Multi-AUV Cooperative Navigation Algorithm Based on Temporal Difference Method

Ren, Ranzhen; Zhang, Lichuan; Liu, Lu; Wu, Dong; Pan, Guang; Huang, Qiaogao; Zhu, Yuchen; Liu, Yazhe; Zhu, Zixiao

doi:10.3390/jmse10070955

Cited by 11 publications

(2 citation statements)

References 35 publications

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“…Therefore, this study analyzes the observability of a singlemaster AUV cooperative navigation system, conducts the observation error analysis, and derives the error propagation equation for a multi-AUV cooperative navigation system. Based on the proof given in [25], the observability of a multi-AUV cooperative navigation system can be proven. A multi-AUV cooperative navigation system is observable, and the relationship between the system's observability and the absolute difference in the azimuth angle of adjacent distance observations can be demonstrated.…”

Section: Observability and Observation Errormentioning

confidence: 99%

An Efficient Multi-AUV Cooperative Navigation Method Based on Hierarchical Reinforcement Learning

Zhu,

Zhang,

Liu

et al. 2023

JMSE

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Positioning errors introduced by low-precision navigation devices can affect the overall accuracy of a positioning system. To address this issue, this paper proposes a master-slave multi-AUV collaborative navigation method based on hierarchical reinforcement learning. First, a collaborative navigation system is modeled as a discrete semi-Markov process with defined state and action sets and reward functions. Second, trajectory planning is performed using a hierarchical reinforcement learning-based approach combined with the polar Kalman filter to reduce the positioning error of slave AUVs, realizing collaborative navigation in multi-slave AUV scenarios. The proposed collaborative navigation method is analyzed and validated by simulation experiments in terms of the relative distance between the master and slave AUVs and the positioning error of a slave AUV. The research results show that the proposed method can not only successfully reduce the observation and positioning errors of slave AUVs in the collaborative navigation process but can also effectively maintain the relative measurement distance between the master and slave AUVs within an appropriate range.

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Section: Observability and Observation Errormentioning

confidence: 99%

An Efficient Multi-AUV Cooperative Navigation Method Based on Hierarchical Reinforcement Learning

Zhu,

Zhang,

Liu

et al. 2023

JMSE

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the case of marine applications, cooperative navigation has been extensively studied for various types of multiple autonomous systems. Multiple AUVs have been the subject of several studies on cooperative navigation [20]- [22]. Both heterogeneous and homogeneous structures have been investigated for the cooperative navigation of multiple AUVs [23].…”

Section: Introductionmentioning

confidence: 99%

Multiple Autonomous Surface Vehicles for Autonomous Cooperative Navigation Tasks in a Marine Environment: Development and Preliminary Field Tests

Park¹,

Kang²,

Lee³

et al. 2023

IEEE Access

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This paper presents the development and field testing of multiple autonomous surface vehicles (ASVs) for autonomous cooperative navigation tasks in marine environments. The developed ASVs were designed in a catamaran hull form, and various systems related to autonomous functionalities, including the electrical propulsion, wireless communication, and guidance, navigation, and control (GNC) systems, were integrated with the situational awareness sensor system. Because not all of the navigation and situational awareness sensor data can be shared owing to the large network load, the network communication and data protocol structure was designed to enhance the data management efficiency. Furthermore, for cooperative navigation between multiple ASVs, the network communication system was synchronously shared with motion information through a robot operating system (ROS) multi-master system. In particular, an autonomy framework with cooperative navigation and control was designed to enable autonomous cooperative maneuvering with multiple ASVs in a global navigation satellite system (GNSS)-denied environment. Furthermore, a cooperative navigation and control approach based on relative geometric information between vehicles was implemented in the framework, which incorporated the capabilities of following a predefined path and maintaining a specific formation based on the relative distance between ASVs. Moreover, to demonstrate the essential maneuvering and functional capabilities of the cooperative navigation and control approach of the developed ASVs, preliminary field tests were conducted in an inland water environment, and the test results are discussed in this paper.

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Anti-disturbance cooperative formation containment control for multiple autonomous underwater vehicles with actuator saturation

Cui¹,

Xu²,

Xing³

et al. 2022

Ocean Engineering

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Multi-AUV Cooperative Navigation Algorithm Based on Temporal Difference Method

Cited by 11 publications

References 35 publications

An Efficient Multi-AUV Cooperative Navigation Method Based on Hierarchical Reinforcement Learning

An Efficient Multi-AUV Cooperative Navigation Method Based on Hierarchical Reinforcement Learning

Multiple Autonomous Surface Vehicles for Autonomous Cooperative Navigation Tasks in a Marine Environment: Development and Preliminary Field Tests

Anti-disturbance cooperative formation containment control for multiple autonomous underwater vehicles with actuator saturation

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