基于模糊神经网络的移动机器人沿墙导航控制

移动机器人沿墙导航控制包含了追踪和避障两种情况,是移动机器人研究中的常见问题。它是指机器人在一定方向上沿墙运动,或者更一般意义上的沿着物体轮廓运动,并与墙保持一定距离。移动机器人利用声纳采集机器人与墙体的距离和角度信息,通过模糊神经网络将输入数据进行融合,从而判断移动机器人的位姿信息,输出左右轮速度控制其动作。实验证明此方法可以有效地保证移动机器人在安全距离内沿墙体运动。对比采用模糊神经网络前后的实验,采用后的移动机器人沿墙导航控制轨迹优于采用前,均方误差大大减小。     

Wall-following control of a two-wheeled mobile robot

Wall-following control problem for a mobile robot is to move it along a wall at a constant speed and keep a specified distance to the wall.This paper proposes wall-following controllers based on Lyapunov function candidate for a two-wheeled mobile robot (MR) to follow an unknown wall. The mobile robot is considered in terms of kinematic model in Cartesian coordinate system. Two wall-following feedback controllers are designed: full state feedback controller and observer-based controller. To design the former controller, the errors of distance and orientation of the mobile robot to the wall are defined, and the feedback controller based on Lyapunov function candidate is designed to guarantee that the errors converge to zero asymptotically. The latter controller is designed based on Busawon’s observer as only the distance error is measured. Additionally, the simulation and experimental results are included to illustrate the effectiveness of the proposed controllers.