  The gait generator, which is capable of producing rhythmic signals for
coordinating multiple joints, is an essential component in the quadruped robot
locomotion control framework. The biological counterpart of the gait generator
is the Central Pattern Generator (abbreviated as CPG), a small neural network
consisting of interacting neurons. Inspired by this architecture, researchers
have designed artificial neural networks composed of simulated neurons or
oscillator equations. Despite the widespread application of these designed CPGs
in various robot locomotion controls, some issues remain unaddressed,
including: (1) Simplistic network designs often overlook the symmetry between
signal and network structure, resulting in fewer gait patterns than those found
in nature. (2) Due to minimal architectural consideration, quadruped control
CPGs typically consist of only four neurons, which restricts the network's
direct control to leg phases rather than joint coordination. (3) Gait changes
are achieved by varying the neuron couplings or the assignment between neurons
and legs, rather than through external stimulation. We apply symmetry theory to
design an eight-neuron network, composed of Stein neuronal models, capable of
achieving five gaits and coordinated control of the hip-knee joints. We
validate the signal stability of this network as a gait generator through
numerical simulations, which reveal various results and patterns encountered
during gait transitions using neuronal stimulation. Based on these findings, we
have developed several successful gait transition strategies through neuronal
stimulations. Using a commercial quadruped robot model, we demonstrate the
usability and feasibility of this network by implementing motion control and
gait transitions.
