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Robot manipulators play a crucial role in various industrial and research settings, requiring precise and controlled interactions with their surroundings. Achieving this goal with fewer sensors offers advantages not only in terms of cost and decreased risk of failure but also enhances accuracy and long-term reliability. In this paper, we introduce a nonlinear force/position controller that eliminates the requirement for velocity measurements. This controller provides versatility by facilitating the generation of bounded control actions during robot-environment interactions, ensuring a higher level of safety for both the robot and its environment during the execution of tasks necessitating physical contact between them. The proposed approach is underpinned by a stability analysis in the Lyapunov sense and has been validated through a series of simulation and experimental tests.
Czasopismo
Rocznik
Tom
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437--468
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wzory
Twórcy
autor
- Robotics Engineering Department, Autonomous University of Aguascalientes, Prol. Mahatma Gandhi 6601, Aguascalientes, 20340, Aguascalientes, Mexico
- Faculty of Sciences, Autonomous University of San Luis Potosi, Av. Chapultepec 1570, Privadas del Pedregal, San Luis Potosi, 78295, San Luis Potosi, Mexico
autor
- Faculty of Sciences, Autonomous University of San Luis Potosi, Av. Chapultepec 1570, Privadas del Pedregal, San Luis Potosi, 78295, San Luis Potosi, Mexico
- Faculty of Engineering, Autonomous University of San Luis Potosi,Dr. Manuel Nava 8, Zona Universitaria Poniente, San Luis Potosi, 78290, San Luis Potosi, Mexico
Bibliografia
- [1] J. Cho, D. Choi and J.H. Park: Sensorless variable admittance control for human-robot interaction of a dual-arm social robot. IEEE Access, 11 (2023), 69366-69377. DOI: 10.1109/ACCESS.2023.3292933
- [2] S.E. Ovur and Y. Demiris: Naturalistic robot-to-human bimanual handover in complex environments through multi-sensor fusion. IEEE Transactions on Automation Science and Engineering, (2023). DOI: 10.1109/TASE.2023.3284668
- [3] Q. Wu, H. Liu and Y. Chen: Development of a hierarchical control strategy for a soft knee exoskeleton based on wearable multi-sensor system. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 237(9), (2023), 1587-1601. DOI: 10.1177/09596518231165345
- [4] M. Javaid, A. Haleem, R.P. Singh and R. Suman: Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics, 1 (2021), 58-75. DOI: 10.1016/j.cogr.2021.06.001
- [5] A. Buerkle, W. Eaton, N. Lohse, T. Bamber and P. Ferreira: EEG based arm movement intention recognition towards enhanced safety in symbiotic human-robot collaboration. Robotics and Computer-Integrated Manufacturing, 70 (2021), 102137. DOI: 10.1016/j.rcim.2021.102137
- [6] S. Wang, J. Zhang, P. Wang, J. Law, R. Calinescu and L. Mihaylova: A deep learning-enhanced digital twin framework for improving safety and reliability in human-robot collaborative manufacturing. Robotics and Computer-Integrated Manufacturing, 85 (2024), 102608. DOI: 10.1016/j.rcim.2023.102608
- [7] G. Wang, Z. Wang, B. Huang, Y. Gan and F. Min: Active compliance control based on EKF torque fusion for robot manipulators. IEEE Robotics and Automation Letters, 8(5), (2023), 2668-2675. DOI: 10.1109/LRA.2023.3258697
- [8] Y. Wu, H. Fang, T. Xu and F. Wan: Adaptive fixed-time minimal learning force/position control of uncertain manipulators subject to input saturation. International Journal of Adaptive Control and Signal Processing, 37(3), (2023), 790-810. DOI: 10.1002/acs.3549
- [9] M. Iskandar, C. Ott, A. Albu-Schäffer, B. Siciliano and A. Dietrich: Hybrid force-impedance control for fast end-effector motions. IEEE Robotics and Automation Letters, 8(7), (2023), 3931-3938. DOI: 10.1109/LRA.2023.3270036
