Abstract
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.
Highlights
Neuromorphic engineering (NE) takes inspiration from biology, from nervous systems
We present a fully neuromorphic robotic arm, whose development started
The spike-based PID controller is a ProportionalIntegrative-Derivative controller completely designed considering the requirements of the spike paradigm
Summary
Neuromorphic engineering (NE) takes inspiration from biology, from nervous systems. Available products in the market provide motor controllers such as black boxes, which receive a reference command for targeting a position of a joint or a revolution speed These controllers communicate with each other through industrial field buses, such as ControllerArea-Network (CAN), which introduces extra latency in the control loop and forces a fixed power consumption (DominguezMorales et al, 2011). These systems never provide direct access to the signals that drive the motors of the robots. The robot uses the same kind of motors (DC motors with optical encoders), the sPID was adapted and configured to maintain a fixed position of each joint This is a challenge in the spike-domain. The rest of the paper is structured as follows: section 2 reviews a spike-based proportional-integral-derivative controller that was adapted for this robot, section 3 provides details about the architecture of the robot and its neuromorphic electronics, section 4 describes the experiments and results, and section 5 presents the conclusions
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