基于数据手套的灵巧手抓取操作及阻抗控制的研究
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摘要
灵巧手的研究已经成为当前机器人技术研究的热点之一,是我国正在发展中的智能太空机器人关键技术之一,而灵巧手及其操作对于空间机器人的工作能力具有重要意义。本文结合国家科技部“863”计划的研究项目“新一代五指仿人灵巧手及其协调控制的研究”(课题编号:2006AA04Z255),以HIT/DLR II五指灵巧手为平台,建立人手与灵巧手的运动映射关系,利用改进的E-ANFIS模型进行抓取模型的位姿重建,接着提出一种具有非线性补偿的阻抗控制算法,并分别进行实验验证。
本文针对人手及HIT/DLR II五指灵巧手的结构特点,分别建立了人手和灵巧手的运动学模型,分析了人手模型和灵巧手模型的结构差异及其造成的映射困难。并利用一种改进的基于指尖位置的运动映射方法,提高了人手和HIT/DLR II五指灵巧手的指尖映射能力。并对映射算法进行了实验验证,结果表明基于数据手套的人手指尖运动能够很好的映射到灵巧手指尖位置,进而保证了灵巧手抓取物体的准确性。
上述的映射尽管能够保证灵巧手能够拟合人手的运动,但是由于数据手套的传感器误差过大,致使当人手抓取物体时,灵巧手通过上述的映射方法仅能得到物体的“粗位置”。为了保证灵巧手抓取物体的稳定性,利用改进的E-ANFIS模型对抓取物体的形状和位姿进行重构。利用人手抓取物体时指尖映射到灵巧手的指尖笛卡尔坐标值作为混合智能模型的输入,进而快速准确地重构出被抓取物体的数学模型,得到抓握物体的“精位置”实现灵巧手的稳定抓取。
针对HIT/DLR II五指灵巧手的抓取特点,在基于位置的阻抗控制算法基础上,提出了一种具有手指的重力补偿、摩擦补偿的阻抗控制算法,为了克服灵巧手手指同硬质物体接触而出现的动态不确定因素,采用扩展卡尔曼滤波器来实现动态结构参数的自适应估计。
最后利用HIT臂/手系统及数据手套为实验系统平台,给出反应数据手套和灵巧手抓握映射能力的抓取实验。基于提出的阻抗控制算法,对手指进行了关节和笛卡尔阻抗控制实验,并对其控制性能进行了分析,最终验证了该控制方法的有效性。
The study of Dexterous hands is one of hotspots in current robots technology research and one of key technologies of intelligent space robots. The operation of dexterous hands is very important for the space robot. Based on national high technology research and development program (863)“Research on new generation five-fingered anthropopathic dexterous robot hand and its cooperative control”(code: 2006AA04Z255 ), With HIT/DLR II dexterous hand for the platform, this dissertation improves the motion mapping based on data glove, improves the E-ANFIS model and using the Model to build the grasped model, and then proposes a flexible impedance control algorithm, finally, achieves the grasping control of the dexterous hands using the control system with data love.
Based on human hand and HIT/DLR II dexterous hand, this dissertation establishes kinematics models of human hand and the dexterous hand, and analyses structural differences between the models. Then an improved fingertip position-based motion mapping is used to improve the mapping ability between the human hand and the dexterous hand, and experiment is made to prove that mapping algorithm is right or not. The results show that the position of the human hand is mapped to the dexterous hand very well. And it make sure that the veracity of grasping object.
Although the mapping mentioned can prove that the dexterous hand can move with the human hand, because of the error of the sensors in the data glove, when the human hand is grasping the object, the dexterous hand can only get the rough station information by the mapping method. Considering the grasping task of dexterous hand with unknown objects, the model of grasped object is constructed by hyperelliptic equation. The improved E-ANFIS model can export shape and posture information of the grasped object. So that the veracity of the grasped object is assured. Information of the dexterous hand is used in the model. The newly grasped model is more accurate for the realization of multi-fingered manipulation. When the human hand is grasping the object, the station of the finger-tip is mapped to the dexterous hand, and its coordinate is used as the input of the intelligent model. And then the model of the grasped object is remade, and the exactly information of the object is obtained to prove the steadily grasp.
Considering HIT/DLR II dexterous hand as the study object, a joint and Cartesian impedance controller with nonlinear compensation is designed. To improve the performance of the impedance controller, system parameter estimations with extended kalman filter and gravity compensation have been investigated on the robot hand.
