Bio-Inspired Neural Network-Based Optimal Path Planning for UUVs Under the Effect of Ocean Currents

Zhu, Danjie; Yang, Simon X.

doi:10.1109/tiv.2021.3082151

Cited by 41 publications

(11 citation statements)

References 22 publications

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“…The bio-inspired neural network algorithm continuously updates the state of neurons by transmitting the information through the network to give an instant reaction and reduces the complexity by limiting the searching area to a certain range. Therefore, the bio-inspired neural network path planning utilizes the preserved information in the neurons to update its planning design and meanwhile adjust the network on time such that it suits well in the dynamic underwater environment, providing an efficient and high adaptive approach for the UUV path planning [61].…”

Section: Artificial Potential Field Methodsmentioning

confidence: 99%

“…(1) Decompose the surrounding area into nonoverlapping but connected cells (2) Address the optimal path between the origin and the destination cells without collisions Artificial Potential Field [41][42][43][44][45][46][47] (1) Predefine a virtual artificial potential field (2) Assume the destination provides the attractive force while obstacles generate repulsive force to the vehicle (3) Address the optimal path for the vehicle through the field descending route Intelligent Path Planning Algorithms [48][49][50][51][52][55][56][57][58][59][60][61][62][63][64] (GA, ACO, Fuzzy logic, NN, and RL)…”

Section: Logic Benefits Drawbacksmentioning

confidence: 99%

See 1 more Smart Citation

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Zhu¹,

Yan²,

Yang³

2022

Intell Robot

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The motion planning and tracking control techniques of unmanned underwater vehicles (UUV) are fundamentally significant for efficient and robust UUV navigation, which is crucial for underwater rescue, facility maintenance, marine resource exploration, aquatic recreation, etc. Studies on UUV motion planning and tracking control have been growing rapidly worldwide, which are usually sorted into the following topics: task assignment of the multi-UUV system, UUV path planning, and UUV trajectory tracking. This paper provides a comprehensive review of conventional and intelligent technologies for motion planning and tracking control of UUVs. Analysis of the benefits and drawbacks of these various methodologies in literature is presented. In addition, the challenges and prospects of UUV motion planning and tracking control are provided as possible developments for future research.

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Section: Artificial Potential Field Methodsmentioning

confidence: 99%

Section: Logic Benefits Drawbacksmentioning

confidence: 99%

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Zhu¹,

Yan²,

Yang³

2022

Intell Robot

Self Cite

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“…AI technology is required to manipulate the UUV body by adjusting the speed for surge, sway, heave, and the angular speed for pitch and yaw when controlling a UUV. UUV control is a subject that is being intensely addressed [ 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 ]. Adjusting the UUV body underwater is required, where communication is difficult, and the UUV should be recovered well at a limited communication bandwidth.…”

Section: Intelligencementioning

confidence: 99%

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bae

Hong

2023

Sensors

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With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

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“…The environmental disturbances of the underwater area, such as currents, create inevitable influences on the AUV path planning. A current effect-eliminated bio-inspired neural network was proposed to guide the AUV navigation considering the effect of currents [70] . A current correcting component was incorporated with the bio-inspired neural network to generate the paths.…”

Section: Navigationmentioning

confidence: 99%

Bio-inspired intelligence with applications to robotics: a survey

Li¹,

Xu²,

Zhu³

et al. 2021

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In the past decades, considerable attention has been paid to bio-inspired intelligence and its applications to robotics. This paper provides a comprehensive survey of bio-inspired intelligence, with a focus on neurodynamics approaches, to various robotic applications, particularly to path planning and control of autonomous robotic systems. Firstly, the bio-inspired shunting model and its variants (additive model and gated dipole model) are introduced, and their main characteristics are given in detail. Then, two main neurodynamics applications to real-time path planning and control of various robotic systems are reviewed. A bio-inspired neural network framework, in which neurons are characterized by the neurodynamics models, is discussed for mobile robots, cleaning robots, and underwater robots. The bio-inspired neural network has been widely used in real-time collision-free navigation and cooperation without any learning procedures, global cost functions, and prior knowledge of the dynamic environment. In addition, bio-inspired backstepping controllers for various robotic systems, which are able to eliminate the speed jump when a large initial tracking error occurs, are further discussed. Finally, the current challenges and future research directions are discussed in this paper.

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Bio-Inspired Neural Network-Based Optimal Path Planning for UUVs Under the Effect of Ocean Currents

Cited by 41 publications

References 22 publications

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bio-inspired intelligence with applications to robotics: a survey

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