Abstract
Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.
Highlights
Collision avoidance is one of the most basic tasks in mobile robotics that ensures safety of the robotic platform, as well as the objects and users around it
A promising strategy for taking these issues into account is to implement the mechanisms used in biological neural networks, which face the same problem of using an unreliable computing substrate that consists of noisy neurons and synapses driven by stochastic biological and diffusion processes
We show that by using the population-coding strategy in a mixed signal analog/digital neuromorphic hardware, it is possible to cope with the variability of its analog circuits and to produce reliably the desired behavior on a robot
Summary
Collision avoidance is one of the most basic tasks in mobile robotics that ensures safety of the robotic platform, as well as the objects and users around it. A promising strategy for taking these issues into account is to implement the mechanisms used in biological neural networks, which face the same problem of using an unreliable computing substrate that consists of noisy neurons and synapses driven by stochastic biological and diffusion processes. These biological mechanisms include adaptation and learning, and using population coding (Ermentrout, 1998; Pouget et al, 2000; Averbeck et al, 2006) and recurrent connections (Wilson and Cowan, 1973; Douglas et al, 1995) to stabilize behaviorally relevant decisions and states against neuronal and sensory noise. We found several limitations of the simple Braitenberg-vehicle approach and suggest extensions of the simple architecture that solve these problems, leading to robust obstacle avoidance and target acquisition in our robotic setup
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
