Multi-UAV Coverage Path Planning Based on Hexagonal Grid Decomposition in Maritime Search and Rescue

Cho, Sung Won; Park, Jinhyoung; Park, Hyun Ji; Kim, Seong–Min

doi:10.3390/math10010083

Cited by 28 publications

(20 citation statements)

References 45 publications

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“…Thomas et al proposed a novel metamodel to estimate the mission success possibility for a SAR task with the support of a supervised learning method [28]. Similar studies can be found in [29][30][31]. We find that the particle swarm optimization (PSO) method is commonly used to determine the optimization deployment strategy of maritime rescue facilities.…”

Section: Introductionmentioning

confidence: 58%

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

Sun

Ling

Chen

et al. 2022

JMSE

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Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.

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Section: Introductionmentioning

confidence: 58%

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

Sun

Ling

Chen

et al. 2022

JMSE

View full text Add to dashboard Cite

show abstract

“…Grid-based approaches [9][10][11][12][13][14][15][16][17][18] mainly divide the area into sub-areas to solve dynamic area coverage problems. In [9], Choset presented a strategy dividing the space into cell regions covered with back-andforth motions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Studies in [10][11][12] use multiple vehicles that focused on partitioning the area into sub-areas and then each sub-area assigned to a vehicle. Cho et al [13] proposed a method which creates a search path to cover an area. A mixed-integer linear programming (MILP) model has been used to determine a coverage path planning for visiting all generated nodes in the shortest amount of time.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

Aydemir¹,

Çetin²

2023

Computer Systems Science and Engineering

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Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their global observations by gathering information about the environment by connecting with other agents. The connectivity provides a consensus for the decision-making process, while each agent takes decisions. At each step, agents acquire all reachable agents' states, determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area, respectively. The method was tested in a multi-agent actor-critic simulation platform. In the study, it has been considered that each UAV has a certain communication distance as in real applications. The results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.

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“…Additionally, because of their excellent mobility and high flexibility, UAVs can operate in hazardous environments or in places that are inaccessible to people or ground-based equipment, such as during forest fires or search-and-rescue operations in the water. Uncertainty in search and rescue is somewhat diminished by the UAVs' operational effectiveness [82]. The UAVs can conduct a thorough search in a short amount of time thanks to their air superiority and information transmission capabilities.…”

Section: Seek and Rescuementioning

confidence: 99%

Design Configuration and Technical Application of Rotary-Wing Unmanned Aerial Vehicles

Zhao¹,

Li²

2022

MITS

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Due to their advantages in hovering, takeoff and landing adaptability, maneuverability, and other factors, rotary-wing unmanned aerial vehicles (UAVs) are widely applied across many different fields. The UAVs' design and configuration can be quite flexible to fit diverse operation conditions. The major goal of innovations in rotary-wing UAVs is to lower operating risk and expense by optimizing payload and structure layout. This study examines three aspects of rotary-wing UAV design and evolution: the number and arrangement of rotors, hybrid-wing-based UAVs, and configuration and loading structures. The most current advancements of UAV applications in crucial industries, including agriculture, fire rescue, inspection and monitoring, and aerial logistics, are then thoroughly examined. Finally, the authors discussed the prospective uses for rotary-wing UAV design in the future.

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Multi-UAV Coverage Path Planning Based on Hexagonal Grid Decomposition in Maritime Search and Rescue

Cited by 28 publications

References 45 publications

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

Design Configuration and Technical Application of Rotary-Wing Unmanned Aerial Vehicles

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