基于改进遗传算法的倾转旋翼无人机区域覆盖路径规划

Area coverage path planning for tilt-rotor unmanned aerial vehicle based on enhanced genetic algorithm

基于改进遗传算法研究倾转旋翼无人机(TRUAV)在多障碍物约束下的区域覆盖路径规划问题.运用最小跨度算法和往返路径生成算法进行任务区域内的覆盖路径初规划,将区域覆盖问题转化为旅行商问题以优化覆盖路径顺序.为了避开区域内的障碍物,提出鱼尾形避障策略.引入最近邻算法,生成比传统遗传算法质量更高的初始种群,设计三点式交叉算子和动态区间变异算子进行遗传操作以提高所提算法的全局搜索能力,避免算法陷入局部最优.在含多个障碍物的多边形区域算例内仿真验证所提算法的性能.结果表明,相比于逐行路径覆盖算法和传统遗传算法,所提算法的覆盖路径长度减少了7.80%,TRUAV的任务区域覆盖效率显著提升.

An enhanced genetic algorithm was proposed to address the challenge of area coverage path planning for a tilt-rotor unmanned aerial vehicle(TRUAV)amidst multiple obstacles.A preliminary coverage path plan for the designated task area was devised,utilizing the minimum spanning and back-and-forth path generation algorithms.The area coverage dilemma was transformed into a traveling salesman problem to optimize the sequence of the coverage path.A fishtail-shaped obstacle avoidance strategy was proposed to circumvent obstacles within the region.The nearest neighbor algorithm was introduced to generate a superior initial population than a genetic algorithm.A three-point crossover operator and a dynamic interval mutation operator were adopted in the genetic processes to improve the proposed algorithm's global search capacity and prevent the algorithm from falling into local optima.The efficacy of the proposed algorithm was rigorously tested through simulations in polygonal areas with multiple obstacles.Results showed that,compared to the sequential path coverage algorithm and the genetic algorithm,the proposed algorithm reduced the length of the coverage path by 7.80%,significantly enhancing the coverage efficiency of TRUAV in the given task areas.