基于层次化联结CPG模型的蛇形机器人3维步态控制

An HCCPG Model-based 3D Gait Control of a Snake-like Robot

联结CPG(connectionist central pattern generator,CCPG)模型适于控制机器人生成步态,但是传统的CCPG模型无法很好地生成3维步态.为此,本文根据生物学原理,提出了一个改进的神经元模型和一个改进的层次化CCPG(hierarchical CCPG,HCCPG)模型.HCCPG模型能够生成相位协调的多自由度运动控制信号,从而解决了传统CCPG模型的步态生成问题.基于该模型,提出了一个统一方法来生成机器人的2维、3维步态.对转弯步态的特性进行了系统化深入分析,以便更好地利用该步态来适应狭窄的弯道环境.本文提出的HCCPG模型以及得到的步态特性,有助于提高机器人的环境适应能力.

The connectionist central pattern generator （CCPG） model is suitable for controlling robots and generating gaits, however, the traditional CCPGs can’t generate the 3D gaits well. To solve this problem, an improved neuron model and an improved hierarchical CCPG （HCCPG） model are proposed according to biology principles. HCCPG can generate the phase-coordinated multi-degrees-of-freedom motion control signals well, so it solves the gait generation problem in traditional CCPGs. Based on the HCCPG, a unified generation method is proposed for 2D gaits and 3D gaits. The properties of turning gait are investigated systematically and thoroughly to make better use of it to adapt to narrow curved passages. The proposed HCCPG model and the derived gait properties are useful for improving the robot’s adaptability.