基于CPG的扣球机器人手臂控制的WTA硬件实现
Towards hardware Implementation of WTA for CPG-based control of a Spiking Robotic Arm
摘要：
生物神经系统通常执行例如动物肢体中的多个自由度的控制。神经形态工程师通过在硬件中模拟这些系统来研究它们，以更深入地了解其可能的应用，以解决工程和机器人技术中的复杂问题。中央模式发生器（CPG）是神经控制器的一部分，通常在最后一步用于产生肢体运动的节奏模式。不同的模式和步态通常通过赢家通吃（WTA）电路竞争，以产生正确的动作。在这项工作中，我们提出了一个WTA电路中实现的尖峰神经网络（SNN）处理器，以产生这样的模式，用于控制机器人手臂的实时。该机器人使用基于尖峰的比例积分微分（SPID）控制器，以保持从WTA电路的神经元的赢家人口命令的关节位置。实验证明了机器人控制的可行性与尖峰电路后，大脑的灵感。
英文摘要：
Biological nervous systems typically perform the control of numerous degrees of freedom for example in animal limbs. Neuromorphic engineers study these systems by emulating them in hardware for a deeper understanding and its possible application to solve complex problems in engineering and robotics. Central-Pattern-Generators (CPGs) are part of neuro-controllers, typically used at their last steps to produce rhythmic patterns for limbs movement. Different patterns and gaits typically compete through winner-take-all (WTA) circuits to produce the right movements. In this work we present a WTA circuit implemented in a Spiking-Neural-Network (SNN) processor to produce such patterns for controlling a robotic arm in real-time. The robot uses spike-based proportional-integrativederivative (SPID) controllers to keep a commanded joint position from the winner population of neurons of the WTA circuit. Experiments demonstrate the feasibility of robotic control with spiking circuits following brain-inspiration.
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