Model-Free Energy-Based Friction Compensation for Industrial Collaborative Robots as Haptic Displays

被引:0
|
作者
Dinc, Huseyin Tugcan [1 ]
Lee, Joong-Ku [2 ]
Ryu, Jee-Hwan [2 ]
机构
[1] Korea Adv Inst Sci & Technol, Robot Program, Daejeon 34141, South Korea
[2] Korea Adv Inst Sci & Technol, Dept Civil & Environm Engn, Daejeon 34141, South Korea
基金
新加坡国家研究基金会;
关键词
Friction; Robots; Torque; Haptic interfaces; Adaptation models; Mathematical models; Kinetic energy; Adaptive friction compensation; collaborative robots; conservation of energy; IDENTIFICATION;
D O I
10.1109/TMECH.2024.3410330
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Collaborative robots are a promising alternative to traditional haptic displays due to their expansive workspaces, ability to generate substantial forces, and cost-effectiveness. However, they have higher levels of friction compared to conventional haptic displays, which negatively impact the precise movement and accuracy of haptic feedback, potentially causing operator fatigue. This article proposes a novel model-free, energy-based approach for estimating and compensating the friction coefficient. Our approach calculates the time-varying impact of frictional forces during an energy cycle, defined as the period between two consecutive zero crossings of the system's kinetic energy, based on the energy dissipated by friction. This approach directly estimates the friction coefficients, based on the chosen friction model, without requiring any prior system model information or tuning parameters. The effectiveness of our approach is demonstrated through single and multi-degree-of-freedom human interaction experiments using a Franka Emika Panda robot. The results indicate that the proposed approach outperforms state-of-the-art friction compensation methods.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Model-based Off-line Compensation of Path Deviation for Industrial Robots in Milling Applications
    Reinl, C.
    Friedmann, M.
    Bauer, J.
    Pischan, M.
    Abele, E.
    von Stryk, O.
    [J]. 2011 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2011, : 367 - 372
  • [42] Energy Saving Control of Bionic Robotic Fish based on Model-free Adaptive Control
    Zhang, Biying
    Jin, Shangtai
    Hou, Zhongsheng
    [J]. IFAC PAPERSONLINE, 2020, 53 (02): : 3934 - 3939
  • [43] Model-Free Dynamic Voltage Control of Distributed Energy Resource (DER)-Based Microgrids
    Hatipoglu, Kenan
    Olama, Mohammed
    Xue, Yaosuo
    [J]. ENERGIES, 2020, 13 (15)
  • [44] Model-free optimal decentralized sliding mode control for modular and reconfigurable robots based on adaptive dynamic programming
    Dong, Bo
    An, Tianjiao
    Zhou, Fan
    Yu, Weibo
    [J]. ADVANCES IN MECHANICAL ENGINEERING, 2019, 11 (12)
  • [45] Research on Gravity Compensation Algorithm of Model-free Manipulator Based on L1-norm Convex Optimization
    Yu, Chenglong
    Li, Zhiqi
    Liu, Hong
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO), 2018, : 1444 - 1450
  • [47] A Novel Energy Efficient Operation Strategy for a Train Based on Model-Free Adaptive Predictive Control
    Yang Wen
    Yin Chenkun
    Hou Zhongsheng
    [J]. PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE, 2012, : 7286 - 7291
  • [48] An energy exchanging and dropping-based model-free output feedback crane control method
    Sun, Ning
    Fang, Yongchun
    Sun, Xiuyun
    Xin, Zhekui
    [J]. MECHATRONICS, 2013, 23 (06) : 549 - 558
  • [49] Deep Reinforcement Learning with Inverse Jacobian based Model-Free Path Planning for Deburring in Complex Industrial Environment
    M. R. Rahul
    Shital S. Chiddarwar
    [J]. Journal of Intelligent & Robotic Systems, 2024, 110
  • [50] Deep Reinforcement Learning with Inverse Jacobian based Model-Free Path Planning for Deburring in Complex Industrial Environment
    Rahul, M. R.
    Chiddarwar, Shital S.
    [J]. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2024, 110 (01)