足式机器人腿部倾角传感器信号处理研究
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摘要
足式机器人在自主行走时,一般通过倾角传感器来测量腿部转动角度计算足端位置,然而目前足式机器人腿部倾角传感器测量时易受噪声干扰、温度等因素的影响,导致测量精度低,足端位置估计不准确。针对以上问题,提出新的倾角传感器信号处理方法,首先利用卡尔曼滤波方法对倾角传感器输出信号进行滤波预处理,然后把滤波信号和倾角传感器输出温度值作为建立的双输入单输出RBF神经网络模型的输入变量,采用蚁群聚类算法的并行寻优特征和自适应调整挥发系数方法来确定RBF神经网络基函数位置。实验结果表明,提出的算法能很好地滤除倾角传感器信号中的噪声,实现了倾角信号的温度补偿,测量误差能够控制在0.75%以内,具有实际运用价值。
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 estimation. 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 clustering algorithm in parallel optimization characteristics and adaptive adjustment of volatile coefficient method to locate 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 measurement error can be controlled within 0.75%,which has practical application value.
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 estimation. 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 clustering algorithm in parallel optimization characteristics and adaptive adjustment of volatile coefficient method to locate 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 measurement error can be controlled within 0.75%,which has practical application value.
引文
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[2]朱月青,张志利,谭立龙,等.BP神经网络在倾角传感器数据融合中的应用[J].仪表技术与传感器,2010(1):101-102.
[3]邵晓敏.全量程无线倾角传感器设计[D].上海:上海大学,2012.
[4]曹建安,张乐平,吴昊,等.采用倾角传感器实现空间旋转角度测量的解析方法研究[J].西安交通大学学报,2013,47(10):109-114.
[5]袁健,周忠海,金光虎,等.基于卡尔曼滤波的自主式水下航行器大尺度编队控制[J].智能系统学报,2013(4):344-348.
[6]王小冬.卡尔曼滤波在卫星导航中的应用研究[D].大连:大连海事大学,2008.
[7]罗贞.基于卡尔曼滤波器的系统状态估计和故障检测[D].武汉:华中科技大学,2013.
[8]乃永强,李军.基于极限学习机的机械臂自适应神经控制[J].信息与控制,2015,44(3):257-262.
[9]乔俊飞,韩红桂.RBF神经网络的结构动态优化设计[J].自动化学报,2010,36(6):865-872.
[10]张朋,范福玲,杨益,等.基于时间序列模型的超声信号滤波算法研究[J].传感技术学报,2015(3):396-400.
[11]郑立斌,王红梅,顾寄南,等.RBF神经网络在机器人视觉伺服控制中的应用[J].机床与液压,2015,43(15):41-43.
[12]孙艳梅,都文和,冯昌浩,等.基于蚁群聚类算法的RBF神经网络在压力传感器中的应用[J].传感技术学报,2013,26(6):806-809.
[13]柏继云.蚁群优化算法及觅食行为模型研究[D].哈尔滨:哈尔滨工业大学,2013.
[14]杨剑峰.蚁群算法及其应用研究[D].杭州:浙江大学,2007.
[15]黄延红.基于蚁群算法的聚类算法研究[D].成都:电子科技大学,2011.