Planning efficient and robust behaviors for model-based power tower inspection

Abstract

This article puts forward a novel idea of power tower inspection depending on model-based behavior planning. It mainly focuses on the problems caused by uncertain security, flight time limitation and stochastic noise affection when inspecting with flying robot. Firstly, we construct a safety space and some target viewing regions based on the model of the power tower. Then a reinforcement learning procedure is adopted to find an optimal policy of guiding the inspection behavior. Experimental results show that the model-based behavior planning improves the efficiency of the inspection significantly even with the wind gusts or stochastic interferences.