Finally, using the experimental platform of dexterous robot hand teleoperation system, Firstly, training data of the intelligent E-ANFIS model is gathered using the data glove, the match training to it is carried on. The capture experiment is made to prove the relationship between data glove and the grasped model. A lot of experiments are made to prove the effectiveness of the the control method. The results prove that the algorithm of the controller is correct.
本文针对人手及HIT/DLR II五指灵巧手的结构特点,分别建立了人手和灵巧手的运动学模型,分析了人手模型和灵巧手模型的结构差异及其造成的映射困难。并利用一种改进的基于指尖位置的运动映射方法,提高了人手和HIT/DLR II五指灵巧手的指尖映射能力。并对映射算法进行了实验验证,结果表明基于数据手套的人手指尖运动能够很好的映射到灵巧手指尖位置,进而保证了灵巧手抓取物体的准确性。
上述的映射尽管能够保证灵巧手能够拟合人手的运动,但是由于数据手套的传感器误差过大,致使当人手抓取物体时,灵巧手通过上述的映射方法仅能得到物体的“粗位置”。为了保证灵巧手抓取物体的稳定性,利用改进的E-ANFIS模型对抓取物体的形状和位姿进行重构。利用人手抓取物体时指尖映射到灵巧手的指尖笛卡尔坐标值作为混合智能模型的输入,进而快速准确地重构出被抓取物体的数学模型,得到抓握物体的“精位置”实现灵巧手的稳定抓取。
针对HIT/DLR II五指灵巧手的抓取特点,在基于位置的阻抗控制算法基础上,提出了一种具有手指的重力补偿、摩擦补偿的阻抗控制算法,为了克服灵巧手手指同硬质物体接触而出现的动态不确定因素,采用扩展卡尔曼滤波器来实现动态结构参数的自适应估计。
最后利用HIT臂/手系统及数据手套为实验系统平台,给出反应数据手套和灵巧手抓握映射能力的抓取实验。基于提出的阻抗控制算法,对手指进行了关节和笛卡尔阻抗控制实验,并对其控制性能进行了分析,最终验证了该控制方法的有效性。
The study of Dexterous hands is one of hotspots in current robots technology research and one of key technologies of intelligent space robots. The operation of dexterous hands is very important for the space robot. Based on national high technology research and development program (863)“Research on new generation five-fingered anthropopathic dexterous robot hand and its cooperative control”(code: 2006AA04Z255 ), With HIT/DLR II dexterous hand for the platform, this dissertation improves the motion mapping based on data glove, improves the E-ANFIS model and using the Model to build the grasped model, and then proposes a flexible impedance control algorithm, finally, achieves the grasping control of the dexterous hands using the control system with data love.
Based on human hand and HIT/DLR II dexterous hand, this dissertation establishes kinematics models of human hand and the dexterous hand, and analyses structural differences between the models. Then an improved fingertip position-based motion mapping is used to improve the mapping ability between the human hand and the dexterous hand, and experiment is made to prove that mapping algorithm is right or not. The results show that the position of the human hand is mapped to the dexterous hand very well. And it make sure that the veracity of grasping object.
Although the mapping mentioned can prove that the dexterous hand can move with the human hand, because of the error of the sensors in the data glove, when the human hand is grasping the object, the dexterous hand can only get the rough station information by the mapping method. Considering the grasping task of dexterous hand with unknown objects, the model of grasped object is constructed by hyperelliptic equation. The improved E-ANFIS model can export shape and posture information of the grasped object. So that the veracity of the grasped object is assured. Information of the dexterous hand is used in the model. The newly grasped model is more accurate for the realization of multi-fingered manipulation. When the human hand is grasping the object, the station of the finger-tip is mapped to the dexterous hand, and its coordinate is used as the input of the intelligent model. And then the model of the grasped object is remade, and the exactly information of the object is obtained to prove the steadily grasp.
Considering HIT/DLR II dexterous hand as the study object, a joint and Cartesian impedance controller with nonlinear compensation is designed. To improve the performance of the impedance controller, system parameter estimations with extended kalman filter and gravity compensation have been investigated on the robot hand.
Finally, using the experimental platform of dexterous robot hand teleoperation system, Firstly, training data of the intelligent E-ANFIS model is gathered using the data glove, the match training to it is carried on. The capture experiment is made to prove the relationship between data glove and the grasped model. A lot of experiments are made to prove the effectiveness of the the control method. The results prove that the algorithm of the controller is correct.
