Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance

Junxiu Liu, Yifan Hua, Rixing Yang, Yuling Luo, Hao Lu, Yanhu Wang, Su Yang, Xuemei Ding, Junxiu Liu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
70 Downloads (Pure)

Abstract

Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario.
Original languageEnglish
Article number905596
Pages (from-to)1-13
Number of pages13
JournalFrontiers in Neuroscience
Volume16
Early online date30 Jun 2022
DOIs
Publication statusPublished online - 30 Jun 2022

Bibliographical note

Funding Information:
This research was partially supported by the National Natural Science Foundation of China under Grant No. 61976063 and the Guangxi Natural Science Foundation under Grant No. 2022GXNSFFA035028.

Publisher Copyright:
Copyright © 2022 Liu, Hua, Yang, Luo, Lu, Wang, Yang and Ding.

Keywords

  • Neuroscience
  • neuroanimats
  • spiking neural networks
  • reinforcement learning
  • spike-timing-dependent plasticity
  • robot

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