Linares-Barranco Alejandro, Perez-Peña Fernando, Jimenez-Fernandez Angel, Chicca Elisabetta
Robotics and Technology of Computers Lab (ETSII-EPS), Universidad de Sevilla, Sevilla, Spain.
Smart Computer Systems Researh and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), Universidad de Sevilla, Sevilla, Spain.
Front Neurorobot. 2020 Nov 30;14:590163. doi: 10.3389/fnbot.2020.590163. eCollection 2020.
Compared to classic robotics, biological nervous systems respond to stimuli in a fast and efficient way regarding the body motor actions. Decision making, once the sensory information arrives to the brain, is in the order of ms, while the whole process from sensing to movement requires tens of ms. Classic robotic systems usually require complex computational abilities. Key differences between biological systems and robotic machines lie in the way information is coded and transmitted. A neuron is the "basic" element that constitutes biological nervous systems. Neurons communicate in an event-driven way through small currents or ionic pulses (spikes). When neurons are arranged in networks, they allow not only for the processing of sensory information, but also for the actuation over the muscles in the same spiking manner. This paper presents the application of a classic motor control model (proportional-integral-derivative) developed with the biological spike processing principle, including the motor actuation with time enlarged spikes instead of the classic pulse-width-modulation. This closed-loop control model, called spike-based PID controller (sPID), was improved and adapted for a dual FPGA-based system to control the four joints of a bioinspired light robot (BioRob X5), called event-driven BioRob (ED-BioRob). The use of spiking signals allowed the system to achieve a current consumption bellow 1A for the entire 4 DoF working at the same time. Furthermore, the robot joints commands can be received from a population of silicon-neurons running on the Dynap-SE platform. Thus, our proposal aims to bridge the gap between a general purpose processing analog neuromorphic hardware and the spiking actuation of a robotic platform.
与传统机器人技术相比,生物神经系统在身体运动动作方面能快速有效地对刺激做出反应。一旦感官信息到达大脑,决策过程只需几毫秒,而从感知到运动的整个过程需要几十毫秒。传统机器人系统通常需要复杂的计算能力。生物系统和机器人机器之间的关键区别在于信息的编码和传输方式。神经元是构成生物神经系统的“基本”元素。神经元通过小电流或离子脉冲(尖峰)以事件驱动的方式进行通信。当神经元排列成网络时,它们不仅允许处理感官信息,还能以相同的尖峰方式对肌肉进行驱动。本文介绍了一种基于生物尖峰处理原理开发的经典运动控制模型(比例积分微分)的应用,包括使用时间放大的尖峰而不是经典的脉宽调制进行电机驱动。这种闭环控制模型,称为基于尖峰的PID控制器(sPID),经过改进并适用于基于双FPGA的系统,以控制一个受生物启发的轻型机器人(BioRob X5)的四个关节,该机器人称为事件驱动生物机器人(ED - BioRob)。使用尖峰信号使系统在同时运行整个4自由度时电流消耗低于1A。此外,机器人关节命令可以从在Dynap - SE平台上运行的一群硅神经元接收。因此,我们的提议旨在弥合通用处理模拟神经形态硬件与机器人平台的尖峰驱动之间的差距。