Three Dimensional AUV Complete Coverage Path Planning with Glasius Bio-inspired Neural Network

Sun, Bing; Zhu, Xinlei; Zhang, Wei; Zhu, Daqi; Chu, Zhenzhong

doi:10.1007/978-3-319-97589-4_11

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…The strategy in [21] planned a reasonable collision-free coverage path through the division of labor and collaboration, so as to achieve full coverage of the same task area. Reference [22] verified the AUV CCPP algorithm based on the GBNN model. AUV in [22] can cover all designated working water areas, no matter in static environment or dynamic environment of a whole three-dimensional space, without missing or collision.…”

Section: Introductionmentioning

confidence: 95%

“…Reference [22] verified the AUV CCPP algorithm based on the GBNN model. AUV in [22] can cover all designated working water areas, no matter in static environment or dynamic environment of a whole three-dimensional space, without missing or collision.…”

Section: Introductionmentioning

confidence: 95%

“…A variant of the BNN, called Glasius Bio-inspired Neural Network (GBNN) model, was reported by [19]- [22]. Reference [19] put forward several improvements for the GBNN model to improve its dynamic performance, and the improved GBNN model was stable and feasible for realtime path planning under the fast changing environments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

2019

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With the wide application of Bioinspired Neural Network in the field of robot path planning, the environmental scale of robot path planning is getting larger, and the environmental resolution requirements are getting higher. However, with the increase of the environment size and resolution requirement, the neuronal activity value calculation cost and the time cost of the Bioinspired Neural Network will increase sharply. Aiming at this problem, this paper proposes an improved Bioinspired Neural Network path planning method based on Scaling Terrain. Using a Multi-Scale Map method and Dijkstra algorithm, the optimal path of a Coarse Scale Map is calculated. The optimal path obtained from the Coarse Scale Map is used to guide the neural network planning weights of the Fine Scale Map from the same terrain. Thus, the optimal path of the Fine Scale Map can be calculated by the improved BNN algorithm. Introducing this Multi-Scale Map Method into the Bioinspired Neural Network can greatly reduce the time cost of the Bioinspired Neural Network path planning algorithm and reduce the mathematical complexity. Simulation results in some computer integrated virtual environments further demonstrate the superiority of this method and the experimental results are encouraging.INDEX TERMS Multi-scale map method, path planning, bioinspired neural network, Dijkstra algorithm.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 95%

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A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

2019

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“…Zhu et al [278] proposed the GBNN model to deal with CPP in building the 2D grid map. Whilst [279] further built on the 3D grid map in static and dynamic environments based on the GBNN approach. Although the model has high computation cost, the robot could plan the path to cover the area under a 2D or 3D environment without collision.…”

Section: ) Neural Networkmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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“…In 2015, Yu et al proposed a hybrid search fast marching (HSFM) method to generate a threedimensional smooth path from the discrete representation of the underwater environment [118]. In 2018, Sun et al established a three-dimensional Glasius bionic neural network model to represent the three-dimensional underwater working environment, which independently planned the path according to the activity of neurons [119]. In 2019, Liu et al proposed a learning fixed-height histogram (LFHH) method based on the estimation of distribution algorithm to solve the path planning in the 3D environment with current and moving obstacles [120].…”

Section: Direction E: 3d Path Planning Algorithmsmentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

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Three Dimensional AUV Complete Coverage Path Planning with Glasius Bio-inspired Neural Network

Cited by 5 publications

References 13 publications

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

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