Journal
NEUROCOMPUTING
Volume 460, Issue -, Pages 1-8Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2021.07.020
Keywords
Target tracking; k-winners-take-all; Recurrent neural networks; Finite-time convergence; Global stability
Categories
Funding
- National Key RAMP
- D Program of China [2018YFB1306601]
- Light of West China Program, Chinese Academy of Sciences
- Chongqing Science and Technology Bureau [cstc2020jcyj-zdxmX0028, cstc2018jszx-cyzdX0041]
Ask authors/readers for more resources
In this paper, a k-winners-take-all (k-WTA) neural network is designed and applied to a task assignment problem in a multi-robot competitive target tracking scenario. The proposed neural network features a single neuron and a non-hard-limiting activation function, which greatly simplifies the model structure and reduces the computation cost. The stability and convergence property of the neural network is theoretically analyzed, and simulations demonstrate its effectiveness in tracking targets moving at higher speeds than the tracking robots.
A k-winners-take-all (k-WTA) neural network is designed and applied to a task assignment problem in a multi-robot competitive target tracking scenario in this paper. The proposed neural network features a single neuron and a non-hard-limiting activation function, which greatly simplifies the model structure and reduces the computation cost. This neural network has finite-time convergence property and can be applied to real-time situations. The stability and convergence property of the neural network is theoretically analyzed. Simulations of handling a situation that a target moves at a higher speed than tracking robots are conducted to demonstrate the effectiveness of the designed scheme. (c) 2021 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available