Mechatronic simulation & exploration of a mechanical context relevant to quadrupedal neuromorphic walking employing Nervous networks for control : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering, Mechatronics at Massey University, Albany, New Zealand

Abstract

Neuromorphic engineering is the studv and emulation of neural sensory and control structures found in the natural world. Currently a significant research focus in this field, and indeed, in engineering at large, is the research of robotic walking platforms - an ideal application for artificial neural controllers. To design such neuromorphic controllers, significant knowledge is needed of the robotic context to which they will he applied. The focus of this research is to explore the relationship between the mechanical design of a robot, and its resultant walking proficiency. A neuromorphic controller utilizing Nervous networks was constructed, and embedded into a typical & useful mechatronic context. This consists of a simple walking platform, of a type commonly used in Nervous network research. This robot was used to provide intuition and a reference point for development of a simulation for empirical testing. A physical simulation of the mechanical context was developed, allowing for the exploration of its behaviour, particularly with regard to the type of walking "caused" by the integration of an appropriate Nervous network controller. To evaluate the behavioural fitness of this context in various configurations, empirical simulations were run using the developed simulation, and heuristic results derived to develop optimized parameters for causing walking behaviours in the studied context. Further simulations were then run to evaluation the efficacy of these developed heuristics. From these simulations & explorations, the presence of an identifiable "critical point phenomenon" in the interaction between the robot's legs was demonstrated. This critical point was then used for parameter extraction; further simulation demonstrated that parameters extracted from this critical point provided near-optimal walking behaviour from the robot in a variety of leg topologies. These results provide significant knowledge and intuition for designers of quadrupedal walking platforms, particularly those driven from Nervous network derived neuromorphic controllers. Implementation of these results in such a robotic platform will provide useful new "real world" data, allowing the developed models & heuristics to be further refined.