DYNAMICAL INVARIANTS FOR CPG CONTROL IN AUTONOMOUS ROBOTS

Several studies have shown the usefulness of central pattern generator circuits to control autonomous rhythmic motion in robots. The traditional approach is building CPGs from nonlinear oscillators, adjusting a connectivity matrix and its weights to achieve the desired function. Compared to existing living CPGs, this approach seems still somewhat limited in resources. Living CPGs have a large number of available mechanisms to accomplish their task. The main function of a CPG is ensuring that some constraints regarding rhythmic activity are always kept, surmounting any disturbances from the external environment. We call this constraints the "dynamical invariant" of a CPG. Understanding the underlying biological mechanisms would take the design of robotic CPGs a step further. It would allow us to begin the design with a set of invariants to be preserved. The presence of these invariants will guarantee that, in response to unexpected conditions, an effective motor program will emerge that will perform the expected function, without the need of anticipating every possible scenario. In this paper we discuss how some bio-inspired elements contribute to building up these invariants.