Paper Info
Title
Improved Glasius bio-inspired neural network for target search by multi-agents
Abstract
This paper focuses on solving the multi-agent cooperative target search problem with the demand for obtaining the maximal cumulative detection reward, given the prior target probability map and the sensor detection ability under various constraints. First, a topologically organized model of Glasius bio-inspired neural network (GBNN) is constructed individually for each agent in order to represent the searching environment. The neural activities are determined not only by the activity propagation among neurons, but also by the external input containing the single detection reward and various constraints synthetically. Then, the agent’s searching motion can be selected greedily based on the dynamic activity landscape of GBNN. With the disadvantages of propagation time delay and activity attenuation, however, the relatively global mechanism in GBNN may lead to unsatisfactory performance or even fail to avoid the local optimal problem. Hence the Gaussian mixture model (GMM) is utilized to extract the high-value subregions and compute the future detection reward quantitatively, which can be introduced into the neuron’s external excitatory input of GBNN directly. The simulation results verify the high efficiency and strong robustness of GBNN-GMM in the searching scenarios.
Year
DOI
Venue
2021
10.1016/j.ins.2021.03.056
Information Sciences
Keywords
DocType
Volume
Multi-agent cooperative target search,Cumulative detection reward,Glasius bio-inspired neural network (GBNN),Gaussian mixture model (GMM)
Journal
568
ISSN
Citations 
PageRank 
0020-0255
1
0.36
References 
Authors
0
2
Name
Order
Citations
PageRank
Peng Yao1101.61
Zhiyao Zhao2165.45