神经元模型的四足机器人适应性行走控制

Adaptive Walking Control of Quadruped Robot Based on Rulkov Neuron Model

摘要:

为了改善足式机器人的适应性行走能力，提出仿生控制和智能优化算法相结合的控制策略.利用Rulkov神经元模型对生物中枢模式发生器(central pattern generator, CPG)进行机理建模；设计了基于CPG模型的单关节和多关节耦合的网络拓扑结构，并利用多目标遗传算法优化CPG单元间的耦合系数矩阵，使得CPG网络的输出信号可以控制机器人关节按照一定的时序发生动作；设计机器人信息融合反馈系统并提出坡面适应性行走控制策略，并以四足机器人GhostDog作为实验对象，在Webots仿真平台上做实验验证.结果表明，所提出的行走控制策略可以控制机器人自主完成模式切换，具有一定的环境适应性.

Abstract:

To improve the adaptive walking ability of legged robot, a strategy combining the bionic control method and the intelligent optimization algorithm is proposed. The Rulkov neuron model is used to model the central pattern generator (CPG). Based on the CPG model, the single and multi-joint coupling network topology is proposed. The coupling coefficient matrix between CPG units is optimized using the multiobjective genetic algorithm. In this way, the robot’s joints can act correspondingly to timing sequence controlled by the output signals of the CPG network. Finally, the information fusion feedback system and adative walking control strategy are proposed, and a simulation using Webots is implemented on the quadruped robot called GhostDog to experimentally verify it. The experimental results show that the proposed walking control strategy can control the robot to switch walking modes automatically and have certain environmental adaptability.