标题： 面向脉冲模拟硬件实现轨迹插值机制以实现脉冲机械臂的平滑闭环控制 显示英文标题

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

摘要： 神经形态工程旨在将动物大脑中的计算原理融入现代技术系统。 按照这种方法，在本工作中我们提出了一种基于事件的机械臂的闭环神经形态控制系统。 该系统包括一个位移的Winner-Take-All脉冲网络，用于插值参考轨迹，以及一个负责控制轨迹流连续性的脉冲比较器网络，该网络将反馈至机器人的实际位置。 比较器模型基于一个差分位置比较神经网络，该网络控制下一个轨迹点的执行，以在系统两个组件之间形成控制回路。 为了评估该系统，我们将模型实现并部署在一个混合信号模拟数字神经形态平台DYNAP-SE2上，以便与ED-Scorbot机械臂平台进行集成和通信。 机器人单关节的实验结果验证了该架构的使用，并为未来整个机器人的神经启发控制铺平了道路。 显示英文摘要

摘要： 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.