Research article Topical Sections

Collision detection and external force estimation for robot manipulators using a composite momentum observer


  • Received: 16 March 2024 Revised: 23 April 2024 Accepted: 30 April 2024 Published: 10 May 2024
  • The collision detection and estimation of external forces for robot manipulators are essential to ensure compliance and safety in the interaction between the robot and the environment or humans. The focus of this paper was to design a hybrid approach for collision detection between robots and their environment, and further to estimate external forces acting on a robot manipulator without the need for additional sensors. The current collision detection methods using observers are still suffering from the problem of an unavoidable trade-off between the estimation sensitivity and the reduction of the peaking value at the initial time. To satisfy both robustness and avoid peaking phenomenon at the initial time, a composite observer was designed, consisting of both a momentum observer and an extended state observer. The first observer provides high-precision tracking, while the second one reduces the peak value at the start. Through their complementary roles, the composite observer achieves improved performance in terms of sensitivity and reducing the peaking value. Simulation results, conducted using a 2-degree-of-freedom (2-DOF) robot manipulator, attest to the efficacy of the proposed approach.

    Citation: Benaoumeur Ibari, Mourad Hebali, Baghdadi Rezali, Menaouer Bennaoum. Collision detection and external force estimation for robot manipulators using a composite momentum observer[J]. AIMS Electronics and Electrical Engineering, 2024, 8(2): 237-254. doi: 10.3934/electreng.2024011

    Related Papers:

  • The collision detection and estimation of external forces for robot manipulators are essential to ensure compliance and safety in the interaction between the robot and the environment or humans. The focus of this paper was to design a hybrid approach for collision detection between robots and their environment, and further to estimate external forces acting on a robot manipulator without the need for additional sensors. The current collision detection methods using observers are still suffering from the problem of an unavoidable trade-off between the estimation sensitivity and the reduction of the peaking value at the initial time. To satisfy both robustness and avoid peaking phenomenon at the initial time, a composite observer was designed, consisting of both a momentum observer and an extended state observer. The first observer provides high-precision tracking, while the second one reduces the peak value at the start. Through their complementary roles, the composite observer achieves improved performance in terms of sensitivity and reducing the peaking value. Simulation results, conducted using a 2-degree-of-freedom (2-DOF) robot manipulator, attest to the efficacy of the proposed approach.



