Methods: To more clearly describe the specific movements of each joint, the local POE method was introduced. For ease of analysis, the structure of the robot's passive arms was simplified using screw theory. A kinematic model for the Delta robot was established using the local POE method. The error model of the robot was obtained through the differential mapping of the exponential product. Based on the derived error model, error sources were subdivided into three major categories: structural errors, actuation angle errors, and spherical joint clearance errors. An in-depth analysis was conducted on how each error source affects the end-effector positioning accuracy of the robot when it moves along the X, Y, and Z directions. A Delta robot with active arm lengths of 400 mm and passive arm lengths of 950 mm was selected as the subject for simulation analysis in MATLAB. The square root of the sum of squared errors in the X, Y, and Z directions was used as a composite error to serve as an evaluation criterion. Results: The simulation results showed that assuming all error sources have a magnitude of 0.100 units (length unit being mm; angular unit being degrees), actuation angle errors had the most significant impact on the end-effector positioning accuracy of the Delta parallel robot, causing a composite error ranging from 1.500 to 2.000 mm. Spherical joint clearance errors caused a composite error of 0.340 mm in the robot. Structural errors exhibited a relatively stable composite error fluctuating around 0.100 mm, with a variation range of approximately 0.010 mm, which can be considered a constant value. Comprehensive analysis indicated that length errors in the active and passive arms significantly influenced end-effector positioning accuracy, with the induced error fluctuations notably larger than those from other sources. Additionally, when the magnitudes of error sources were 0.025 mm, 0.050 mm, 0.075 mm, and 0.100 mm, their impacts on robot positioning accuracy increased proportionally. Conclusions: The Delta robot error analysis model based on screw theory and utilizing the local POE method offers a more intuitive and comprehensive approach to analyzing the impact of major error sources on positioning accuracy compared to traditional error modeling methods. This approach effectively avoids issues of singularity and incompleteness. It provides theoretical reference for error modeling analysis of other parallel mechanisms. Through the assessment of the influence of each error source presented in this paper, during subsequent error compensation phases, more precise corrections can be made to the significantly impactful actuation angle errors, thereby effectively improving the efficiency and effectiveness of overall error compensation.
Deyong SHANG, Zhan PAN, Shuangfu SUO, Fan ZHANG. Error analysis of Delta robots based on screw theory[J]. Journal of Tsinghua University(Science and Technology), 2025, 65(7): 1336-1346. DOI: 10.16511/j.cnki.qhdxxb.2025.27.016
为研究并联机构的实际运动学特征,国内外学者在机构误差建模过程中应用了不同的数学方法。常用的方法有D-H参数法[8-9]和空间矢量法[10-12]。贺礼等[13]采用D-H方法建立了Delta机器人的误差模型,为Delta机器人的精度分析及误差补偿提供了分析工具,但是D-H参数法在建模过程中存在奇异性的问题。张文昌等[14]采用空间矢量法建立了Delta机器人的误差模型,完成了机器人运动学误差标定,但是使用运动学逆解方法构建的误差模型需要对部分结构进行理想假设,因此无法保证模型中包含所有潜在的误差源。近年来,利用指数积(product of exponentials,POE)公式建模的POE方法已被广泛应用于并联机器人的运动学描述,相比于D-H参数法和空间矢量法,基于旋量理论的POE方法充分利用了机器人的几何特性,物理意义明确,建立的模型表达更加清晰。根据Chasles定理,空间中物体的任意刚体运动可以等效为螺旋运动,即绕某轴的转动和沿平行某直线的移动。可以通过平滑改变旋量坐标来避免运动产生的奇异性问题,保证了采用POE方法建立模型的连续性,并且同时符合完整性和最小化的要求。Okamura和Park[15-16]采用全局POE方法建立了开环机械手的误差模型,在二者基础上,He等[17]进一步推导了指数映射关于关节旋量和关节变量微分的显式表达式,以加法的形式呈现。此外,Chen等[18]通过为每个杆件分配局部坐标系,结合局部POE方法分析了机构的误差。现有研究成果证明了旋量理论在并联机器人误差建模方面的优越性,但目前在Delta并联机器人误差建模及误差分析方面有待进一步研究。
假设各结构误差对末端定位精度的影响相互独立。设结构误差均为0.100 mm。为分析结构误差对机器人X、Y、Z三个方向的运动分别造成的影响,机器人运动空间定为X∈[—600 mm, 600 mm];Y∈[—600 mm, 600 mm];Z∈[650 mm, 1 000 mm],分析结果如图 6所示。以下分析将用综合误差表示对机器人末端误差造成的影响,其定义为
观察式(27)可知,当驱动角轴线位置处于理想状况下,仅存在驱动角度误差,即δξi2=0时,机器人末端位置误差与驱动角度误差成正比关系,即相同运动轨迹,驱动角度误差越大引起的末端综合误差越大。为分析3条支链的驱动角度误差分别对末端定位精度的影响,假设驱动角度误差均为0.100°,机器人运动空间定为X∈[—600 mm, 600 mm];Y∈[—600 mm, 600 mm];Z∈[650 mm, 1 000 mm],结果如图 8所示。
YAOR, ZHUW B, HUANGP. Accuracy analysis of stewart platform based on interval analysis method[J]. Chinese Journal of Mechanical Engineering, 2013, 26 (1): 29- 34.
