Heuristic Navigation Model Based on Genetic Programming for Multi-UAV Power Inspection Problem with Charging Stations

Created by W.Langdon from gp-bibliography.bib Revision:1.7644

@InProceedings{Chen:2023:SMC,

• author = "Xiang-Ling Chen and Xiao-Cheng Liao and Wei-Neng Chen",
• booktitle = "2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
• title = "Heuristic Navigation Model Based on Genetic Programming for Multi-{UAV} Power Inspection Problem with Charging Stations",
• year = "2023",
• pages = "363--370",
• abstract = "Efficient power inspection is crucial for maintaining a stable power system. During an inspection, unmanned aerial vehicles (UAVs) usually need to be recharged due to the wide geographical range of inspection and the limited battery capacity of UAVs. This limitation makes the problem more challenging that requires not only optimising the task execution order, but also taking the chargings of UAVs into consideration. In order to address this complex problem, this work first formulates the UAV power inspection planning problem with charging stations. After that, we propose a new heuristic navigation model, in which UAVs can follow a heuristic rule to decide where to go next based on both its own information and task-related information. To obtain the heuristic rule, we design a set of features to describe the status of the UAVs and task completion. Then a genetic programming (GP) algorithm is introduced to evolve and get the heuristic rule. Finally, by applying heuristic navigation rule, the UAV navigation model can automatically prioritize task and charging order, and generate UAV flight routes that satisfy all constraints. The experiment results show that our method significantly outperforms the state-of-the-art algorithms.",
• keywords = "genetic algorithms, genetic programming, Adaptation models, Navigation, Inspection, Charging stations, Autonomous aerial vehicles, Mathematical models, Task analysis, unmanned aerial vehicles (UAVs), task assignment problem, charging problem, heuristic navigation model",
• DOI = "doi:10.1109/SMC53992.2023.10394169",
• ISSN = "2577-1655",
• month = oct,
• notes = "Also known as \cite{10394169}",

}

Genetic Programming entries for Xiang-Ling Chen Xiao-Cheng Liao Wei-Neng Chen

Citations