Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments. (20th November 2015)

• Record Type: Journal Article
• Authors: Zhu, Daqi; Lv, Ruofan; Cao, Xiang; Yang, Simon X.
• Is Part Of: International journal of advanced robotic systems. Volume 12:Number 11(2015)
• Publication Date: 2015-11-20
• Subjects: Multi-AUV (Autonomous Underwater Vehicle) -- Bio-Inspired Neural Network Algorithm -- Hunting -- Path Planning; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/61555
• Languages: English
• ISSNs: 1729-8806

Abstract

The multi-AUV hunting problem is one of the key issues in multi-robot system research. In order to hunt the target efficiently a new hunting algorithm based on a bio-inspired neural network has been proposed in this paper. Firstly, the AUV's working environment can be represented, based on the biological-inspired neural network model. There is one-to-one correspondence between each neuron in the neural network and the position of the grid map in the underwater environment. The activity values of biological neurons then guide the AUV's sailing path and finally the target is surrounded by AUVs. In addition, a method called negotiation is used to solve the AUV's allocation of hunting points. The simulation results show that the algorithm used in the paper can provide rapid and highly efficient path planning in the unknown environment with obstacles and non-obstacles.