video: Demo of bursting central pattern generators network for robot locomotion showing five different modes: walk, jump, trot, crawl and gallop.
Credit: Rajkumar Kubendran, Enigma Lab/University of Pittsburgh
When navigating a busy sidewalk, most people can avoid puddles, other pedestrians, and cracks in the pavement. It may seem intuitive – because it is.
There’s a biological component that allows humans and other mammals to navigate our complex environments. Central Pattern Generators (CPG) are neural networks that produce rhythmic patterns of control signals for limbs using simple environmental cues. When we quickly step away to avoid something blocking our path, that’s our CPGs doing their job.
Rajkumar Kubendran, principal investigator and assistant professor of electrical and computer engineering of the University of Pittsburgh, received a two-year, $1,606,454 award from the National Science Foundation to to engineer synthetic bversions of these neural networks in robots. Feng Xiong, associate professor of electrical and computer engineering at Pitt, and M.P. Anantram, at the University of Washington, will serve as co-principal investigators.
“While these networks are natural for us, there is currently no efficient way to replicate them using electronic devices and computers,” Kubendran said. “Agile robots that can explore unknown and treacherous terrains have the potential to enable autonomous navigation for commercial transport, enhance disaster response during floods and earthquakes or to remote and unsafe areas like malfunctioning nuclear plants or space exploration.”