Algorithm for Multi-drone Path Planning and Coverage of Agricultural Fields

Tevyashov, G. K.; Mamchenko, Mark; Migachev, Andrey N.; Galin, Rinat; Kulagin, Konstantin A.; Trefilov, Petr; Onisimov, Rodion O.; Goloburdin, Nikolay V.

doi:10.1007/978-981-16-3349-2_25

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…However, the algorithm is comparatively complex, which is its only disadvantage. Some multi-drone systems also appear in the literature where the task delegation method is used from one drone to another [33,45]. A single drone system in the context of a dock station is also used for this kind of purpose [46].…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

Al-Naeem

Rahman

Banerjee³

et al. 2023

Sustainability

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Crop monitoring and smart spraying have become indispensable parts of precision agriculture where unmanned aerial vehicles (UAVs) play a lead role. In particular, in large agricultural fields, aerial monitoring is a sustainable solution provided it can be performed in an energy-efficient manner. The existing literature points out that the research on precision agriculture using UAVs is still very minimal. In this article, we propose a support vector machine (SVM)-based UAV location management technique where UAVs change position over various portions or regions of a large agricultural field so that crops are properly monitored in an energy-efficient manner. Whenever a processing request is generated from any sensor in a part of the field, the UAV investigates with an SVM to decide whether to move on to the center of that field based on various parameters or characteristics such as region-id, packet-id, time of day, waiting times of the packets, the average waiting time of others within a predefined time window, location of the UAV, residual energy of the UAV after processing the packet, and movement after processing the packet. We use 70% of our data for training and the other 30% for testing. In our simulation study, we use accuracy, precision, and recall to measure in both contexts to determine the efficiency of the model, and also the amount of energy preserved is computed corresponding to every move. We also compare our approach with current state-of-the-art energy-preserving UAV movement control techniques which are compatible with the present application scenario. The proposed technique produced 6.5%, 34.5%, and 61.5% better results in terms of percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL), respectively.

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Section: Related Workmentioning

confidence: 99%

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

Al-Naeem

Rahman

Banerjee³

et al. 2023

Sustainability

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“…Vazquez-Carmona et al [34] developed an efficient algorithm for the CPP, especially for disinfecting the areas, and simulated the routes that they developed. Tevyashov et al [35] solved the multi-drone CPP of the agricultural fields, by minimizing the maximum time needed to cover assigned areas. The common objectives of the CPP models are maximum area coverage, minimum energy consumption, and minimum time [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Single-drone energy efficient coverage path planning with multiple charging stations for surveillance

Celik,

Ustaomer,

Satoglu

2023

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Drones have started to be used for surveillance within the cities, visually scanning the predefined zones, quickly detecting abnormal states such as fires, accidents, and pollution, or assessing the disaster zones. Coverage Path Planning (CPP) is a problem that aims to determine the most suitable path or motion plan for a vehicle to cover the entire desired area in the task. So, this paper proposes a novel two-dimensional coverage path planning (CPP) mathematical model with the fact that a single drone may need to be recharged within its route based on its energy consumption, and the obstacles must be avoided while constructing the route. Our study aims to create realistic routes for drones by considering multiple charging stations and obstacles for surveillance. We tested the model for a grid example based on the scenarios obtained by changing the layout, the number of obstacles and recharging stations, and area size using the Python Gurobi Optimization library. As a contribution, we analyzed the impact of the number of existing obstacles and recharging stations, the size and layout of the area to be covered on total energy consumption, and the total solution time of CPP in our study for the first time in the literature, through a detailed Scenario Analysis. Results show that the map size and the number of covered cells affect the total energy consumption, but different layouts with shuffled cells are not effective. The area size to be covered affects the total computation time, significantly. As the number of obstacles and recharging stations increases, the computation time decreases up to a certain limit, then stabilizes.

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Modeling of Joint Motion Planning of Group of Mobile Robots and Unmanned Aerial Vehicle

Shirokov

Salomatin

Galin

et al. 2023

Frontiers in Robotics and Electromechanics

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Algorithm for Multi-drone Path Planning and Coverage of Agricultural Fields

Cited by 5 publications

References 18 publications

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

Single-drone energy efficient coverage path planning with multiple charging stations for surveillance

Modeling of Joint Motion Planning of Group of Mobile Robots and Unmanned Aerial Vehicle

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