Multi-AUV task assignment and path planning with ocean current based on biological inspired self-organizing map and velocity synthesis algorithm

Cao, Xiang; Zhu, Daqi

doi:10.1080/10798587.2015.1118277

Cited by 32 publications

(12 citation statements)

References 25 publications

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“…The simulation results show that the proposed algorithms are effective. The proposed methods can be expanded to parameter estimation of speech signal processing, 51,52 the self-organizing map, 53 and applied to other fields. [54][55][56][57][58][59][60][61][62][63][64][65]…”

Section: Resultsmentioning

confidence: 99%

The parameter estimation algorithms based on the dynamical response measurement data

2017

Advances in Mechanical Engineering

147

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This article studies the parameter estimation to the system response from the discrete measurement data. By constructing the dynamical rolling cost functions and using the nonlinear optimization, the gradient identification method is presented for estimating the parameters of the sine response signal with double frequency. In order to overcome the difficulty for determining the step size and deduce the influence of noises, the stochastic gradient identification method is derived to estimate the signal parameters. For the purpose of improving the accuracy, a multi-innovation stochastic gradient parameter estimation algorithm is presented using the moving window data. Finally, the simulation examples are provided to test the algorithm performance.

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Section: Resultsmentioning

confidence: 99%

The parameter estimation algorithms based on the dynamical response measurement data

2017

Advances in Mechanical Engineering

147

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“…Cao and Zhu [186] proposed an algorithm for Multi-AUV that combines ANNs and velocity synthesis (VS) to eliminate the effects of currents in a multirobot collaborative path search. Later, they mixed the bioinspired self-organizing map (BISOM) algorithm to ensure the stability of environmental modeling in [187]. Combined with the ant colony algorithm design synthesis algorithm in [9], the simulation completed the requirements of dynamic obstacle avoidance and a path search of an AUV and considerably improved the speed of the path search.…”

Section: ) Artificial Neural Network (Ann)mentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

127

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An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research. INDEX TERMS AUV, path planning, model building, path search.

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“…Xiang [47] proposed a combined biological inspired self-organizing map and a velocity synthesis algorithm. The self-organizing map neural network is used by assigning a team of AUVs [47][48][49] to achieve multiple target locations. These algorithm when combined works well in task assignment and path planning in different scenarios.…”

Section: Task Allocationmentioning

confidence: 99%

A novel bio- inspired algorithm for hunting in multi robot scenario

Agrawal

Potdar

2019

IJEECS

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In a multi-robot scenario, cooperative hunting is a key issue when a group of robots are hunting for evader/evaders and when the location of the evader is continuously changing. Cooperative hunting is addressed in this paper by proposing a novel bio inspired Corner Dragging Algorithm (CDA). Corner Dragging Algorithm operates by making an alliance of robots that drag the evader towards any one of the four corners; whichever is closest to the evader. Different shapes of obstacles are avoided during this pursuit. While developing the Corner Dragging Algorithm, we analyze the shortcomings and advantages of some of the existing algorithms including dynamic alliance and formation construction algorithm and incorporate these changes in our design to achieve improved results. Performance of the algorithm is evaluated on the basis of simulation in MATLAB.

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Multi-AUV task assignment and path planning with ocean current based on biological inspired self-organizing map and velocity synthesis algorithm

Cited by 32 publications

References 25 publications

The parameter estimation algorithms based on the dynamical response measurement data

The parameter estimation algorithms based on the dynamical response measurement data

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

A novel bio- inspired algorithm for hunting in multi robot scenario

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