Mathematical Modeling and Analysis of Multirobot Cooperative Hunting Behaviors

Song, Yong; Li, Yibin; Li, Caihong; Ma, Xin

doi:10.1155/2015/184256

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The algorithm improves the hunting success rate through collaboration. Song et al 6 established a steady-state mathematical model for multi-robot hunting behavior and improved the stability of target hunting. Denzinger and Fuchs 7 proposed a target hunting strategy that combines neighborhood rules and genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

Cao

2019

International Journal of Advanced Robotic Systems

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As one of the challenging tasks of multiple autonomous underwater vehicles systems, the realization of target hunting is the great significance. The multiple autonomous underwater vehicle target hunting is studied in this article. In some research, because the hunting members cannot reach the hunting point at the same time, the hunting time is long or the target escapes. To improve the efficiency of the target hunting, the leader–follower formation algorithm is introduced. Firstly, the task is assigned based on the distance between the autonomous underwater vehicle and the target. Then, the autonomous underwater vehicles with the same task are formed based on leader–follower mode, and the formation is kept to track the target. In the final capture phase, multiple autonomous underwater vehicle system use angle matching algorithm to round up target. The simulation results show that the proposed algorithm can effectively accomplish the target hunting task, save the hunting time, and avoid the target escape. Compared with the bioinspired neural network algorithm, the proposed algorithm shows better performance.

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Section: Introductionmentioning

confidence: 99%

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

Cao

2019

International Journal of Advanced Robotic Systems

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“…This algorithm can decide the hunter's action (speed and direction) to hunt for the evaders. Song et al (2015) studied the hunting behaviour of multiple robots and proposed a mathematical model. The output of this model was stable, but the chase process was not studied because it assumed that the robots complete hunting when all of them detect the target.…”

Section: Introductionmentioning

confidence: 99%

Multi-AUV Cooperative Hunting Control with Improved Glasius Bio-inspired Neural Network

Chen

Zhu

2018

J. Navigation

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Cooperative hunting with multiple Autonomous Underwater Vehicles (AUVs) not only needs the AUVs to cooperate, but also demands real-time path planning to catch up with evading targets. In this paper a time-based alliance mechanism to form efficient dynamic hunting alliances is proposed. After that, during the active hunting stage, an improved neural network model based on a Glasius Bio-inspired Neural Network (GBNN) is presented for path planning to immediately achieve tracking of an intelligent target. This study shows that the improved GBNN model has good performance in real-time hunting path planning. From the simulation studies as described in this paper, both the hunting alliance formation mechanism and the proposed real-time hunting path planning strategy show their advantages. The results show that the improved GBNN model proposed in this paper can work well in the control of multiple AUVs to hunt for intelligent evading targets in environments containing obstacles.

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An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting

Liu

et al. 2017

Int. J. Fuzzy Syst.

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Mathematical Modeling and Analysis of Multirobot Cooperative Hunting Behaviors

Cited by 5 publications

References 16 publications

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

Multi-AUV Cooperative Hunting Control with Improved Glasius Bio-inspired Neural Network

An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting

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