Optimizing cooperative cognitive search and rescue UAVs

Rahmes, Mark; Chester, David B.; Hunt, Jodie; Chiasson, Basil

doi:10.1117/12.2305928

Cited by 16 publications

(10 citation statements)

References 2 publications

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“…Authors in [35] addressed the maritime SAR problem using cognitive and automated UAVs. They applied cooperative game theory to enable cognitive multi-UAVs, and tried to achieve their goal based on UAVs with on-board computers and with pre-knowledge of victims last seen locations.…”

Section: Literature Reviewmentioning

confidence: 99%

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

2019

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In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78%-100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it finds more survivors and rescues them faster than the other algorithms, with an average of 55%∼77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% confidence for a one-month mission time.INDEX TERMS Autonomous agents, drones, search and rescue, unmanned aerial vehicles.

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Section: Literature Reviewmentioning

confidence: 99%

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

2019

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“…M. Zhang et al proposed SOT-NET [9]. Due to the characteristics of low cost, sensitivity, intelligence, and autonomy, unmanned aerial vehicles (UAVs) have gradually been used in the maritime search and rescue field [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

YOLOv5s maritime distress target detection method based on swin transformer

Liu,

Qi,

et al. 2024

IET Image Processing

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In recent years, the task of maritime emergency rescue has increased, while the cost of time for traditional methods of search and rescue is pretty long with poor effect subject to the constraints of the complex circumstances around the sea, the effective conditions, and the support capability. This paper applies deep learning and proposes a YOLOv5s‐SwinDS algorithm for target detection in distress at sea. Firstly, the backbone network of the YOLOv5s algorithm is replaced by swin transformer, and a multi‐level feature fusion module is introduced to enhance the feature expression ability for maritime targets. Secondly, deformable convolutional networks v2 (DCNv2) is used instead of traditional convolution to improve the recognition capability for irregular targets when the neck network features are output. Finally, the CIoU loss function is replaced with SIoU to reduce the redundant box effectively while accelerating the convergence and regression of the predicted box. Experimenting on the publicly dataset SeaDronesSee, the , , and of YOLOv5s‐SwinDS model are 87.9%, 75.8%, 79.1% and 42.9%, respectively, which get higher results than the original YOLOv5s model, the YOLOv7 series of models, and the YOLOv8 series of models. The experiments verifies that the algorithm has good performance in detecting maritime distress targets.

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“…Conventional approaches, such as direct onboard inspections, suffer from low monitoring efficiency, low sampling ratios, and operational complexity. Thirdly, drone technology has been integrated into marine rescue platforms, successfully locating and planning paths to rescue targets [27][28][29]. The three main types of UAVs used in marine rescue are maritime search and rescue UAVs, high-endurance UAVs, and ground-effect UAVs.…”

Section: Introductionmentioning

confidence: 99%

Applications, Evolutions, and Challenges of Drones in Maritime Transport

Wang,

Zhou,

Xing

et al. 2023

JMSE

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The widespread interest in using drones in maritime transport has rapidly grown alongside the development of unmanned ships and drones. To stimulate growth and address the associated technical challenges, this paper systematically reviews the relevant research progress, classification, applications, technical challenges, and possible solutions related to the use of drones in the maritime sector. The findings provide an overview of the state of the art of the applications of drones in the maritime industry over the past 20 years and identify the existing problems and bottlenecks in this field. A new classification scheme is established based on their flight characteristics to aid in distinguishing drones’ applications in maritime transport. Further, this paper discusses the specific use cases and technical aspects of drones in maritime rescue, safety, navigation, environment, communication, and other aspects, providing in-depth guidance on the future development of different mainstream applications. Lastly, the challenges facing drones in these applications are identified, and the corresponding solutions are proposed to address them. This research offers pivotal insights and pertinent knowledge beneficial to various entities such as maritime regulatory bodies, shipping firms, academic institutions, and enterprises engaged in drone production. This paper makes new contributions in terms of the comprehensive analysis and discussion of the application of drones in maritime transport and the provision of guidance and support for promoting their further development and integration with intelligent transport.

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Optimizing cooperative cognitive search and rescue UAVs

Cited by 16 publications

References 2 publications

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

YOLOv5s maritime distress target detection method based on swin transformer

Applications, Evolutions, and Challenges of Drones in Maritime Transport

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