架空线路无人机自主巡检的路径规划策略

Path planning strategy of autonomous inspection for unmanned aerial vehicles on overhead lines

针对架空线路无人机自主巡检路径规划问题,首先考虑无人机访问目标的时间成本,以巡线时间间隔与缺陷等级的几何平均作为访问节点的收益,并将其等价扩展为OP(定向问题)优化模型,以实现在一定时间内获得更大收益。然后提出一种基于LA(自学习)机制改进PSO(粒子群优化算法)的AGA(自适应遗传算法),来求解该模型在基准实例的最优解,以验证所提模型的有效性。最后以架空线路无人机自主巡检任务为应用背景,验证所提模型与算法的稳定性,且为无人机自主巡检路径规划策略的制定提供参考。

For the problem of autonomous inspection path planning of UAVs(unmanned aerial vehicles) on overhead lines,considering the time cost of access target of UAVs,the geometric mean of the line patrol time interval and the defect level is used as the benefit of visiting nodes,which is equivalently extended to the OP(orientation problem) optimization model,in order to achieve the goal of obtaining greater benefits within a certain time limit.Then,an AGA(adaptive genetic algorithm) based on LA(learning automata) mechanism to improve PSO(particle swarm optimization) is proposed to solve the optimal solution of the proposed model in the benchmark instance and verify the validity of the proposed model.Finally,taking the autonomous inspection task of UAVs on overhead lines as the application background,the stability of the proposed model and algorithm is validated,which provides a reference for the formulation of path planning strategy of autonomous inspection for UAVs.