Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture

Mukhamediev, Ravil I.; Yakunin, Kirill; Aubakirov, Margulan; Assanov, Ilyas; Kuchin, Yan; Сымагулов, Адилхан; Levashenko, Vitaly; Zaitseva, Elena; Sokolov, Dmitry; Amirgaliyev, Yedilkhan

doi:10.1109/access.2023.3235207

Cited by 37 publications

(15 citation statements)

References 59 publications

(79 reference statements)

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“…The concept of energy efficiency is implemented from perspectives: (i). Positions of changing stations [32][33][34]. (ii).…”

Section: Related Workmentioning

confidence: 99%

“…Category (ii) can be further classified into three subcategories based on specialized techniques that are applied to plan flight paths of UAVs: (a). Ant-colony-based optimization [29][30][31][32][33][34][35][36][37][38][39][40][41]. (b).…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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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“…The concept of energy efficiency is implemented from perspectives: (i). Positions of changing stations [32][33][34]. (ii).…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…Over the last few years, there has been an accelerated demand for Unmanned Aerial Vehicles (UAVs) due to several applications such as in agriculture, search and rescue, monitoring, and security [1]- [4]. It is now well known that using cooperative heterogeneous agents, for instance UAVs and ground robots, in such applications improves the overall performance [5], [6]. Generally, in these applications, the motion of one agent is independent of the motion of the other agent.…”

Section: Introductionmentioning

confidence: 99%

Competitive perimeter defense with a turret and a mobile vehicle

Bajaj

Bopardikar²,

Moll³

et al. 2023

Front. Control Eng.

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We consider perimeter defense problem in a planar conical environment with two cooperative heterogeneous defenders, i.e., a turret and a mobile vehicle, that seek to defend a concentric perimeter against mobile intruders. Arbitrary numbers of intruders are released at the circumference of the environment at arbitrary time instants and locations. Upon release, they move radially inwards with fixed speed towards the perimeter. The defenders are heterogeneous in terms of their motion and capture capabilities. Specifically, the turret has a finite engagement range and can only turn (clockwise or anti-clockwise) in the environment with fixed angular rate whereas, the vehicle has a finite capture radius and can move in any direction with unit speed. We present a competitive analysis approach to this perimeter defense problem by measuring the performance of multiple cooperative online algorithms for the defenders against arbitrary inputs, relative to an optimal offline algorithm that has information about the entire input sequence in advance. Specifically, we establish necessary conditions on the parameter space to guarantee finite competitiveness of any online algorithm. We then design and analyze four cooperative online algorithms and characterize parameter regimes in which they have finite competitive ratios. In particular, our first two algorithms are 1-competitive in specific parameter regimes, our third algorithm exhibits different competitive ratios in different regimes of problem parameters, and our fourth algorithm is 1.5-competitive in specific parameter regimes. Finally, we provide multiple numerical plots in the parameter space to reveal additional insights into the relative performance of our algorithms.

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“…In the last decade, researchers have proposed many excellent mission assignment algorithms, which are divided into two main categories, centralized mission planning and distributed mission planning, depending on the different structures of cooperative control of multiple UAVs [5,6].…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Cooperative Mission Assignment Based on Hybrid WAFC-RRAS Algorithm

Huang,

Hu,

Yang

et al. 2023

IEEE Access

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Multi-UAV cooperative mission assignment is an important research direction in the field of UAV research. In the planning process, the assignment conflict between UAVs and mission points and the solution efficiency are the key difficulties in multi-UAV cooperative mission assignment. Based on the Weighted Approximate Flight Cost (WAFC) method and the Reduced Redundant Assignment Scheme (RRAS) algorithm, a multi-UAV cooperative mission problem considering different assignment models is proposed. The contributions of this study are: firstly, the approximate flight cost matrix is constructed based on the vertical distance between the UAV and the mission point, and the matrix is applied to calculate the initial flight distance of the UAV, which fully takes into account the effects of mountain type and radar threat on the mission assignment. Secondly, the auxiliary flight cost method and the spatial mapping matrix are constructed, which maps the discrete approximation of the flight cost to the continuous space and solves the UAV's local conflict problem in the assignment process. Finally, an adaptive selection mutation strategy based on iterative individual fitness values is proposed to reduce the redundancy of candidate assignment schemes and improve the efficiency of the planning system. The simulation results verify that the algorithm has high cooperative capability, good robustness and fast solution speed when dealing with cooperative multi-UAV mission assignment planning.INDEX TERMS Vertical cut method, Approximate flight cost method, Mapping strategies, Assignment conflicts, Multi-UAV cooperative mission assignment.

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Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture

Cited by 37 publications

References 59 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

Competitive perimeter defense with a turret and a mobile vehicle

Multi-UAV Cooperative Mission Assignment Based on Hybrid WAFC-RRAS Algorithm

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