Contact force estimation for serial manipulator based on weighted moving average with variable span and standard Kalman filter with automatic tuning

Feng Cao1, Paul D. Docherty1,2, XiaoQi Chen3
1Mechanical Engineering Department, University of Canterbury, Christchurch, New Zealand
2Institute for Technical Medicine (ITeM), Furtwangen University, Villingen-Schwenningen, Germany
3Faculty of Science, Engineering and Technology, Swinburne University of Technology, Victoria, Australia

Tóm tắt

Sensorless contact force estimation methods facilitate the application of the serial manipulators to manufacturing as they enable robots to interact with unexpected collisions at low cost. In this paper, an external force estimation approach with no embedded sensors is proposed. The approach combines a Weighted Moving Average (WMA) with variable span, the standard Kalman filter (SKF), and its tuning routines. Improved confidence in the motor output torque is achieved due to the reduction of the measurement noise in the motor current by the WMA. The span of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real time. With the comprehensive information of uncertainty in motor current noise and measurement errors of individual joints speed, an automatic tuning algorithm of the SKF is presented. Validation of the presented estimation approach in terms of estimation accuracy and response time was conducted on the Universal Robot 5 manipulator with differing end effector loads. It was found that the combined force estimation method leads to a reduction of the root-mean-square error and response time by 55.2% and 20.8% in comparison with the established method. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) is available. Consequently, the cost of collision recognition could be reduced dramatically.

