A BIO-INSPIRED VISUAL FEEDBACK LOCOMOTION CONTROL BASED ON CNN UNIVERSAL MACHINE

Abstract

In this paper a new framework for bio-inspired robot locomotion control, entirely based on Cellular Neural Networks (CNNs), is introduced. In fact, CNNs are employed both for generating locomotion patterns in a hexapod robot, and for its trajectory control via visual feedback. In the paper the latter problem will be emphasized, being the CNN locomotion generation problem already treated in literature. Feedback signals are images captured by a camera and processed in real time by a CNN used as analog image processor. The actual framework makes use of a traditional PC where a tool for the synchronization of the visual CNN chip and the robot control has been designed. This paper describes the methodology as an important stage for the study, definition and optimization of the overall control methodology. The results obtained reveal true real time capabilities and the PC interface can be easily substituted with a processing board to be integrated with the visual CNN and with the locomotion-devoted CNN, in order to constitute a unified integrated system for real time visual motion control in complex structures.