WANGK, LIJ, SHENH P, et al. Inverse dynamics of A 3-DOF parallel mechanism based on analytical forward kinematics[J]. Chinese Journal of Mechanical Engineering, 2022, 35 (1): 119.
YANGY B, WANGM X. Modeling and analysis of position accuracy reliability of R(RPS&RP)& 2-UPS parallel mechanism[J]. Journal of Mechanical Engineering, 2023, 59 (15): 62- 72.
LIG M, QUH B, GUOS. Sensitivity analysis of a planar parallel manipulator with kinematic redundancy[J]. Journal of Mechanical Engineering, 2020, 56 (23): 45- 57.
XIEF G, LIUX J, CHENY Z. Error sensitivity analysis of novel virtual center mechanism with parallel kinematics[J]. Journal of Mechanical Engineering, 2013, 49 (17): 85- 91.
12
WUJ F, ZHANGR, WANGR H, et al. A systematic optimization approach for the calibration of parallel kinematics machine tools by a laser tracker[J]. International Journal of Machine Tools and Manufacture, 2014, 86, 1- 11.
ZHANGW C, MEIJ P, LIUY, et al. Calibration of Delta parallel robot kinematic errors based on laser tracker[J]. Journal of Tianjin University (Science and Technology), 2013, 46 (3): 257- 262.
15
PARK F C, OKAMURA K. Kinematic calibration and the product of exponentials formula[M]//LENAR AČG I AČG J, RAVANI B. Advances in Robot Kinematics and Computational Geometry. Dordrecht: Springer, 1994: 119-128.
16
OKAMURAK, PARKF C. Kinematic calibration using the product of exponentials formula[J]. Robotica, 1996, 14 (4): 415- 421.
HER B, ZHAOY J, YANGS N, et al. Kinematic- parameter identification for serial-robot calibration based on POE formula[J]. IEEE Transactions on Robotics, 2010, 26 (3): 411- 423.
WANGW, TIANW, LIAOW H, et al. Error compensation of industrial robot based on deep belief network and error similarity[J]. Robotics and Computer-Integrated Manufacturing, 2022, 73, 102220.
LIB, TIANW, ZHANGC F, et al. Positioning error compensation of an industrial robot using neural networks and experimental study[J]. Chinese Journal of Aeronautics, 2022, 35 (2): 346- 360.
LIK, ZHANGJ J, QIK C, et al. Error analysis and compensation of the six-dimensional controller based on the parallel mechanism with sub-closed chains[J]. Journal of Machine Design, 2019, 36 (9): 29- 35.
MURRAYR M, LIZ X, SASTRYS S, et al. A mathematical introduction to robotic manipulation[M]. Boca Raton: CRC Press, 1994.
24
孔令雨. 并联机构的运动学误差建模及参数可辨识性分析[D]. 上海: 上海交通大学, 2018.
KONG L Y. Kinematics error modeling and parameter identifiability analysis of parallel mechanism[D]. Shanghai: Shanghai Jiao Tong University, 2018. (in Chinese)
LUO Y K. Dynamic analysis on dynamics and simulation of delta parallel robot manipulator based on screw theory[D]. Changsha: Central South University of Forestry & Technology, 2017. (in Chinese)
26
SELIGJ M. Geometric fundamentals of robotics[M]. New York: Springer, 2005.