Journal article
Ibrahim Youssef, Mehmet Mutlu, Behzad Bayat, A. Crespi, Simon Hauser, J. Conradt, Alexandre Bernardino, A. Ijspeert
IEEE Robotics and Automation Letters, 2020
IEEE Robotics and Automation Letters, 2020
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APA
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Youssef, I., Mutlu, M., Bayat, B., Crespi, A., Hauser, S., Conradt, J., … Ijspeert, A. (2020). A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras. IEEE Robotics and Automation Letters.
Chicago/Turabian
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Youssef, Ibrahim, Mehmet Mutlu, Behzad Bayat, A. Crespi, Simon Hauser, J. Conradt, Alexandre Bernardino, and A. Ijspeert. “A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras.” IEEE Robotics and Automation Letters (2020).
MLA
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Youssef, Ibrahim, et al. “A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras.” IEEE Robotics and Automation Letters, 2020.
BibTeX Click to copy
@article{ibrahim2020a,
title = {A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras},
year = {2020},
journal = {IEEE Robotics and Automation Letters},
author = {Youssef, Ibrahim and Mutlu, Mehmet and Bayat, Behzad and Crespi, A. and Hauser, Simon and Conradt, J. and Bernardino, Alexandre and Ijspeert, A.}
}
Abstract
The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a challenge in most biomimetic aquatic robots. Event-driven sensors use a biologically inspired sensing strategy to eliminate the need for complete frame capture. Systems employing event-driven cameras enjoy reduced latencies, power consumption, bandwidth, and benefit from a large dynamic range. However, to the best of our knowledge, no work has been done to evaluate the performance of these devices in underwater robotics. This work proposes a robotic lamprey design capable of supporting computer vision, and uses this system to validate a computational neuron model for driving anguilliform swimming. The robot is equipped with two different types of cameras: frame-based and event-based cameras. These were used to stimulate the neural network, yielding goal-oriented swimming. Finally, a study is conducted comparing the performance of the computational model when driven by the two different types of camera. It was observed that event-based cameras improved the accuracy of swimming trajectories and led to significant improvements in the rate at which visual inputs were processed by the network.