摘要
Abstract
When walking independently,a legged robot usually measures the rotation angle of the legs and calculates the position of the foot by means of the tilt sensor,however,the leg angle sensor is easily affected by the influence of various factors,such as noise,temperature,leading to low accuracy of measurement and the foot end position estima-tion. To solve the above problems,put forward a new angle sensor signal processing method,firstly by using Kalman filter for Angle sensor output signal filtering preprocessing,and then output the filtered signal and temperature value of the tilt sensor as a double input single output RBF neural network input variables of the model,using the ant clus-tering algorithm in parallel optimization characteristics and adaptive adjustment of volatile coefficient method to lo-cate the RBF neural network basis function. The experimental results show that the proposed algorithm can filter the noise of the tilt sensor signal well,and achieve the temperature compensation of the inclination signal. The measure-ment error can be controlled within 0.75％,which has practical application value.关键词
信号处理/足式机器人/卡尔曼滤波/温度补偿/蚁群聚类算法/RBF神经网络Key words
signal processing/legged robot/Kalman filtering/temperature compensation/ant colony clustering algo-rithm/RBF neural network分类
信息技术与安全科学
