Competent vision and navigation systems: From flying insects to autonomously navigating robots

Srinivasan, M., Thurrowgood, S. and Soccol, D. (2009) Competent vision and navigation systems: From flying insects to autonomously navigating robots. IEEE Robotics and Automation Magazine, 16 3: 59-71. doi:10.1109/MRA.2009.933627

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Author Srinivasan, M.
Thurrowgood, S.
Soccol, D.
Title Competent vision and navigation systems: From flying insects to autonomously navigating robots
Journal name IEEE Robotics and Automation Magazine   Check publisher's open access policy
ISSN 1070-9932
1558-223X
Publication date 2009-09
Year available 2009
Sub-type Article (original research)
DOI 10.1109/MRA.2009.933627
Open Access Status
Volume 16
Issue 3
Start page 59
End page 71
Total pages 13
Editor K. P. Valavanis
Place of publication Piscataway, NJ, United States
Publisher I E E E
Collection year 2010
Language eng
Subject C1
080106 Image Processing
170112 Sensory Processes, Perception and Performance
010202 Biological Mathematics
970106 Expanding Knowledge in the Biological Sciences
970109 Expanding Knowledge in Engineering
Formatted abstract
In this article, we describe how flying insects use vision for guidance, especially in the contexts of regulating flight speed, negotiating narrow gaps, avoiding obstacles, and performing smooth landings. We show that many of these maneuvers, which were traditionally believed to involve relatively complex and high-level perception, can be achieved through the use of low-level cues and relatively simple computation. We also describe tests of the effectiveness of some of these strategies for autonomous guidance of small-scale terrestrial and aerial vehicles in the contexts of corridor navigation, altitude control, and terrain following and landing. We also describe a novel, mirror- based imaging system that is tailored for these tasks and facilitates the requisite visual computations.
Keyword Vision
Insects
Aerial robotics
Biologically inspired robots
Landing response
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
Queensland Brain Institute Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 18 times in Scopus Article | Citations
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Created: Tue, 09 Feb 2010, 13:58:19 EST by Debra McMurtrie on behalf of Queensland Brain Institute