    加载中


    [1] Haninger K, Radke M, Vick A, Krüger J (2022) Towards high-payload admittance control for manual guidance with environmental contact. IEEE Robot Autom Lett 7: 4275–4282. https://doi.org/10.1109/LRA.2022.3150051 doi: 10.1109/LRA.2022.3150051
    [2] Villani V, Pini F, Leali F, Secchi C (2018) Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics 55: 248–266. https://doi.org/10.1016/j.mechatronics.2018.02.009 doi: 10.1016/j.mechatronics.2018.02.009
    [3] Vicentini F (2021) Collaborative robotics: a survey. J Mech Design 143: 040802. https://doi.org/10.1115/1.4046238 doi: 10.1115/1.4046238
    [4] Ferraguti F, Landi CT, Singletary A, Lin HC, Ames A, Secchi C, et al. (2022) Safety and efficiency in robotics: The control barrier functions approach. IEEE Robot Autom Mag 29: 139–151. https://doi.org/10.1109/mra.2022.3174699 doi: 10.1109/mra.2022.3174699
    [5] Cirillo A, Ficuciello F, Natale C, Pirozzi S, Villani L (2015) A conformable force/tactile skin for physical human–robot interaction. IEEE Robot Autom Lett 1: 41–48. https://doi.org/10.1109/LRA.2015.2505061 doi: 10.1109/LRA.2015.2505061
    [6] Pang G, Yang G, Heng W, Ye Z, Huang X, Yang H, et al. (2020) CoboSkin: Soft robot skin with variable stiffness for safer human–robot collaboration. IEEE T Ind Electron 68: 3303–3314. https://doi.org/10.1109/TIE.2020.2978728 doi: 10.1109/TIE.2020.2978728
    [7] Hughes D, Lammie J, Correll N (2018) A robotic skin for collision avoidance and affective touch recognition. IEEE Robot Autom Lett 3: 1386–1393. https://doi.org/10.1109/LRA.2018.2799743 doi: 10.1109/LRA.2018.2799743
    [8] Ye Z, Pang G, Xu K, Hou Z, Lv H, Shen Y, et al. (2022) Soft robot skin with conformal adaptability for on-body tactile perception of collaborative robots. IEEE Robot Autom Lett 7: 5127–5134. https://doi.org/10.1109/LRA.2022.3155225 doi: 10.1109/LRA.2022.3155225
    [9] Flacco F, Kröger T, De Luca A, Khatib O (2012) A depth space approach to human-robot collision avoidance. 2012 IEEE International Conference On Robotics And Automation 338–345. https://doi.org/10.1109/ICRA.2012.6225245 doi: 10.1109/ICRA.2012.6225245
    [10] Wisanuvej P, Liu J, Chen C, Yang G (2014) Blind collision detection and obstacle characterisation using a compliant robotic arm. 2014 IEEE International Conference On Robotics And Automation (ICRA) 2249–2254. https://doi.org/10.1109/ICRA.2014.6907170 doi: 10.1109/ICRA.2014.6907170
    [11] Sandykbayeva D, Kappassov Z, Orazbayev B (2022) Vibrotouch: Active tactile sensor for contact detection and force sensing via vibrations. Sensors 22: 6456. https://doi.org/10.3390/s22176456 doi: 10.3390/s22176456
    [12] Katsampiris-Salgado K, Haninger K, Gkrizis C, Dimitropoulos N, Krüger J, Michalos G, et al. (2024) Collision detection for collaborative assembly operations on high-payload robots. Robot Com-Int Manuf 87: 102708. https://doi.org/10.1016/j.rcim.2023.102708 doi: 10.1016/j.rcim.2023.102708
    [13] Sharkawy A, Koustoumpardis P, Aspragathos N (2019) Manipulator collision detection and collided link identification based on neural networks. Advances In Service And Industrial Robotics: Proceedings Of The 27th International Conference On Robotics In Alpe-Adria Danube Region (RAAD 2018) 3–12. https://doi.org/10.1007/978-3-030-00232-9_1 doi: 10.1007/978-3-030-00232-9_1
    [14] Min F, Wang G, Liu N (2019) Collision detection and identification on robot manipulators based on vibration analysis. Sensors 19: 1080. https://doi.org/10.3390/s19051080 doi: 10.3390/s19051080
    [15] Heo Y, Kim D, Lee W, Kim H, Park J, Chung W (2019) Collision detection for industrial collaborative robots: A deep learning approach. IEEE Robot Autom Lett 4: 740–746. https://doi.org/10.1109/LRA.2019.2893400 doi: 10.1109/LRA.2019.2893400
    [16] Narukawa K, Yoshiike T, Tanaka K, Kuroda M (2017) Real-time collision detection based on one class SVM for safe movement of humanoid robot. 2017 IEEE-RAS 17th International Conference On Humanoid Robotics (Humanoids) 791–796. https://doi.org/10.1109/HUMANOIDS.2017.8246962 doi: 10.1109/HUMANOIDS.2017.8246962
    [17] Dimeas F, Avendaño-Valencia L, Aspragathos N (2015) Human-robot collision detection and identification based on fuzzy and time series modelling. Robotica 33: 1886–1898. https://doi.org/10.1017/S0263574714001143 doi: 10.1017/S0263574714001143
    [18] Jing X, Guangxin W, Chongyang L, Hong L (2016) Real-time collision detection for manipulators based on fuzzy synthetic evaluation. 2016 IEEE International Conference On Mechatronics And Automation 777–782. https://doi.org/10.1109/ICMA.2016.7558661 doi: 10.1109/ICMA.2016.7558661