- [10] C. Chávez-Olivares, F. Reyes-Cortés and E. González-Galván: On explicit force regulation with active velocity damping for robot manipulators. Automatika, 56(4), (2015), 478-490. DOI: 10.1080/00051144.2015.11828661
- [11] L. Rojas-García, I. Bonilla-Gutiérrez, M. Mendoza-Gutiérrez and C. Chávez-Olivares: Adaptive force/position control of robot manipulators with bounded inputs. Journal of Mechanical Science and Technology, 36(3), (2022), 1497-1509. DOI: 10.1007/s12206-022-0236-1
- [12] L. Rojas-García, M. Mendoza, I. Bonilla and C. Chávez-Olivares: Adaptive force control with active damping for robot manipulators with bounded inputs. Computational and Applied Mathematics, 41(6), (2022), 266. DOI: 10.1007/s40314-022-01976-2
- [13] I. Bonilla, M. Mendoza, D.U. Campos-Delgado and D.E. Hernández-Alfaro: Adaptive impedance control of robot manipulators with parametric uncertainty for constrained path-tracking. International Journal of Applied Mathematics and Computer Science, 28(2), (2018), 363-374. DOI: 10.2478/amcs-2018-0027
- [14] V.I. Ramírez-Vera, M.O. Mendoza-Gutiérrez and I. Bonilla-Gutiérrez: Impedance control with bounded actions for human-robot interaction. Arabian Journal for Science and Engineering, 47(11), (2022), 14989-15000. DOI: 10.1007/s13369-022-06638-3
- [15] M. Guo, H. Zhang, C. Feng, M. Liu and J. Huo: Manipulator residual estimation and its application in collision detection. Industrial Robot: An International Journal, 45(3), (2018), 354-362. DOI: 10.1108/IR-01-2018-0019
- [16] P. Cao, Y. Gan and X. Dai: Finite-time disturbance observer for robotic manipulators. Sensors, 19(8), (2019). DOI: 10.3390/s19081943
- [17] T. Sun and H. Liu: Adaptive force and velocity control based on intrinsic contact sensing during surface exploration of dynamic objects. Autonomous Robots, 44(5), (2020), 773-790. DOI: 10.1007/s10514-019-09896-7
- [18] J. Hu and R. Xiong: Contact force estimation for robot manipulator using semiparametric model and disturbance Kalman filter. IEEE Transactions on Industrial Electronics, 65(4), (2018), 3365-3375. DOI: 10.1109/TIE.2017.2748056
- [19] G. Peng, C. Yang, W. He and C.L.P. Chen: Force sensorless admittance control with neural learning for robots with actuator saturation. IEEE Transactions on Industrial Electronics, 67(4), (2020), 3138-3148. DOI: 10.1109/TIE.2019.2912781
- [20] M. Hanafusa and J. Ishikawa: Mechanical impedance control of cooperative robot during object manipulation based on external force estimation using recurrent neural network. Unmanned Systems, 8(3), (2020), 239-251. DOI: 10.1142/S230138502050017X
- [21] S. Yao, Y. Zhuang, Z. Li and R. Song: Adaptive admittance control for an ankle exoskeleton using an EMG-driven musculoskeletal model. Frontiers in Neurorobotics, 12 (2018). DOI: 10.3389/fnbot.2018.00016
- [22] P. Song, Y. Yu and X. Zhang: A tutorial survey and comparison of impedance control on robotic manipulation. Robotica, 37(5), (2019), 801-836. DOI: 10.1017/S0263574718001339
- [23] H. Hu, X. Wang and L. Chen: Impedance with finite-time control scheme for robot-environment interaction. Mathematical Problems in Engineering, 2020 (2020), 2796590. DOI: 10.1155/2020/2796590
- [24] M. Lin, H. Wang, J. Niu, Y. Tian, X. Wang, G. Liu and L. Sun: Adaptive admittance control scheme with virtual reality interaction for robot-assisted lower limb strength training. Machines, 9(11), (2021). DOI: 10.3390/machines9110301
- [25] A. Marban, V. Srinivasan, W. Samek, J. Fernández and A. Casals: A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery. Biomedical Signal Processing and Control, 50 (2019), 134-150. DOI: 10.1016/j.bspc.2019.01.011
- [26] D.-K. Ko, K.-W. Lee, D.H. Lee and S.-C. Lim: Vision-based interaction force estimation for robot grip motion without tactile/force sensor. Expert Systems with Applications, 211 (2023), 118441. DOI: 10.1016/j.eswa.2022.118441