Tài liệu tham khảo

Ren Y, Chen Z, Liu Y, Gu Y, Jin M, Liu H (2017) Adaptive hybrid position/force control of dual-arm cooperative manipulators with uncertain dynamics and closed-chain kinematics. J Franklin Inst 354(17):7767–7793 Jafari A, Ryu JH (2016) Independent force and position control for cooperating manipulators handling an unknown object and interacting with an unknown environment. J Franklin Inst 353:857–875 Lo H, Xie S (2012) Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Med Eng Phys 34(3):261–268 Meng W, Liu Q, Zhou Z, Ai Q, Sheng B, Xie S (2015) Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 31:132–145 Gaz C, Magrini E, De Luca A (2018) A model-based residual approach for human-robot collaboration during manual polishing operations. Mechatronics 55:234–247 Yao B, Zhou Z, Wang L, Xu W, Liu Q, Liu A (2018) Sensorless and adaptive admittance control of industrial robot in physical human-robot interaction. Robot Comput-Integr Manuf 51:158–168 Zhang X, Chen H, Xu J, Song X, Wang J, Chen X (2018) A novel sound-based belt condition monitoring method for robotic grinding using optimally pruned extreme learning machine. J Mater Process Technol 260:9–19 Xu X, Chen W, Zhu D, Yan DS, Ding H (2021) Hybrid active/passive force control strategy for grinding marks suppression and profile accuracy enhancement in robotic belt grinding of turbine blade. Robot Comput-Integr Manuf 67:102407 Zhu D, Feng X, Xu X, Yang Z, Li W, Yan S, Ding H (2020) Robotic grinding of complex components: a step towards efficient and intelligent machining–challenges, solutions, and applications. Robot Comput-Integr Manuf 65 Stolt A, Linderoth M, Robertsson A, Johansson R (2012) Force controlled robotic assembly without a force sensor. Proc IEEE Int Conf Robot Autom 1538–1543 Lu S, Chung JH, Velinsky SA (2005) Human-robot collision detection and identification based on wrist and base force/torque sensors. In: Proc IEEE Int Conf Robot Autom 3796–3801 Gregory A, Lin M, Gottschalk S, Taylor R (2000) Fast and accurate collision detection for haptic interaction using a three degree-of-freedom force-feedback device. Comput Geom Theory Appl 15(1–3):69–89 Raibert MH, Craig JJ (1981) Hybrid position/force control of manipulators. J Dyn Syst Meas Control 103(2):126–133 Hogan N (1985) Impedance control: an approach to manipulation-part I: theory; part II: implementation; part III: applications. Trans ASME J Dyn Syst Meas Control 107(1):1–24 . Ohishi K, Miyazaki M, Fujita M (1992) Hybrid control of force and position without force sensor. In: Proc Int Conf IEEE Ind Electron Soc 670–675 Eom KS, Suh IH, Chung WK, Oh SR (1998) Disturbance observer based force control of robot manipulator without force sensor. In: Proc IEEE Int Conf Robot Autom 3012–3017 Katsura S, Matsumoto Y, Ohnishi K (2007) Modeling of force sensing and validation of disturbance observer for force control. IEEE Trans Ind Electron 54(1):530–538 Chan L, Naghdy F, Stirling D (2013) Extended active observer for force estimation and disturbance rejection of robotic manipulators. Robot Auton Syst 61(12):1277–1287 Shimada N, Yoshioka T, Ohishi K, Miyazaki T, Yokokura Y (2015) Variable dynamic threshold of jerk signal for contact detection in industrial robots without force sensor. Electr Eng Jpn 193(1):43–54 Chen W-H, Ballance DJ, Gawthrop PJ, O’Reilly J (2000) A nonlinear disturbance observer for robotic manipulators. IEEE Trans Ind Electron 47(4):932–938 Chen W-H (2004) Disturbance observer based control for nonlinear systems. IEEE/ASME Trans Mechatron 9(4):706–710 Mohammadi A, Tavakoli M, Marquez H, Hashemzadeh F (2013) Nonlinear disturbance observer design for robotic manipulators. Control Eng Pract 21(3):253–267 Nikoobin A, Haghighi R (2009) Lyapunov-based nonlinear disturbance observer for serial n-link robot manipulators. J Intell Robot Syst 55(2/3):135–153 Zhang H, Ahmad S, Liu G (2015) Torque estimation for robotic joint with harmonic drive transmission based on position measurements. IEEE Trans Robot 31(2):322–330 Zhou F, Li Y, Liu G (2017) Robust decentralized force/position fault-tolerant control for constrained reconfigurable manipulators without torque sensing. Nonlinear Dyn 89(3) Dong B, Zhou F, Liu K, Li Y (2018) Torque sensorless decentralized neuro-optimal control for modular and reconfigurable robots with uncertain environments. Neurocomputing 282:60–73 Jeong JW, Chang PH, Park KB (2011) Sensorless and modeless estimation of external force using time delay estimation: application to impedance control. J Mech Sci Technol 25(8):2051–2059 Luca AD, Mattone R (2005) Sensorless robot collision detection and hybrid force/motion control. In: Proc IEEE Int Conf Robot Autom 999–1004 Luca AD, Albu-Schäffer A, Haddadin S, Hirzinger G (2006) Collision detection and safe reaction with the DLR-III lightweight manipulator arm. In: Proc IEEE/RSJ Int Conf Intell Robots Syst 1623–1630 Luca AD, Mattone R (2003) Actuator failure detection and isolation using generalized momenta. Proc IEEE Int Conf Robot Autom 1:634–639 Ragaglia M, Zanchettin AM, Bascetta L, Rocco P (2016) Accurate sensorless lead-through programming for lightweight robots in structured environments. Robot Cim-Int Manuf 39:9–21 Yuan J, Qian Y, Yuan Z, Gao L, Wan W (2019) Position based impedance force controller with sensorless force estimation. Assembly Autom 39(3):489–496 Yen SH, Tang PC, Lin YC, Lin CY (2019) Development of a virtual force sensor for a low-cost collaborative robot and applications to safety control. Sensors 19(11):2603 Qiu Z, Ozawa R, Ma S (2019) A force-sensorless approach to collision detection based on virtual powers. Adv Robot 33(23):1209–1224 Tian Y, Chen Z, Jia T, WangA, Li L (2016) Sensorless collision detection and contact force estimation for collaborative robots based on torque observer. Proc IEEE Int Conf Robot Biom 946–951 Liu Z, Yu F, Zhang L, Li T (2017) Real-time estimation of sensorless planar robot contact information. J Robot Mechatron 29(3):557–565 Cao F, Docherty PD, Ni S et al (2021) Contact force and torque sensing for serial manipulator based on an adaptive Kalman filter with variable time period. Robot Comput-Integr Manuf 72:102210 Jung J, Lee J, Huh K (2006) Robust contact force estimation for robot manipulators in three-dimensional space. Proc Inst Mech Eng C J Mech Eng Sci 220(9):1317–1327 Pehlivan AU, Losey DP, O’Malley MK (2016) Minimal assist-as-needed controller for upper limb robotic rehabilitation. IEEE Trans Robot 32(1):113–124 Jin H, Rong X (2018) Contact force estimation for robot manipulator using semi-parametric model and disturbance kalman filter. IEEE Trans Ind Electron 65(4):3365–3375 Wahrburg A, Bös J, Listmann KD, Dai F, Matthias B, Ding H (2017) Motor-current-based estimation of cartesian contact forces and torques for robotic manipulators and its application to force control. IEEE Trans Autom Sci Eng 1–8 Spong MW, Hutchinson S, Vidyasagar M (2006) Robot modeling and control. Wiley, New York, NY, USA Axelsson P, Gustafsson F (2015) Discrete-time solutions to the continuous-time differential Lyapunov equation with applications to Kalman filtering. IEEE Trans Autom Control 60(3):632–643 Van Loan C (1978) Computing integrals involving the matrix exponential. IEEE Trans Autom Control 23(3):395–404 Olsson H, Åström K, Canudas de Wit C, Gäfvert M, Lischinsky P (1998) Friction models and friction compensation. Eur J Control 4(3):176–195