A Fuzzy Grouping-Based Memetic Algorithm for Multi-Depot Multi-Uav Power Pole Inspection

Abstract

Power pole inspection is very important to maintain the normal operation of electrical system. It usually requires to fly a fleet of unmanned aerial vehicles (UAVs) from multiple depots at the same time to jointly complete the inspection tasks distributed in a wide area, which is a challenging planning problem. In order to address this problem, this work first builds the model of the multi-depot multi-UAV power pole inspection problem with charging stations. After that, a fuzzy grouping-based memetic algorithm named FGATS is proposed to solve the problem. Specifically, a fuzzy grouping strategy is proposed to divide a large-scale problem into multiple small-scale subproblems in order to reduce the complexity of the problem. It utilizes a membership probability model instead of a fixed index to assign tasks to different depots. Then, a hybrid algorithm combining genetic algorithm and tabu search is designed to optimize the subproblems jointly. This allows for a better balance of global and local searches. After optimizing a certain number of iterations, the problem is re-divided and the populations are re- initialized by the proposed solution update strategy. The solution update strategy learns useful historical task-sequence knowledge to help the current round. Experiments on both artificial terrains and a real terrain verifies the effectiveness and efficiency of FGATS.

Keywords: Memetic algorithm, fuzzy grouping, Unmanned aerial vehicles (UAVs), Genetic algorithm, tabu search