Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture
Ravil I. Mukhamediev, Kirill Yakunin, Margulan Aubakirov, Ilyas Assanov, Yan Kuchin, Адилхан Сымагулов, Vitaly Levashenko, Elena N. Zaitseva 等 10 位
Satbayev University Institute of Information and Computational Technologies Almaty Management University Maharishi International University
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摘要与影响
Precision farming is one of the ways of transition to the intensive methods of agricultural production. The case of application of unmanned aerial vehicles (UAVs) for solving problems of agriculture and animal husbandry is among the actively studied issues. The UAV is capable of solving the tasks of monitoring, fertilizing, herbicides, etc. However, the effective use of UAV requires to solve the tasks of flight planning, taking into account the heterogeneity of the available attachments and the problem solved in the process of the overflight. This research investigates the problem of flight planning of a group of heterogeneous UAVs applied to solving the issues of coverage, which may arise both in the course of monitoring and in the process of the implementation of agrotechnical measures. The method of coverage path planning of heterogenic UAVs group based on a genetic algorithm is proposed; this method provides planning of flight by a group of UAVs using a moving ground platform on which UAVs are recharged and refueled (multi heterogenic UAVs coverage path planning with moving ground platform (mhCPPmp)). This method allows calculating a fly by to solve the task of covering fields of different shapes and permits selecting the optimal subset of UAVs from the available set of devices; it also provides a 10% reduction in the cost of a flyby compared to an algorithm that does not use heterogeneous UAVs or a moving platform.
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生物医学Smart Agriculture and AI
Advanced Manufacturing and Logistics Optimization · Environmental Sustainability and Technology
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