| 基于KF的自平衡机器人姿态角补偿方法 |
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| 引用本文： | 李潮全,高学山,王树三,李科杰.基于KF的自平衡机器人姿态角补偿方法[J].北京理工大学学报,2012,32(1):28-32. |
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| 作者姓名： | 李潮全 高学山 王树三 李科杰 |
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| 作者单位： | 北京理工大学智能机器人研究所,北京,100081;北京理工大学智能机器人研究所,北京100081;哈尔滨工业大学机器人技术与系统国家重点实验室,黑龙江,哈尔滨150080;中国航天技术研究院200厂,北京,100854 |
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| 基金项目： | 国家"八六三"计划项目(2008AA04Z208);机器人技术与系统国家重点实验室(哈尔滨工业大学)(SKLRS-2010-ZD-04) |
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| 摘 要： | 研究基于卡尔曼滤波(KF)的低通滤波补偿方法,对采用单轴加速度计的自平衡机器人姿态角测量系统进行补偿,消除由于振动、冲击等引起的信号失真,增强控制系统的稳定性.根据前期研究及实验,深入分析振动、冲击对传感器信号的影响,提出了一种基于最小二乘拟合原理的姿态角计算模型.在获得机器人自由振荡频率的基础上,设计了基于KF的混合低通滤波单元,并进行了物理模型实验.结果表明,该补偿方法能完全消除振动造成的传感信号失真.此外,该补偿方法对强烈冲击所造成的信号波动亦有明显衰减,能显著提升自平衡机器人的稳定性.
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| 关 键 词： | 自平衡机器人 姿态角信号 KF混合滤波 补偿 |
| 收稿时间： | 2011/1/24 0:00:00 |
Position Angular Compensation for Self-Balance Robot Based on Kalman Filtering |
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| LI Chao-quan,GAO Xue-shan,WANG Shu-san and LI Ke-jie.Position Angular Compensation for Self-Balance Robot Based on Kalman Filtering[J].Journal of Beijing Institute of Technology(Natural Science Edition),2012,32(1):28-32. |
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| Authors: | LI Chao-quan GAO Xue-shan WANG Shu-san and LI Ke-jie |
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| Institution: | Intelligent Robot Institute of Beijing Institute of Technology, Beijing 100081, China;Intelligent Robot Institute of Beijing Institute of Technology, Beijing 100081, China; States Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China;200 Factory of China Academy of Aerospace Technology, Beijing 100854, China;Intelligent Robot Institute of Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | This paper studies the compensation method based on low pass KF filter to eliminate the signal distortion caused by vibration or shock in angular position measurement system using single-axis accelerometers, and to improve the system stability. Least square fitting principle was used to build the posture angle calculation model, a hybrid low-pass filter was designed after getting the free frequency, and the physical test was implemented. The results show that the proposed compensation method can completely eliminate the signal distortion caused by high frequency vibrations, and it can also significantly improve the stability of self-balancing robot as well as the signal fluctuations caused by heavy impact. |
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| Keywords: | self-balance robot position angular signal Kalman blend filtering compensation |
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