    [19] Sedigh Ziyabari H, Aliyari Shoorehdeli M (2018) Fuzzy robust fault estimation scheme for a class of nonlinear systems based on an unknown input sliding mode observer. J Vib Control 24: 1861–1873. https://doi.org/10.1177/1077546316669 doi: 10.1177/1077546316669
    [20] Caccavale F, Walker I (1997) Observer-based fault detection for robot manipulators. Proceedings Of International Conference On Robotics And Automation 4: 2881–2887. https://doi.org/10.1109/ROBOT.1997.606724 doi: 10.1109/ROBOT.1997.606724
    [21] Morinaga S, Kosuge K (2003) Collision detection system for manipulator based on adaptive impedance control law. 2003 IEEE International Conference On Robotics And Automation (Cat. No. 03CH37422) 1: 1080–1085. https://doi.org/10.1109/ROBOT.2003.1241736 doi: 10.1109/ROBOT.2003.1241736
    [22] Haddadin S (2013) Towards safe robots: Approaching Asimov's 1st law., Springer Tracts in Advanced Robotics, Vol. 90, Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-642-40308-8
    [23] Hu J, Xiong R (2017) Contact force estimation for robot manipulator using semiparametric model and disturbance Kalman filter. IEEE T Ind Electron 65: 3365–3375. https://doi.org/10.1109/TIE.2017.2748056 doi: 10.1109/TIE.2017.2748056
    [24] De Luca A, Mattone R (2003) Actuator failure detection and isolation using generalized momenta. 2003 IEEE International Conference On Robotics And Automation (cat. No. 03CH37422) 1: 634–639. https://doi.org/10.1109/ROBOT.2003.1241665 doi: 10.1109/ROBOT.2003.1241665
    [25] Haddadin S, De Luca A, Albu-Schäffer A (2017) Robot collisions: A survey on detection, isolation, and identification. IEEE T Robot 33: 1292–1312. https://doi.org/10.1109/TRO.2017.2723903 doi: 10.1109/TRO.2017.2723903
    [26] Garofalo G, Mansfeld N, Jankowski J, Ott C (2019) Sliding mode momentum observers for estimation of external torques and joint acceleration. 2019 International Conference On Robotics And Automation (ICRA) 6117–6123. https://doi.org/10.1109/ICRA.2019.8793529 doi: 10.1109/ICRA.2019.8793529
    [27] Long S, Dang X, Sun S, Wang Y, Gui M (2022) A Novel Sliding Mode Momentum Observer for Collaborative Robot Collision Detection. Machines 10: 818. https://doi.org/10.3390/machines10090818 doi: 10.3390/machines10090818
    [28] Zhang C, Mu C, Wang Y, Li J, Liu Z (2023) Collision detection for six-DOF serial robots force/position hybrid control based on continuous friction model. Measurement and Control 56: 571–582. https://doi.org/10.1177/00202940221091575 doi: 10.1177/00202940221091575
    [29] Abdelaziz O, Luo M, Jiang G, Chen S (2020) Adaptive threshold for robot manipulator collision detection using fuzzy system. SN Appl Sci 2: 319. https://doi.org/10.1007/s42452-020-2110-z doi: 10.1007/s42452-020-2110-z
    [30] Birjandi S, Kühn J, Haddadin S (2020) Observer-extended direct method for collision monitoring in robot manipulators using proprioception and IMU sensing. IEEE Robot Autom Lett 5: 954–961. 10.1109/LRA.2020.2967287
    [31] Ren T, Dong Y, Wu D, Chen K (2018) Collision detection and identification for robot manipulators based on extended state observer. Control Eng Pract 79: 44–153. https://doi.org/10.1016/j.conengprac.2018.07.004 doi: 10.1016/j.conengprac.2018.07.004
    [32] Li Y, Li Y, Zhu M, Xu Z, Mu D (2021) A nonlinear momentum observer for sensorless robot collision detection under model uncertainties. Mechatronics 78: 102603. https://doi.org/10.1016/j.mechatronics.2021.102603 doi: 10.1016/j.mechatronics.2021.102603
    [33] Murray R, Li Z, Sastry S, Sastry S (1994) A mathematical introduction to robotic manipulation, 1st Eds., California, Boca Raton: CRC press, 519. https://doi.org/10.1201/9781315136370
    [34] De Luca A, Mattone R (2005) Sensorless robot collision detection and hybrid force/motion control. Proceedings Of The 2005 IEEE International Conference On Robotics And Automation 999–1004. https://doi.org/10.1109/ROBOT.2005.1570247 doi: 10.1109/ROBOT.2005.1570247
    [35] De Luca A, Albu-Schaffer A, Haddadin S, Hirzinger G (2006) Collision detection and safe reaction with the DLR-Ⅲ lightweight manipulator arm. 2006 IEEE/RSJ International Conference On Intelligent Robots And Systems 1623–1630. https://doi.org/10.1109/IROS.2006.282053 doi: 10.1109/IROS.2006.282053
    [36] Han J (2009) From PID to active disturbance rejection control. IEEE T Ind Electron 56: 900–906. https://doi.org/10.1109/TIE.2008.2011621 doi: 10.1109/TIE.2008.2011621
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(246) PDF downloads(32) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog