Search using a swarm of unmanned aerial vehicles

Sulak, Viktor; Kotuliak, Ivan; Čičák, Pavel

doi:10.1109/iceta.2017.8102534

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…UAVs are crucial in many real-world applications [29], including traffic monitoring, extending wireless coverage, search and rescue, geographic mapping, precision crop monitoring, and shipping [30]. UAVs are often referred to as drones.…”

Section: Fanet Overview and Related Workmentioning

confidence: 99%

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Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

et al. 2023

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Flying Ad hoc Network (FANET) is a self-organizing wireless network that constitutes swarms of flying nodes, namely Unmanned Aerial Vehicles (UAV), and communicates in close proximity. It has various distinguishing characteristics that set it apart from other ad hoc networks, posing some issues, particularly in routing. UAVs are highly dynamic and have frequent topology changes. Hence, the network urges an efficient routing technique to coordinate the node swarms and enhance the evaluation metrics of the network. The biological behavior of various living organisms, such as animals, insects, microbes, and humans, inspires researchers to solve various routing problems in ad hoc networks. Decentralized self-organized swarms of UAVs closely resemble the biological system. Therefore, the Bio-Inspired Algorithms (BIA) resolve a wide range of routing challenges in FANET. A Systematic Literature Review (SLR) is adopted to survey FANET routing methods based on non-hybrid and hybrid BIAs to properly comprehend the existing bio-inspired strategies used in FANET routing. The review will be beneficial for the researchers in the specified area. To our knowledge, no SLR has been conducted about the FANET routing protocol that employs BIA. This paper examines (1) the characteristics and features of existing routing algorithms, (2) the need of both non-hybrid and hybrid BIA for effective and optimal routing, (3) an analysis of the method's simulation tools, evaluation metrics, and mobility models, (4) the current issues and scope of the study related to the specified method.INDEX TERMS Bio-Inspired Algorithm (BIA), Flying Ad hoc Networks (FANET), Metaheuristic optimization, Systematic Literature Review (SLR), Unmanned Aerial Vehicle (UAV).

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Section: Fanet Overview and Related Workmentioning

confidence: 99%

“…RQ1 is addressed in this section. Every FANET mission or application is distinct from others, and these differences are considered when choosing a suitable working approach [30]. Within this SLR's collected data, there are 16 papers that focus on discussing the properties of FANET.…”

Section: A: Fanet's Routing Characteristicsmentioning

confidence: 99%

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

et al. 2023

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“…This problem relates to ensuring that every area in the environment is covered by the flight path of the UAV-CPP provides a solution for guiding the path planning of the UAV. Due to the limitations of a single UAV in terms of flight distance, large-area surveillance, and search capabilities, UAV swarms composed of multiple UAVs have become increasingly valuable in UAV applications [12]. Compared to individual UAVs, UAV swarms offer significant size and coordination advantages that can increase the effectiveness and reliability of mission accomplishment [13].…”

Section: Introductionmentioning

confidence: 99%

Efficient Multi-UAV Path Planning for Collaborative Area Search Operations

Lee

2023

Applied Sciences

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Efficient UAV path-planning algorithms significantly improve inspection efficiency and reduce costs. However, due to the limitation of battery capacity, the endurance of existing UAVs is limited, making it difficult for them to directly undertake information collection, cruising, and inspection tasks over large work areas. This paper considers the problem of path allocation for multiple UAVs to minimize work time and reports research on multi-UAV (unmanned aerial vehicle) multi-task long-duration operation path planning. We propose a multi-UAV collaborative search algorithm based on the greedy algorithm (MUCS-GD) and a multi-UAV collaborative search algorithm based on the binary search algorithm (MUCS-BSAE), and later apply two UAV collaborative search algorithms to five UAV flight paths: (1) a snake curve path, (2) a “square wave signal” curve path, (3) a Peano curve path, (4) a Hilbert curve path, and (5) a Moore curve path, and compare the simulation results. We found that the performance of MUCS-BSAE was better than that of MUCS-GD in all of the above flight paths. In addition, the path with the “square wave signal” curve was the near-optimal path among all the flight paths. Finally, we improved the MUCS-BSAE applied on the “square wave signal” curve path and obtained an improved collaborative search algorithm for multiple UAVs based on the binary search algorithm (IMUCS-BSAE), which further reduced the working time of the UAV.

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