Cheng Hu
Bio-Inspired Embedded Vision System for Autonomous Micro-Robots: The LGMD Case
Hu, Cheng; Arvin, Farshad; Xiong, Caihua; Yue, Shigang
Abstract
In this paper, we present a new bio-inspired vision system embedded for micro-robots. The vision system takes inspiration from locusts in detecting fast approaching objects. Neurophysiological research suggested that locusts use a wide-field visual neuron called lobula giant movement detector (LGMD) to respond to imminent collisions. In this paper, we present the implementation of the selected neuron model by a low-cost ARM processor as part of a composite vision module. As the first embedded LGMD vision module fits to a micro-robot, the developed system performs all image acquisition and processing independently. The vision module is placed on top of a micro-robot to initiate obstacle avoidance behavior autonomously. Both simulation and real-world experiments were carried out to test the reliability and robustness of the vision system. The results of the experiments with different scenarios demonstrated the potential of the bio-inspired vision system as a low-cost embedded module for autonomous robots.
Citation
Hu, C., Arvin, F., Xiong, C., & Yue, S. (2017). Bio-Inspired Embedded Vision System for Autonomous Micro-Robots: The LGMD Case. IEEE Transactions on Cognitive and Developmental Systems, 9(3), 241-254. https://doi.org/10.1109/tcds.2016.2574624
Journal Article Type | Article |
---|---|
Online Publication Date | May 30, 2016 |
Publication Date | Sep 30, 2017 |
Deposit Date | May 27, 2022 |
Journal | IEEE Transactions on Cognitive and Developmental Systems |
Print ISSN | 2379-8920 |
Electronic ISSN | 2379-8939 |
Publisher | Institute of Electrical and Electronics Engineers |
Volume | 9 |
Issue | 3 |
Pages | 241-254 |
DOI | https://doi.org/10.1109/tcds.2016.2574624 |
Public URL | https://durham-repository.worktribe.com/output/1203872 |
You might also like
Editorial: Swarm neuro-robots with the bio-inspired environmental perception.
(2024)
Journal Article
Swarm flocking using optimisation for a self-organised collective motion
(2024)
Journal Article
Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring
(2023)
Journal Article
Reinforcement learning-based aggregation for robot swarms
(2023)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search