Biological organisms coexist in complex environments where they adaptively interact with each other and with their surroundings. These interactions are typically governed by a collection of protein-mediated networks of biochemical reactions which regulate the behaviour of such organisms. Amongst the different biochemical networks, this talk draws its attention to signalling networks. They represent the main chain connecting cells with their environment. This talk presents two computational models inspired by the structure and dynamics of signalling networks, and discusses their applicability to a real world control problem, the generation of adaptive rhythmic locomotion patterns within multi-legged robotic systems. Results have highlighted that artificial signalling networks improve a robot’s adaptivity by self-adjusting their dynamics according to a terrain's irregularities.
Dr Luis Fuente received an MSc in Intelligent Systems and Robotics at De Montfort University in 2010. He then joined the Department of Electronics at the University of York where he completed a PhD in biological computation and robotics in 2014. Currently, he is a research assistant in the Robotics Laboratory at Oxford Brookes University. His main research interests include the use of biologically-inspired architectures within robotic systems and the development of multi-legged walking strategies based on chaos control theory.
Host: Michael Lones