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An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments

Bagheri, Zahra M.; Cazzolato, Benjamin S.; Grainger, Steven; O'Carroll, David C. LU and Wiederman, Steven D. (2017) In Journal of Neural Engineering 14(4).
Abstract

Objective. Many computer vision and robotic applications require the implementation of robust and efficient target-tracking algorithms on a moving platform. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. Lightweight and low-powered flying insects, such as dragonflies, track prey or conspecifics within cluttered natural environments, illustrating an efficient biological solution to the target-tracking problem. Approach. We used our recent recordings from 'small target motion detector' neurons in the dragonfly brain to inspire the development of a closed-loop target detection and tracking algorithm. This model exploits facilitation, a slow build-up of response to targets... (More)

Objective. Many computer vision and robotic applications require the implementation of robust and efficient target-tracking algorithms on a moving platform. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. Lightweight and low-powered flying insects, such as dragonflies, track prey or conspecifics within cluttered natural environments, illustrating an efficient biological solution to the target-tracking problem. Approach. We used our recent recordings from 'small target motion detector' neurons in the dragonfly brain to inspire the development of a closed-loop target detection and tracking algorithm. This model exploits facilitation, a slow build-up of response to targets which move along long, continuous trajectories, as seen in our electrophysiological data. To test performance in real-world conditions, we implemented this model on a robotic platform that uses active pursuit strategies based on insect behaviour. Main results. Our robot performs robustly in closed-loop pursuit of targets, despite a range of challenging conditions used in our experiments; low contrast targets, heavily cluttered environments and the presence of distracters. We show that the facilitation stage boosts responses to targets moving along continuous trajectories, improving contrast sensitivity and detection of small moving targets against textured backgrounds. Moreover, the temporal properties of facilitation play a useful role in handling vibration of the robotic platform. We also show that the adoption of feed-forward models which predict the sensory consequences of self-movement can significantly improve target detection during saccadic movements. Significance. Our results provide insight into the neuronal mechanisms that underlie biological target detection and selection (from a moving platform), as well as highlight the effectiveness of our bio-inspired algorithm in an artificial visual system.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
insect neurophysiology, insect-inspired vision, neurorobotic, target tracking
in
Journal of Neural Engineering
volume
14
issue
4
publisher
IOP Publishing
external identifiers
  • scopus:85029732595
ISSN
1741-2560
DOI
10.1088/1741-2552/aa776c
language
English
LU publication?
yes
id
3b4200a6-9995-415f-bc5d-62e356e241d9
date added to LUP
2018-01-24 10:34:57
date last changed
2018-01-25 03:00:02
@article{3b4200a6-9995-415f-bc5d-62e356e241d9,
  abstract     = {<p>Objective. Many computer vision and robotic applications require the implementation of robust and efficient target-tracking algorithms on a moving platform. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. Lightweight and low-powered flying insects, such as dragonflies, track prey or conspecifics within cluttered natural environments, illustrating an efficient biological solution to the target-tracking problem. Approach. We used our recent recordings from 'small target motion detector' neurons in the dragonfly brain to inspire the development of a closed-loop target detection and tracking algorithm. This model exploits facilitation, a slow build-up of response to targets which move along long, continuous trajectories, as seen in our electrophysiological data. To test performance in real-world conditions, we implemented this model on a robotic platform that uses active pursuit strategies based on insect behaviour. Main results. Our robot performs robustly in closed-loop pursuit of targets, despite a range of challenging conditions used in our experiments; low contrast targets, heavily cluttered environments and the presence of distracters. We show that the facilitation stage boosts responses to targets moving along continuous trajectories, improving contrast sensitivity and detection of small moving targets against textured backgrounds. Moreover, the temporal properties of facilitation play a useful role in handling vibration of the robotic platform. We also show that the adoption of feed-forward models which predict the sensory consequences of self-movement can significantly improve target detection during saccadic movements. Significance. Our results provide insight into the neuronal mechanisms that underlie biological target detection and selection (from a moving platform), as well as highlight the effectiveness of our bio-inspired algorithm in an artificial visual system.</p>},
  articleno    = {046030},
  author       = {Bagheri, Zahra M. and Cazzolato, Benjamin S. and Grainger, Steven and O'Carroll, David C. and Wiederman, Steven D.},
  issn         = {1741-2560},
  keyword      = {insect neurophysiology,insect-inspired vision,neurorobotic,target tracking},
  language     = {eng},
  month        = {07},
  number       = {4},
  publisher    = {IOP Publishing},
  series       = {Journal of Neural Engineering},
  title        = {An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments},
  url          = {http://dx.doi.org/10.1088/1741-2552/aa776c},
  volume       = {14},
  year         = {2017},
}