Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T05:19:18.989Z Has data issue: false hasContentIssue false
  • English
  • Français

Unknown External Force Estimation and Collision Detection for a Cooperative Robot

Published online by Cambridge University Press:  20 December 2019

Shirin Yousefizadeh Open the ORCID record for Shirin Yousefizadeh [Opens in a new window]  and
Thomas Bak
Shirin Yousefizadeh*
Affiliation:
Department of Electronic Systems, Aalborg University, Fredrik Bajres Vej 7C, Aalborg Øst 9220, Denmark. E-mail: [email protected]
Thomas Bak
Affiliation:
Department of Electronic Systems, Aalborg University, Fredrik Bajres Vej 7C, Aalborg Øst 9220, Denmark. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In human–robot cooperative industrial manipulators, safety issues are crucial. To control force safely, contact force information is necessary. Since force/torque sensors are expensive and hard to integrate into the robot design, estimation methods are used to estimate external forces. In this paper, the goal is to estimate external forces acting on the end-effector of the robot. The forces at the task space affect the joint space torques. Therefore, by employing an observer to estimate the torques, the task space forces can be obtained. To accomplish this, loadcells are employed to compute the net torques at the joints. The considered observers are extended Kalman filter (EKF) and nonlinear disturbance observer (NDOB). Utilizing the computed torque obtained based on the loadcells measurements and the observer, the estimates of external torques applied on the robot end-effector can be achieved. Moreover, to improve the degree of safety, an algorithm is proposed to distinguish between intentional contact force from an operator and accidental collisions. The proposed algorithms are demonstrated on a robot, namely WallMoBot, which is designed to help the operator to install heavy glass panels. Simulation results and preliminary experimental results are presented to demonstrate the effectiveness of the proposed methods in estimating the joint space torques generated by the external forces applied to the WallMoBot end-effector and to distinguish between the user-input force and accidental collisions.

