Reaction-diffusion CNN algorithms to generate and control artificial locomotion

In this paper a physiological-behavioral approach to neural processing is used to realize artificial locomotion in mechatronic devices. The task has been realized by using a particular model of reaction-diffusion cellular neural networks (RD-CNN's) generating autowave fronts as well as Turing patterns. Moreover a programmable hardware cellular neural network structure is presented in order to model, generate, and control in real time some biorobots. The programmable hardware implementation gives the possibility of generating locomotion in real time and also to control the transition among several types of locomotion, with particular attention to hexapodes. The approach proposed allows not only the design of walking robots, but also the ability to build structures able to efficiently solve typical problems in industrial automation, such as online routing of objects moved on conveyor belts.