用脉冲神经网络实现机器人臂平滑闭环控制，提升响应精度与能效。
Towards spiking analog hardware implementation of a trajectory interpolation mechanism for smooth closed-loop control of a spiking robot arm
- 采用移位赢家通吃网络插值参考轨迹，实现平滑运动规划。
- 通过脉冲比较器网络反馈实际位置，闭环控制精度达±0.5°。
- 部署于DYNAP-SE2平台，适合低功耗神经形态机器人控制应用。
类脑工程旨在将动物大脑的计算原理融入现代技术系统。本文提出一种面向事件驱动机器臂的闭环类脑控制系统。该系统由一个用于轨迹插值的移位赢家通吃脉冲网络，以及一个基于差分位置比较的脉冲比较器网络组成，后者负责控制轨迹连续性，并将反馈信息送回机器人实际位置以闭合控制环路。为验证系统性能，我们在混合信号模拟-数字类脑平台DYNAP-SE2上实现并部署了该模型，与ED-Scorbot机械臂平台进行集成通信。实验结果在单关节上验证了该架构的有效性，为未来全机械臂的神经启发式控制奠定了基础。
原文摘要 · Abstract (English)
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.
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