Coordination of distributed unmanned surface vehicles via model-based reinforcement learning methods

Miao, Runlong; Wang, Lingxiao; Pang, Shuo

doi:10.1016/j.apor.2022.103106

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…In [ 36 ], LoRa communication technology was utilized to establish the communication between the UAV and on-ground sensors and collect the data from the agriculture field periodically. Policy-iteration based path planning algorithm was applied in routing formation which avoids the inter-unmanned surface vehicle collision [ 37 ]. A bi-level hybridization-based metaheuristic algorithm was deployed in [ 38 ] to find the tour planning and avoid the large blind searches in complex space.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient T-Based Routing Topology for Target Tracking in Battery Operated Mobile Wireless Sensor Networks

Karunanithy

Idayachandran

Vetrivelan

et al. 2023

Sensors

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Real-time smart applications are now possible because to developments in communication and sensor technology. Wireless sensor networks (WSNs) are used to collect data from specific disaster sites, such as fire events, gas leaks, land mines, earthquake, landslides, etc., where it is necessary to know the exact location of the detected information to safely rescue the people. For instance, the detection and disposal of explosive materials is a difficult task because land mines consistently threaten human life. Here, the T-based Routing Topology (TRT) is suggested to gather data from sensors (metal detectors, Ground Penetrating Radars (GPR), Infra-Red sensors, etc.), Global Positioning System (GPS), and cameras in land mine-affected areas. Buried explosive materials can be found and located with high accuracy. Additionally, it will be simpler to eliminate bombs and reduce threats to humans. The efficiency of the suggested data collection method is evaluated using Network Simulator-2 (NS-2). Also, the proposed T-based routing topology requires a minimal number of nodes to cover the entire searching area and establish effective communication. In contrast, the number of nodes participating in the sensing area grows, as the depth of the tree increases in the existing tree topology-based data gathering. And for cluster topology, the number of nodes deployment depends on the transmission range of the sensor nodes.

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Section: Related Workmentioning

confidence: 99%

An Energy-Efficient T-Based Routing Topology for Target Tracking in Battery Operated Mobile Wireless Sensor Networks

Karunanithy

Idayachandran

Vetrivelan

et al. 2023

Sensors

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“…Similarly, in civilian fields, it can expand the scope of marine operations, improve operational efficiency, and complete complex tasks that cannot be achieved by a single ship, including coordinated search and rescue, coordinated reconnaissance, long-distance supply, maritime patrol, and coordinated transportation, among others. The formation problem is an important issue in the control of multi-USV systems [2][3][4], which requires USVs to maintain a certain relative spatial position while in motion. This plays an important role when USV clusters complete common tasks such as oil and gas exploration and maritime search and rescue [5].…”

Section: Introductionmentioning

confidence: 99%

Algebraic-Connectivity-Based Multi-USV Distributed Formation Method via Adding a Reverse Edge

et al. 2023

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This paper concerns the formation problem in multi-USV cluster formation containment tracking tasks with a special topology. A topology reconstruction method was proposed that enables the followers’ formation to be dispersed while achieving the fastest convergence rate for the system. This topology structure is based on tree topology and DAG (directed acyclic graph) local structure stem as prototypes, using the principle of adding reverse edges on the stem to reduce algebraic connectivity. By adding a reverse edge to obtain a more dispersed formation, a method for selecting appropriate reverse edges was achieved. Through relevant theoretical quantitative and qualitative analysis, it was demonstrated that adding this reverse edge can enable the system to achieve the fastest convergence rate. Finally, through simulation experiments, it was verified that the selected reverse edge can optimize the formation of followers and achieve the fastest convergence rate.

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“…A continuous hybrid model-free RL method based on a deterministic policy gradient [31] has been applied to adjust the framework of SMC parameters to control the course of AUVs. A model-based RL method for organizing unmanned surface vessels (USV) when searching for multiple moving targets has been proposed [32]. Zheng and Liu [33] improved the deep deterministic policy gradient (DDPG) method, added a mean field network to maximize multi-agent return, and achieved good crowd-evacuation path-planning results in a crowded simulation system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sampling Path Planning for a 3D Marine Observation Platform Based on Evolutionary Deep Reinforcement Learning

Zhang,

Liu,

Zhou

2023

JMSE

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Adaptive sampling of the marine environment may improve the accuracy of marine numerical prediction models. This study considered adaptive sampling path optimization for a three-dimensional (3D) marine observation platform, leading to a path-planning strategy based on evolutionary deep reinforcement learning. The low sampling efficiency of the reinforcement learning algorithm is improved by evolutionary learning. The combination of these two components as a new algorithm has become a current research trend. We first combined the evolutionary algorithm with different reinforcement learning algorithms to verify the effectiveness of the combination of algorithms with different strategies. Experimental results indicate that the fusion of the two algorithms based on a maximum-entropy strategy is more effective for adaptive sampling using a 3D marine observation platform. Data assimilation experiments indicate that adaptive sampling data from a 3D mobile observation platform based on evolutionary deep reinforcement learning improves the accuracy of marine environment numerical prediction systems.

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Coordination of distributed unmanned surface vehicles via model-based reinforcement learning methods

Cited by 17 publications

References 28 publications

An Energy-Efficient T-Based Routing Topology for Target Tracking in Battery Operated Mobile Wireless Sensor Networks

An Energy-Efficient T-Based Routing Topology for Target Tracking in Battery Operated Mobile Wireless Sensor Networks

Algebraic-Connectivity-Based Multi-USV Distributed Formation Method via Adding a Reverse Edge

Adaptive Sampling Path Planning for a 3D Marine Observation Platform Based on Evolutionary Deep Reinforcement Learning

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