UAV Path Planning Approach for Environmental Supervision Data Collection in Power Grid Construction Projects

To regulate power grid construction projects and mitigate their impact on the environment, it is necessary to regularly collect environmental supervision data on-site during the construction process. Currently, environmental supervision mainly relies on manual investition, which is inefficient and has limited coverage. With the integration of the new generation of information technology and unmanned aerial vehicles (UAV), environmental supervision is making strides towards becoming more digitalized, automated, and intelligent. The path planning problem of UAV collecting environmental data is formulated as a multi-sortie travelling salesman problem (MS-TSP) model, and a hybrid genetic algorithm (HGA) is proposed. The effectiveness of HGA is validated through the TSPLIB benchmarks, and a case study is conducted using real-world data. The research findings demonstrate that HGA can swiftly determine optimal path for collecting environmental supervision data for UAV, thereby significantly enhancing collection efficiency.