引文
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2姜力.具有力感知功能的机器人灵巧手手指及控制的研究.哈尔滨工业大学博士论文. 2001
3和平.基于物体阻抗的机器人灵巧手控制.哈尔滨工业大学博士论文. 2004
4 R. Tomovic, G. M. Boni. An Adaptive Artificial Hand. IRE Transactions on Automatic Control. 1962, 7(3): 3-10
5 T. Okada. Computer control of multijointed finger system for precise object-handing. IEEE Transactions on Systems, Man and Cybernetics. 1982, 12(3): 289-299
6 M. T. Mason, J. K. Salisbury. Robot Hands and the Mechanics of Manipulation. MIT Press, Cambridge, USA, 1985: 3~93
7 S. C. Jacobsen, J. E. Wood, D. F. Knutti, and K. B. Biggers. The UTAH/MIT Dexterous Hand: Work in Progress. The International Journal of Robotics Research. 1984, 3(4): 21~50
8 C. S. Lovchik, M. A. Difler. The Robonaut Hand: A Dextrous Robotic Hand for Space. Proceedings of the IEEE International Conference on Robotics and Automation. Detroit, Michigan. 1999: 907~912
9 Y. Nakano, M. Fujie, Y. Hosada. Hitachi’s Robot Hand. Robotics Age. Vol.6, No.7, Jul.1984: 18-20
10 H. Kawasaki, T. Mouri, S. Ito. Toward Next Stage of Kinetic Humanoid Hand. CD-ROM of World Automation Congress 10th Int. Symposium on Robotics with Applications, 2004
11 T. Mouri, H. Kawasaki, K. Yoshikawa, J. Takai, S. Ito. Anthropomorphic Robot Hand: Gifu Hand III. ICCAS, 2002, 10: 1288~1293
12 A. Caffaz, G. Cannata. The Design and Development of the DIST-Hand Dextrous Gripper. Proceedings of the IEEE International Conference on Robotics and Automation. Leuven, Belgium. 1998: 2075-2080
13 C. Melchiorri, G. Vassura. Implementation of Whole-Hand Manipulation Capability in the UB Hand System Design. Advanced Robotics. 1995,9(5):547-560
14 Shadow Robot Company. Developments in Dextrous Hands for Advanced Applications. Proceedings of the IEEE World Automation Congress. 2004:123~128
15 G. Hirzinger, M. Fischer, B. Brunner, et. al. Advances in Robotics: The DLR Experience. The International Journal of Robotics Research.1999, 18(1): 1064~1087
16 H. Liu, J. Butterfass, S. Knoch, P. Meusel, G. Hirzinger. A New Control Strategy for DLR’s Multisensory Articulated Hand. IEEE Control Systems. 1999, 19(2): 47~54
17刘宏, G. Hirzinger.智能机器人灵巧手的研究—DLR Dextrous Robot Hand I and II.西安交通大学学报. 2003, 37(4): 331~337
18 J. Butterfass, G. Hirzinger, S. Knoch, H. Liu. DLR’s Multisensory Articulated Hand, Part I: Hard-and Software Architecture. Proceedings of the IEEE International Conference on Robotics and Automation. Leuven, Belgium. 1998: 2081~2086
19 S. Haidacher, J. Butterfass, M. Fischer, et al. DLR Hand II: Hard- and Software Architecture for Information Processing, Proceedings of the 2003 International Conference on Robotics and Automation. 2003:684~689
20 J. Butterfass, M. Grebenstein, H. Liu and G. Hirzinger. DLR-Hand II: Next Generation of a Dextrous Robot Hand. Proceedings of the 2001 IEEE International Conference on Robotics and Automation. 2001, Seoul: 21-26
21王国庆,李大寨,钱锡康.新型三指灵巧机械手的研究.机械工程学报. 1997, 33(3): 71-75
22 Y. Zhang, Z. Han, H. Zhang, X. Shang, T. Wang, W. Guo. Design and Control of The BUAA Four-fingered Hand. Proceedings of the 2001 IEEE international Conference on Robotics & Automation. 2001, Seoul: 2517~2522
23谢宗武. HIT-1机器人灵巧手柔顺控制的研究.哈尔滨工业大学博士论文. 2003
24 X. H. Gao, M. H. Jin, L. Jiang, L. Yang. The HIT/DLR Dexterous Hand: Work in Progress. Proceeding of the 2003 IEEE International Conference on Robotics & Automation. 2003, Taipei: 3164~3168
25 H. Liu, P. Meusel, N. Seitz. The modular multisensory DLR-HIT-Hand. ELSEVIER of Mechanism and Machine Theory. 2006, (4): 1~15
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