of the paths in population in any generation of the genetic algorithm (GA) are constructed using the dynamical mathematical model of a UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm is tested with different scenarios, and the results are presented in Section VI. Although there are previous studies in this field, the focus here is on maximizing the CI instead of minimizing the total mission time. In addition it is observed that the proposed operators generate better paths than classical evolutionary operators.
机译：路径规划考虑了设计车辆应该遵循的路径的问题。沿着设计路径，目标是最大化从所需区域（DR）收集的信息（CI），同时避免飞越禁区（FR）并到达目的地。根据新型进化算子的建议，研究了单个无人机的航路规划问题：要求区域推拉（PTDR），禁止区域推拉（PFFR）和禁止推拉终点（PTFP）。除了这些新提出的运算符之外，还使用标准的变异和交叉运算符。通过使用模式搜索方法和解决旅行商问题（TSP），可以获得初始种群的种子路径。使用该种子路径，通过随机选择的航向角生成路径的初始种群。应该强调的是，在遗传算法（GA）的任何一代中，人口中的所有路径都是使用配备有自动驾驶和制导算法的无人机的动态数学模型构建的。仿真是在MATLAB / Simulink环境中实现的。在不同的场景下测试了路径规划算法，结果在第六节中介绍。尽管该领域已有研究，但这里的重点是最大化CI，而不是最小化总任务时间。另外，可以观察到，提出的算子比经典的进化算子产生更好的路径。

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