ArticleFrontiers in neurorobotics2017
Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.
Article in Frontiers in neurorobotics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 89 citations in OpenAlex.
- Brain-inspired learning rules for spiking neural network-based control: a tutorial.Biomedical engineering letters · 2025Review
- SLoN: a spiking looming perception network exploiting neural encoding and processing in ON/OFF channels.Frontiers in neuroscience · 2024Article
- A directionally selective collision-sensing visual neural network based on fractional-order differential operator.Frontiers in neurorobotics · 2023Article
- Embodied neuromorphic intelligence.Nature communications · 2022Review
- The Curved Openspace Algorithm and a Spike-Latency Model for Sonar-Based Obstacle Avoidance.Frontiers in neurorobotics · 2022Article
- Neuromorphic Engineering Needs Closed-Loop Benchmarks.Frontiers in neuroscience · 2022Review
- Organic neuromorphic electronics for sensorimotor integration and learning in robotics.Science advances · 2021Article
- A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the CrabFrontiers in neuroscience · 2021Article
- Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks.Frontiers in neuroscience · 2021Article
- A hybrid and scalable brain-inspired robotic platform.Scientific reports · 2020Article
- Neurorobots as a Means Toward Neuroethology and Explainable AI.Frontiers in neurorobotics · 2020Review
- Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle.Frontiers in neurorobotics · 2019Article
- GPUs Outperform Current HPC and Neuromorphic Solutions in Terms of Speed and Energy When Simulating a Highly-Connected Cortical Model.Frontiers in neuroscience · 2018Article
- Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields.Frontiers in neuroscience · 2018Article
- Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning.Frontiers in neuroscience · 2018Article
- Low-Cost Servomotor Driver for PFM Control.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.