Maximum Allowable TCF Calibration Error for Robotic Pose Servoing

被引:1
|
作者
Hou, Jun [1 ,2 ]
Xing, Shiyu [1 ,2 ]
Ma, Yunkai [1 ,2 ]
Jing, Fengshui [1 ,2 ]
Tan, Min [3 ]
机构
[1] Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
[3] Chinese Acad Sci, Inst Automat, Key Lab Cognit & Decis Intelligence Complex Syst, Beijing 100190, Peoples R China
来源
IEEE ROBOTICS AND AUTOMATION LETTERS | 2025年 / 10卷 / 02期
基金
中国国家自然科学基金;
关键词
Calibration; Robots; Robot kinematics; Stars; Assembly; Servomotors; Robustness; Measurement uncertainty; Accuracy; Visualization; Formal methods in robotics and automation; calibration and identification; assembly;
D O I
10.1109/LRA.2024.3522840
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Robotic pose servoing aims to move the robot end-effector to the target pose. Closed-loop servo systems can tolerate a small TCF (tool control frame) calibration error and accurately reach the target pose through multiple pose measurements and pose adjustments. However, the maximum allowable TCF calibration error remains an open question. This paper demonstrates that the necessary condition for robotic pose servoing is a TCF calibration error angle of less than 60 degrees, with no limit on the translational component of the TCF calibration error. Next, an improved pose servoing method is proposed to address the conflict between the large TCF error and the limited robot workspace. This method introduces a scaling factor to limit the adjustment range within the robot workspace, ensuring greater robustness. Finally, robot-assisted cabin docking is selected as an experimental validation case. Simulation and physical experiments validate the maximum allowable TCF calibration error. Comparative experiments confirm the robustness of the improved pose servoing method, achieving cabin docking despite significant TCF calibration errors.
引用
收藏
页码:1744 / 1751
页数:8
相关论文
共 50 条
  • [41] GPU-Enabled particle based optimization for robotic-hand pose estimation and self-calibration
    Vicente, Pedro
    Ferreira, Ricardo
    Jamone, Lorenzo
    Bernardino, Alexandre
    2015 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC), 2015, : 3 - 8
  • [42] Analysis of unified error model and simulated parameters calibration for robotic machining based on Lie theory
    Fu, Zhongtao
    Dai, Jian S.
    Yang, Kun
    Chen, Xubing
    Lopez-Custodio, Pablo
    ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2020, 61
  • [43] The Role of Absolute Positioning Error in Hand-Eye Calibration and Robotic Guidance Systems: An Analysis
    Chalus, Michal
    Vanicek, Ondrej
    Liska, Jindrich
    2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, IROS, 2023, : 1797 - 1802
  • [44] Easy Pose-Error Calibration for Articulated Serial Robot Based on Three-Closed-Loop Transformations
    Cai, Mingjun
    Liu, Huashan
    Dong, Menghua
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
  • [45] Easy Pose-Error Calibration for Articulated Serial Robot Based on Three-Closed-Loop Transformations
    Cai, Mingjun
    Liu, Huashan
    Dong, Menghua
    IEEE Transactions on Instrumentation and Measurement, 2021, 70
  • [46] Free and Global Pose Calibration of a Rotating Laser Monocular Vision Sensor for Robotic 3D Measurement System
    Li Lingmin
    Xi Juntong
    INTERNATIONAL CONFERENCE ON OPTICS IN PRECISION ENGINEERING AND NANOTECHNOLOGY (ICOPEN2013), 2013, 8769
  • [47] A method for calibrating robotic kinematic parameters based on a multi-error source model and an optimized measurement pose set
    Cheng, Bo
    Wang, Bo
    Chen, Shujun
    Zhang, Ziqiang
    Xiao, Jun
    INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2025,
  • [48] A high-precision calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations
    Qiu, Dongwei
    Li, Shaofu
    Wang, Tong
    Ye, Qing
    Li, Ruijie
    Ding, Keliang
    Xu, Hao
    MEASUREMENT, 2020, 153
  • [49] A Maximum-Likelihood-Estimation-based Digital Background Calibration Technique for Interstage Gain Error in Pipelined ADCs
    Cao, Tianxiang
    Xu, Zhiwei
    Han, Shuai
    Ding, Kaijie
    Zhu, Jiang
    2024 9TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS, ICCCS 2024, 2024, : 167 - 171
  • [50] Estimating maximum allowable analytical error in whole blood glucose meter measurements from meter downloads of 35 former-DCCT study patients.
    Cembrowski, GS
    Helou, E
    Bergenstal, R
    Upham, P
    CLINICAL CHEMISTRY, 1998, 44 : A156 - A156