A Novel Hardware-Efficient Central Pattern Generator Model Based on Asynchronous Cellular Automaton Dynamics for Controlling Hexapod Robot

This paper presents a novel hardware-efficient central pattern generator (CPG) model to realize a bio-inspired gait of a hexapod robot. The CPG model consists of a network of cellular automaton (CA) oscillators; thus, it can be implemented as a network of sequential logic circuits. Detailed analyses of nonlinear oscillation dynamics show that the oscillator that is driven by multiple asynchronous clocks is more suitable to realize the gait of the robot than an oscillator that is driven by a single clock or multiple synchronous clocks. Moreover, detailed analyses of nonlinear network dynamics show that the clocks among the CA oscillators should be asynchronous to appropriately realize the gait. Using the analyses, systematic procedures to design the CPG model are proposed. The proposed CPG model is implemented in a field programmable gate array (FPGA); our experiments validate that the CPG model implemented in an FPGA can realize the bio-inspired gait of a hardware robot. Further, we show that the proposed CPG model utilizes fewer circuit elements and lower power than a conventional CPG model.