Intelligent obstacle avoidance technology for UAV distribution network inspection based on deep learning and multi-sensor fusion in complex scenarios

doi:10.13474/j.cnki.11-2246.2025.0105

Abstract

Abstract: In the inspection process of electric power distribution networks, complex environmental conditions, such as tree shading and random obstacles, often lead to UAVs encountering problems such as hovering and crashing when performing their tasks, which seriously affects inspection efficiency and safety. To cope with this challenge, this paper proposes an intelligent obstacle avoidance technique for automatic UAV inspection in complex scenarios. An environmental sensing system fusing LiDAR and machine vision is developed to capture multi-scale information for real-time obstacle identification by utilizing an atrous spatial pyramid pooling structure to increase the sensory field of convolution kernel. Advanced path planning algorithms are utilized to dynamically adjust the UAV's flight path to avoid obstacles. The results on the simulation tests show that the system's obstacle avoidance ability in complex environments is significantly improved, the inspection efficiency is increased by more than 20%, and the risk of accidents is effectively reduced. The proposed intelligent obstacle avoidance technology provides an efficient and safe solution for UAV inspection of power distribution networks, which has a wide range of application value and promotion prospects.

Key words: unmanned aerial vehicle, atrous spatial pyramid pooling, obstacle avoidance, path planning

CLC Number:

P237

LIAO Hongbing, KUANG Songling, LI Yangfan, HUANG Xiaolu, WANG Gang, WEI Hong. Intelligent obstacle avoidance technology for UAV distribution network inspection based on deep learning and multi-sensor fusion in complex scenarios[J]. Bulletin of Surveying and Mapping, 2025, 0(1): 22-28.

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