Keywords

External force estimationCollision detectionCooperative robotExtended Kalman filter (EKF)Nonlinear Disturbance Observer (NDOB)
Type
Articles
Information
Robotica , Volume 38 , Issue 9 , September 2020 , pp. 1665 - 1681
Copyright
© Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hersh, M., “Overcoming barriers and increasing independence–service robots for elderly and disabled people,” Int. J. Adv. Rob. Syst. 12(8), 114 (1972).10.5772/59230CrossRefGoogle Scholar
Sloth, C. and Pedersen, R., “Control of wall mounting robot,” IFAC-PapersOnLine 50(1), 5648–5653 (2017).10.1016/j.ifacol.2017.08.1113CrossRefGoogle Scholar
Soltanpour, M. R. and Khooban, M. H., “A particle swarm optimization approach for fuzzy sliding mode control for tracking the robot manipulator,” Nonlinear Dyn. 74(1–2), 467–478 (2013).10.1007/s11071-013-0983-8CrossRefGoogle Scholar
Luo, J. and Hauser, K., “Robust trajectory optimization under frictional contact with iterative learning,” Auton. Rob. 41(6), 1447–1461 (2017).10.1007/s10514-017-9629-xCrossRefGoogle Scholar
Asai, K. and Ulusoy, G., Computer Integrated Manufacturing: Current Status and Challenges, vol. 49 (Springer Science & Business Media, Berlin, Heidelberg, 2012).Google Scholar
Colomé, A., Pardo, D., Alenya, G. and Torras, C., “External Force Estimation During Compliant Robot Manipulation,” IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe (2013) pp. 3535–3540.Google Scholar
Novak, J. L. and Feddema, I., “A Capacitance-Based Proximity Sensor for Whole Arm Obstacle Avoidance,” IEEE International Conference On Robotics and Automation (ICRA), Nice, France (1992) pp. 1307–1314.Google Scholar
Nuelle, K., Schulz, M. J., Aden, S., Dick, A., Munske, B., Gaa, J., Kotlarski, J. and Ortmaier, T., “Force Sensing, Low-Cost Manipulator in Mobile Robotics,” IEEE International Conference on Robotics and Automation (ICRA), Marina Bay Sands, Singapore (2017) pp. 196–201.Google Scholar
Ghalyan, I. F. J., Force-Controlled Robotic Assembly Processes of Rigid and Flexible Objects (Springer, Switzerland, 2016).10.1007/978-3-319-39185-4CrossRefGoogle Scholar
De Luca, A., Albu-Schaffer, A., Haddadin, S. and Hirzinger, G., “Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (2006) pp. 1623–1630.10.1109/IROS.2006.282053CrossRefGoogle Scholar
Eom, K. S., Suh, I. H., Chung, W. K. and Oh, S. R., “Disturbance Observer Based Force Control of Robot Manipulator Without Force Sensor,” IEEE International Conference On Robotics and Automation (ICRA), Leuven, Belgium, vol. 4 (1998) pp. 3012–3017.Google Scholar
Chan, L., Naghdy, F. and Stirling, D., “Extended active observer for force estimation and disturbance rejection of robotic manipulators,” Rob. Auton. Syst. 61(12), 1277–1287 (2013).10.1016/j.robot.2013.09.003CrossRefGoogle Scholar
Liao, G., Sheng, Y. and Zeng, X., “Spacecraft Hovering Around Asteroid via Disturbance Observer Based Exponential Time-Varying Sliding Mode Controller,” Proceedings of the 13th IEEE International Conference on Control & Automation (ICCA), Ohrid, Macedonia (2017) pp. 313–318.Google Scholar
Mitsantisuk, C., Ohishi, K., Urushihara, S. and Katsura, S., “Kalman filter-based disturbance observer and its applications to sensorless force control,” Adv. Rob. 25(3–4), 335–353 (2011).10.1163/016918610X552141CrossRefGoogle Scholar
Nagatsu, Y. and Katsura, S., “High-Order Disturbance Estimation Using Kalman Filter for Precise Reaction-Torque Control,” IEEE International Workshop on Advanced Motion Control (AMC), Auckland (2016) pp. 79–84.Google Scholar
Hu, J. and Xiong, R., “Contact force estimation for robot manipulator using semi-parametric model and disturbance Kalman filter,” IEEE Trans. Ind. Electron. 65(4), 3365–3375 (2018).CrossRefGoogle Scholar
Mohammadi, A., Marquez, H. J. and Tavakoli, M., “Nonlinear disturbance observers: Design and applications to Euler-Lagrange systems,” IEEE Control Syst. 37(4), 50–72 (2017).Google Scholar
Grewal, M. S., “Kalman Filtering,” In:International Encyclopedia of Statistical Science (Springer, Berlin, Heidelberg, 2011) pp. 705–708.10.1007/978-3-642-04898-2_321CrossRefGoogle Scholar
Hartikainen, J., Solin, A. and Särkkä, S., “Optimal Filtering with Kalman Filters and Smoothers”, 16 (Department of Biomedica Engineering and Computational Sciences, Aalto University School of Science, Greater Helsinki, Finland, 2011).Google Scholar
Zelenak, A., Pryor, M. and Schroeder, K., “An Extended Kalman Filter for Collision Detection During Manipulator Contact Tasks,” American Society of Mechanical Engineers (ASME) Dynamic Systems and Control Conference (2014) pp. V001T11A005–V001T11A005.Google Scholar
Liang, W., Huang, S., Chen, S. and Tan, K. K., “Force estimation and failure detection based on disturbance observer for an ear surgical device,” ISA Trans 66(7), 476–484 (2017).CrossRefGoogle Scholar
Smith, A. C., Mobasser, F., and Hashtrudi-Zaad, K., “Neural-network-based contact force observers for haptic applications,” IEEE Trans. Rob. 22(6), 1163–1175 (2006).10.1109/TRO.2006.882923CrossRefGoogle Scholar
Cousineau, L. and Miura, N., Construction Robots: The Search for New Building Technology in Japan (ASCE Publications, Virginia, 1998).Google Scholar
Chen, Y., Turner, S., McNamee, R., Ramsay, C. N. and Agius, R. M., “The reported incidence of work-related ill-health in Scotland (2002–2003),” Occup. Med. 55(4), 252–261 (2005).CrossRefGoogle ScholarPubMed
Hein, B. and Hensel, M. and Worn, H., “Intuitive and Model-Based On-line Programming of Industrial Robots: A Modular On-line Programming Environment,” IEEE International Conference on Robotics and Automation (ICRA), Pasadena, California (2008) pp. 3952–3957.Google Scholar
Park, Y. G. and Chung, W. K., “Unified External Torque-Sensing Algorithm for Flexible-Joint Robot Based on Kalman Filter,” IEEE International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Jeju (2013) pp. 78–79.Google Scholar
Bishop, G. and Welch, G., “An Introduction to the Kalman Filter,” Proceedings of SIGGRAPH, course 8, no. 27599–23175, vol. 8 (2001) p. 35.Google Scholar
Yousefizadeh, S. and Bak, T., “Nonlinear Disturbance Observers for External Force Estimation in a Cooperative Robot,” 19th International Conference on Advanced Robotics (ICAR), Brazil (2019).CrossRefGoogle Scholar
Tseng, C. S. and Tompkins, C. K., “Fuzzy observer-based fuzzy control design for nonlinear systems with persistent bounded disturbances,” Fuzzy Sets Syst. 158(2), 164–179 (2007).10.1016/j.fss.2006.09.014CrossRefGoogle Scholar
Flash, T. and Hogan, N., “The coordination of arm movements: An experimentally confirmed mathematical model,” J. Neurosci. 5(7), 1688–1703 (1985).CrossRefGoogle Scholar
Pan, J. and Tompkins, W. J., “A real-time QRS detection algorithm,” IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985).10.1109/TBME.1985.325532CrossRefGoogle Scholar
Rangayyan, R. M., Biomedical Signal Analysis, vol. 33 (John Wiley & Sons, Hoboken, NJ, 2015)CrossRefGoogle Scholar