An Address Event Representation-Based Processing System for a Biped Robot. (29th February 2016)

• Record Type: Journal Article
• Authors: Jaramillo-Avila, Uziel; Rostro-Gonzalez, Horacio; Camuñas-Mesa, Luis A.; Romero-Troncoso, Rene de Jesus; Linares-Barranco, Bernabe
• Is Part Of: International journal of advanced robotic systems. Volume 13:Number 1(2016)
• Publication Date: 2016-02-29
• Subjects: Address Event Representation -- Neuromorphic Systems -- Dynamic Vision Sensors -- Biped Robot Locomotion -- Spiking Neurons; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/62321
• Languages: English
• ISSNs: 1729-8806

Abstract

In recent years, several important advances have been made in the fields of both biologically inspired sensorial processing and locomotion systems, such as Address Event Representation-based cameras (or Dynamic Vision Sensors) and in human-like robot locomotion, e.g., the walking of a biped robot. However, making these fields merge properly is not an easy task. In this regard, Neuromorphic Engineering is a fast-growing research field, the main goal of which is the biologically inspired design of hybrid hardware systems in order to mimic neural architectures and to process information in the manner of the brain. However, few robotic applications exist to illustrate them. The main goal of this work is to demonstrate, by creating a closed-loop system using only bio-inspired techniques, how such applications can work properly. We present an algorithm using Spiking Neural Networks (SNN) for a biped robot equipped with a Dynamic Vision Sensor, which is designed to follow a line drawn on the floor. This is a commonly used method for demonstrating control techniques. Most of them are fairly simple to implement without very sophisticated components; however, it can still serve as a good test in more elaborate circumstances. In addition, the locomotion system proposed is able to coordinately control the six DOFs of a biped robot in switching between basic forms of movement. The latter has been implemented as a FPGA-based neuromorphic system. Numerical tests and hardware validation are presented.