Coverage path planning for maritime search and rescue using reinforcement learning

Ai, Bo; Jia, Maoxin; Xu, Hanwen; Xu, Jin; Wen, Zhen; Li, Benshuai; Zhang, Dan

doi:10.1016/j.oceaneng.2021.110098

Cited by 55 publications

(23 citation statements)

References 21 publications

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“…Coverage Path Planning (CPP) is the process of generating a feasible path for completely covering an area interested [7]. It is widely used in seabed terrain matchings [8], mine countermeasures [9], search and rescues [4], pipeline inspections [10], hydrographic surveys [11], and so on. Generally, CPP algorithms for surface SAR can be used underwater [2].…”

Section: Introductionmentioning

confidence: 99%

“…[20] was based on sensor information to dynamically adapt to unknown environments. Considering the dynamic marine environment, Ai et al [4] applied reinforcement learning to solve the CPP problem. However, besides real-time sensor information, the impact of prior information in real applications on the CPP problem is important but rarely discussed.…”

Section: Introductionmentioning

confidence: 99%

“…Maritime Search and Rescue (SAR) is a challenging task in dangerous and unknown environments [1]. The purpose of maritime SAR is to locate and rescue the target on the surface [2,3] or underwater [4]. For the sake of high efficiency and safety in underwater SAR missions, people have utilised a Side-Scan Sonar (SSS) mounted on an Autonomous Unmanned Vehicle (AUV) for wide-area searching for many years [5,6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar

Cai

Chen

Yan

et al. 2022

IET Radar Sonar & Navi

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The coverage path planning (CPP) technique attracts growing interest in studies on underwater search and rescue (SAR) conducted with an autonomous underwater vehicle (AUV) equipped with a side‐scan sonar (SSS). In SAR missions, prior information is crucial. Aiming at the underwater SAR mission with prior information, a new coverage path planner (SAR‐A*) is proposed. The ultimate goal is to generate a feasible path for completely covering the task area and preferentially visiting more valuable cells with fewer turns. First, the whole task area is decomposed into hexagon cells as waypoints to be visited for complete coverage. Second, the probability of discovering the target is obtained according to the target presence probability and the SSS detection ability. Under the assumption of prior target information, the target presence probability is modelled as a two‐dimension Gaussian distribution based on predicted target locations or trajectories. Then, an optimal next‐waypoint selection process is formulated as a multi‐objective decision‐making problem and solved by the weighted metric method. Finally, simulation and experimental results demonstrate that the generated path can improve the cumulative probability of discovering the target with fewer turns.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar

Cai

Chen

Yan

et al. 2022

IET Radar Sonar & Navi

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show abstract

“…It is noted that seaborne transportation assumes a large amount of cargo transiting tasks due to the advantages of lower freight, higher capacity, convenience, etc. [1,2]. Traffic density in both coastal and inland channels has experienced a drastic expansion in recent years, whilst ship berthing and departure activities have rapidly increased as well [3,4].…”

Section: Introductionmentioning

confidence: 99%

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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“…Underwater search and rescue is one of the important tasks of intelligent maritime search and rescue [1,2] . It is an important application field for underwater vehicles, artificial intelligent information processing, and under-water acoustic communication, as well as a multidisciplinary field for new technology.…”

Section: Introductionmentioning

confidence: 99%

Development and experimental verification of search and rescue ROV

Sun¹,

Pang²,

Chen³

et al. 2022

Intell Robot

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This paper presents the design of a new type of search and rescue remotely operated vehicle (ROV) system. The goal is to achieve the underwater target search and detection and small target capture and rescue operation requirements. First, the overall design of the whole underwater surface system and the layout design of the propulsion system are given. On this basis, the ROV frame structure, electronic cabin, and power cabin are designed and analyzed. To accomplish the grasping task, a grasping hand is designed based on a multifunctional manipulator to achieve underwater grasping. To make the ROV more intelligent, different kinds of underwater object detection and tracking methods are adopted and analyzed. Finally, it was tested in a pool and the sea to verify the reliability and stability of the designed search and rescue ROV.

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Coverage path planning for maritime search and rescue using reinforcement learning

Cited by 55 publications

References 21 publications

A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar

A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar

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

Development and experimental verification of search and rescue ROV

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