ArticleFrontiers in neurorobotics2021
Neuromorphic NEF-Based Inverse Kinematics and PID Control.
Article in Frontiers in neurorobotics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 40 citations in OpenAlex.
- A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Autonomous driving controllers with neuromorphic spiking neural networks.Frontiers in neurorobotics · 2023Article
- Implementation of Kalman Filtering with Spiking Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Embodied neuromorphic intelligence.Nature communications · 2022Review
- Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics.Patterns (New York, N.Y.) · 2022Article
- Adaptive control of a wheelchair mounted robotic arm with neuromorphically integrated velocity readings and online-learning.Frontiers in neuroscience · 2022Article
- Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation.Frontiers in neuroscience · 2021Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
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Abstract
Neuromorphic implementation of robotic control has been shown to outperform conventional control paradigms in terms of robustness to perturbations and adaptation to varying conditions. Two main ingredients of robotics are inverse kinematic and Proportional-Integral-Derivative (PID) control. Inverse kinematics is used to compute an appropriate state in a robot's configuration space, given a target position in task space. PID control applies responsive correction signals to a robot's actuators, allowing it to reach its target accurately. The Neural Engineering Framework (NEF) offers a theoretical framework for a neuromorphic encoding of mathematical constructs with spiking neurons for the implementation of functional large-scale neural networks. In this work, we developed NEF-based neuromorphic algorithms for inverse kinematics and PID control, which we used to manipulate 6 degrees of freedom robotic arm. We used online learning for inverse kinematics and signal integration and differentiation for PID, offering high performing and energy-efficient neuromorphic control. Algorithms were evaluated in simulation as well as on Intel's Loihi neuromorphic hardware.
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Registered trials
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