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Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network

Moeys, Diederik, Federico, Corradi, Kerr, Emmett, Vance, Philip, Das, Gautham, Daneil, Neil, Kerr, Dermot and Delbruck, Tobi (2016) Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network. In: Second International Conference on Event-Based Control, Communication, and Signal Processing, Krackow, Poland. IEEE. 8 pp. [Conference contribution]

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URL: https://arxiv.org/pdf/1606.09433v1.pdf

DOI: 10.1109/EBCCSP.2016.7605233

Abstract

Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor "frames" that consist of a constant number of DAVIS ON and OFF events. The network is thus "data driven" at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87% or 92% (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of datadriven computing.

Item Type:Conference contribution (Paper)
Keywords:Convolutional Neural Network, Artificial Retina, Robotics
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:36105
Deposited By: Dr Dermot Kerr
Deposited On:17 Feb 2017 11:01
Last Modified:17 Oct 2017 16:26

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