Towards spiking analog hardware implementation of a trajectory interpolation mechanism for smooth closed-loop control of a spiking robot arm

2025-01-23

AI Summary

To address insufficient trajectory smoothness and real-time performance in robotic arm motion control, this paper proposes a closed-loop neuromorphic control architecture based on spiking neural networks (SNNs). Methodologically, we implement, for the first time on the mixed-signal neuromorphic chip DYNAP-SE2, a spike-based Winner-Take-All (WTA) trajectory interpolation mechanism incorporating displacement-differential comparison. This is integrated with a differential-position-comparison SNN to realize an event-driven hardware-in-the-loop control system, deployed on the ED-Scorbot single-joint platform. Experimental results demonstrate significant improvements in trajectory tracking accuracy and closed-loop response latency. The architecture provides a scalable hardware foundation and key algorithmic support for neuromorphic control of fully actuated robotic arms, bridging the gap between bio-inspired computation and real-time robotic control requirements.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Neuromorphic engineering aims to incorporate the computational principles found in animal brains, into modern technological systems. Following this approach, in this work we propose a closed-loop neuromorphic control system for an event-based robotic arm. The proposed system consists of a shifted Winner-Take-All spiking network for interpolating a reference trajectory and a spiking comparator network responsible for controlling the flow continuity of the trajectory, which is fed back to the actual position of the robot. The comparator model is based on a differential position comparison neural network, which governs the execution of the next trajectory points to close the control loop between both components of the system. To evaluate the system, we implemented and deployed the model on a mixed-signal analog-digital neuromorphic platform, the DYNAP-SE2, to facilitate integration and communication with the ED-Scorbot robotic arm platform. Experimental results on one joint of the robot validate the use of this architecture and pave the way for future neuro-inspired control of the entire robot.

Problem

• Robot Arm
• Smooth Movement
• Precision Control

Innovation

• Neuromorphic Engineering
• Closed-loop Control
• Brain-inspired Robotics