基于CPG控制的六足爬壁机器人自主越障研究

Autonomous obstacle crossing of a wall-climbing hexapod robot based on CPG control

针对基于混沌CPG(central pattern generation)模型控制的六足爬壁机器人在幕墙上自主越障爬行的问题,本文提出了一种融合障碍探测模块、位姿调整模块以及简单神经环路混沌CPG的运动控制方法,并根据幕墙障碍特点和由传感器采集到的机器人方位信息及障碍距离信息,设计了合理的越障策略。实验显示该机器人可据环境自主切换三足行进步态、四足越障步态和五足原地旋转步态,并能够调整重心与幕墙间的距离以增加稳定性,最后完成了机器人在幕墙上自主爬壁和越障。

This study proposes a motion control method,which integrates an obstacle detection module,a position and pose adjustment module,and a simple neural circuit chaotic central pattern generation(CPG),to solve the problem of autonomous obstacle climbing of a wall-climbing hexapod robot controlled based on the chaotic CPG model on a curtain wall.A reasonable obstacle crossing strategy is designed based on the obstacle characteristics of curtain walls,direction information,and obstacle distance information collected from sensors.The experiment shows that the robot can autonomously change gait among a3-feet gait for walking,a4-feet gait for obstacle crossing,and a5-feet autochthonous rotation gait based on the environment.In addition,the robot can adjust the distance between the gravity center and the curtain walls to enhance stability.In conclusion,the robot climbed walls and crossed the obstacles in the curtain walls.