Neuromorphic Robotic Arm Control for Smooth Joint Motion
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Solution Overview
Problem
Existing robotic arm controllers lack the ability to provide smooth and safe motion, especially when interacting with humans, leading to potential safety hazards and user discomfort, while also resulting in reduced motor lifespan and inefficiencies in energy usage and explainability.
Innovation Solution
The implementation of neuromorphic controllers based on spiking neural networks (SNNs) that mimic the neuronal connectomes of humans and animals, utilizing position and speed control proprioceptor neurons, extensor and flexor motor neurons, and presynaptic inhibitory neurons to generate actuation signals for precise and smooth joint control, enabling coordinated motion of multiple joints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional robotic arm controllers are used, then basic motion control is achieved, but smoothness and safety during human interaction deteriorate
Solution Approach 1:
The patent replaces traditional mechanical control systems with a neuromorphic controller that uses spiking neural networks to generate smooth, human-like motion patterns. The SNN mimics biological motor control to produce naturally accelerated and decelerated movements, eliminating the need for complex mechanical damping systems while improving both safety and smoothness during human-robot interaction.
Solution Approach 2:
The patent changes the control parameters from traditional position-based control to spike-timing-based control, where the timing and frequency of neural spikes dynamically adjust motor output. This parameter transformation enables continuous, smooth velocity profiles with natural acceleration and deceleration phases, significantly improving motion smoothness while maintaining safety through biologically-inspired control dynamics.
2Duration of action of stationary object
If traditional control methods are used, then robotic arm movement is achieved, but motor lifespan deteriorates due to frequent start-stop operations
Solution Approach 1:
The patent replaces traditional on/off control mechanisms with continuous neuromorphic control that generates smooth velocity profiles. The spiking neural network produces naturally decelerating motion patterns that minimize abrupt stops and starts, reducing mechanical stress on motors and extending their operational lifespan while maintaining efficient task completion through coordinated multi-joint movement.
3Use of energy by moving object
If simple control algorithms are used, then computational efficiency is improved, but ability to replicate human-like smooth movements deteriorates
Solution Approach 1:
The patent copies the fundamental structure and dynamics of biological motor control systems into an artificial spiking neural network. By replicating the key computational principles of neuronal ensembles and their connectivity patterns, the SNN achieves human-like smooth movement generation with minimal computational resources, energy-efficient spike-based communication, and biologically-plausible control architecture.
4Measurement precision
If complex neuronal ensembles are used to approximate differential equations, then control precision is improved, but device complexity and energy consumption worsen
Solution Approach 1:
The patent extracts only the essential computational elements needed for smooth motion control from complex biological neuronal ensembles. Instead of implementing thousands of neurons, the SNN uses a minimal set of spiking neurons with carefully designed connectivity patterns that capture the core dynamics of human-like movement, achieving high control precision with significantly reduced device complexity and energy consumption.
Data Source
AI summary
This document describes neuromorphic controllers. In one aspect, a method for controlling one or more joints of a robotic arm includes receiving, by neuromorphic controller comprising a spiking neural network (SNN), a target value of a joint control variable for a joint of the robotic arm. The SNN includes two position proprioceptor neurons, two-speed proprioceptor neurons, a presynaptic inhibitory neuron, an extensor motor neuron, and a flexor motor neuron. The neuromorphic controller updates an actual value of the joint control variable for the joint of the robotic arm based on the target value of the joint control variable. The updating includes generating, by one of the two position proprioceptor neurons, the first spikes to one of the extensors motor neurons or the flexor motor neuron based on a difference between the actual value of the joint control variable and the target value of the joint control variable.