Neuromorphic robotic platform with visual input, processor and actuator, based on spiking neural networks

Cheng, Ran, Mirza, Khalid B. and Nikolic, Konstantin ORCID: (2020) Neuromorphic robotic platform with visual input, processor and actuator, based on spiking neural networks. Applied System Innovation, 3 (2). pp. 28-43.

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This paper describes the design and modus of operation of a neuromorphic robotic platform based on SpiNNaker, and its implementation on the goalkeeper task. The robotic system utilises an address event representation (AER) type of camera (dynamic vision sensor (DVS)) to capture features of a moving ball, and a servo motor to position the goalkeeper to intercept the incoming ball. At the backbone of the system is a microcontroller (Arduino Due) which facilitates communication and control between different robot parts. A spiking neuronal network (SNN), which is running on SpiNNaker, predicts the location of arrival of the moving ball and decides where to place the goalkeeper. In our setup, the maximum data transmission speed of the closed-loop system is approximately 3000 packets per second for both uplink and downlink, and the robot can intercept balls whose speed is up to 1 m/s starting from the distance of about 0.8 m. The interception accuracy is up to 85%, the response latency is 6.5 ms and the maximum power consumption is 7.15W. This is better than previous implementations based on PC. Here, a simplified version of an SNN has been developed for the ‘interception of a moving object’ task, for the purpose of demonstrating the platform, however a generalised SNN for this problem is a nontrivial problem. A demo video of the robot goalie is available on YouTube.

Item Type: Article
Identifier: 10.3390/asi3020028
Keywords: neuromorphic engineering; SpiNNaker; DVS; robotic goalkeeper
Subjects: Computing > Intelligent systems
Related URLs:
Depositing User: Konstantin Nikolic
Date Deposited: 02 Jul 2020 10:42
Last Modified: 06 Feb 2024 16:03


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