- [27] J. Jiang and S. Luo: Chapter 2: Robotic perception of object properties using tactile sensing. In: Q. Li, S. Luo, Z. Chen, C. Yang, J. Zhang (Eds.), Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation, Academic Press, 2022, pp. 23-44. DOI: 10.1016/B978-0-32-390445-2.00009-X
- [28] J. Xu, J. Pan, T. Cui, S. Zhang, Y. Yang and T.-L. Ren: Recent progress of tactile and force sensors for human-machine interaction. Sensors, 23(4), (2023). DOI: 10.3390/s23041868
- [29] Z. Deng, Y. Jonetzko, L. Zhang and J. Zhang: Grasping force control of multi-fingered robotic hands through tactile sensing for object stabilization. Sensors, 20(4), (2020). DOI: 10.3390/s20041050
- [30] A. Wahrburg, J. Bös, K.D. Listmann, F. Dai, B. Matthias and H. Ding: Motor-current-based estimation of cartesian contact forces and torques for robotic manipulators and its application to force control. IEEE Transactions on Automation Science and Engineering, 15(2), (2018), 879-886. DOI: 10.1109/TASE.2017.2691136
- [31] X. Liu, G. Zuo, J. Zhang and J. Wang: Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensation. International Journal of Advanced Robotic Systems, 16(4), (2019), 1729881419856132. DOI: 10.1177/1729881419856132
- [32] S.-H. Yen, P.-C. Tang, Y.-C. Lin and C.-Y. Lin: Development of a virtual force sensor for a low-cost collaborative robot and applications to safety control. Sensors, 19(11), (2019). DOI: 10.3390/s19112603
- [33] M.A. Arteaga and A. Gutierrez-Giles: On the robustness of force estimation methods for robot manipulators: An experimental study. Journal of the Franklin Institute, 360(16), (2023), 11705-11735. DOI: 10.1016/j.jfranklin.2023.09.015
- [34] L. Ding, H. Xing, H. Gao, A. Torabi, W. Li and M. Tavakoli: VDC-based admittance control of multi-DOF manipulators considering joint flexibility via hierarchical control framework. Control Engineering Practice, 124 (2022), 105186. DOI: 10.1016/j.conengprac.2022.105186
- [35] L. Roveda and D. Piga: Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks. Autonomous Robots, 45 (2021), 371-388. DOI: 10.1007/s10514-021-09970-z
- [36] S.B. Liu, A. Giusti and M. Althoff: Velocity estimation of robot manipulators: An experimental comparison. IEEE Open Journal of Control Systems, 2 (2022) 1-11. DOI: 10.1109/OJCSYS.2022.3222753
- [37] A. Gutiérrez-Giles and M. Arteaga-Pérez: Output feedback hybrid force/motion control for robotic manipulators interacting with unknown rigid surfaces. Robotica, 38(1), (2020), 136-158. DOI: 10.1017/S0263574719000523
- [38] Z. Zhang, M. Leibold and D. Wollherr: Integral sliding-mode observer-based disturbance estimation for Euler-Lagrangian systems. IEEE Transactions on Control Systems Technology, 28(6), (2020), 2377-2389. DOI: 10.1109/TCST.2019.2945904
- [39] H.H. Kim, M.C. Lee, J.H. Kyung and H.M. Do: Evaluation of force estimation method based on sliding perturbation observer for dual-arm robot system. International Journal of Control, Automation and Systems, 19(1), (2021), 1-10. DOI: 10.1007/s12555-019-0324-x
- [40] H. Khalil: Nonlinear Systems. Pearson, India, 2014, 3rd Ed.
- [41] R. Kelly, V. Santibáñez and A. Loría: Control of Robot Manipulators in Joint Space. London, Springer, 2006.
- [42] M. Rodríguez-Liñán, M. Mendoza, I. Bonilla and C. Chávez-Olivares: Saturating stiffness control of robot manipulators with bounded inputs. International Journal of Applied Mathematics and Computer Science, 27(1), (2017), 79-90. DOI: 10.1515/amcs-2017-0006
- [43] C. Canudas, B. Siciliano and G. Bastin: Theory of Robot Control. London, Springer, 2012.
- [44] C. Chávez-Olivares, F. Reyes-Cortés, E. González-Galván, M. Mendoza-Gutiérrez and I. Bonilla-Gutiérrez: Experimental evaluation of parameter identification schemes on an anthropomorphic direct drive robot. International Journal of Advanced Robotic Systems, 9(5), (2012). DOI: 10.5772/52190
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-06a21743-dd69-448b-9fa5-fbbe9dd669dc