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.

翻译：步态生成器是四足机器人运动控制框架中的核心组件，能够产生协调多关节的节律性信号。其生物学对应物为中枢模式发生器（简称CPG），即由相互作用的神经元构成的小型神经网络。受此结构启发，研究者设计了由模拟神经元或振荡器方程构成的人工神经网络。尽管这些设计的CPG已广泛应用于各类机器人运动控制，但仍存在若干未解决的问题：（1）简化的网络设计常忽略信号与网络结构间的对称性，导致生成的步态模式少于自然界中存在的种类。（2）由于缺乏对架构的深入考量，四足控制CPG通常仅由四个神经元构成，这限制了网络对腿部相位的直接控制能力，而无法实现关节协调。（3）步态切换通过改变神经元耦合方式或神经元与腿部间的映射关系实现，而非借助外部刺激。本研究运用对称性理论设计了一个由Stein神经元模型构成的八神经元网络，能够实现五种步态及髋膝关节的协调控制。通过数值仿真验证了该网络作为步态生成器的信号稳定性，并揭示了神经元刺激下步态转换过程中出现的各类结果与模式。基于这些发现，我们通过神经元刺激开发了多种成功的步态转换策略。借助商用四足机器人模型，我们通过实现运动控制与步态转换，验证了该网络的实用性与可行性。