| Issue |
BIO Web Conf.
Volume 103, 2024
International Scientific-Practical Conference “Agriculture and Food Security: Technology, Innovation, Markets, Human Resources” (FIES 2023)
|
|
|---|---|---|
| Article Number | 00093 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/bioconf/202410300093 | |
| Published online | 17 April 2024 | |
Multicriteria adaptation of agricultural spraying drones
1 Blagonravov Mechanical Engineering Research Institute of the Russian Academy of Sciences (MERI of RAS), 4 Mal. Kharitonyevskiy Pereulok, 101990, Moscow, Russia
2 Belgorod State Technological University named after V.G. Shukhov, 46 Kostyukova street, Belgorod, 308012, Russia
* Corresponding author: rlbgtu@gmail.com
The article proposes a method for searching for the best configurations of robots under new and previously unknown operating conditions, which makes it possible to find solutions close to optimal using a limited number of full-scale tests. The problem of processing agricultural crops using liquid-spraying UAVs is considered. The time required for a given UAV to process a given landing area under arbitrarily specified external conditions was selected as a performance indicator of the UAV configuration. The search algorithm is based on multicriteria analysis of vector characteristics of UAVs using the criteria importance theory. Previous studies have used information about the relative importance of criteria. In this work, we additionally evaluate the value of gradations of the criteria scale and construct a value function for UAV configurations. Using a simple simulation model, a numerical experiment was carried out for 1000 different operating conditions. The effectiveness of the proposed method is confirmed by the small size of deviations of the found solutions from the optimal ones.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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