Robot Collision Detection Based on External Torque Observer

doi:10.12141/j.issn.1000-565X.230086

Abstract

Abstract:

Collision detection technology can reduce the probability of equipment damage and personal injury and plays an important role in modern human-robot collaborative production. To realize the collision detection without external torque sensor, it is necessary to accurately estimate the external torque of industrial robots. However, the accuracy of external torque estimation can be affected by parameters identification error of dynamic model and measurement error of motor current. To solve these problems, this paper designed a disturbance Kalman filter external torque observer based on the disturbance principle. The observer takes the equivalent external torque of external collision as the disturbance term, defines the joint disturbance model, and introduces the generalized momentum of the robot to construct the state-space equation. Considering the parameters identification error of the dynamic model and the measurement error of the motor current, it carried out an iterative estimation based on Kalman filter algorithm to obtain the optimal external torque. In order to improve the sensitivity of collision detection, a time-varying symmetric threshold function which varies with joint velocity was proposed for collision detection. The proposed method can adjust the threshold according to the change of joint velocity to adapt to the observed values of external torques at different working speeds. Experimental results show that compared with the generalized momentum observer, the accuracy of external torque estimation of the proposed observer is improved by 52.03%. In order to verify the effectiveness of the proposed method, this paper used a 6-DOF series joint industrial robot to conduct collision detection experiments. The experimental results show that compared with the static threshold, the time-varying threshold method reduces the detection delay by 58.06%, which can improve the sensitivity of collision detection and is more conducive to the safe operation and collision protection of industrial robots.

Key words: industrial robot, collision detection, external torque observer, time-varying threshold

CLC Number:

TP242.2

ZHANG Tie, CHEN Yijie, ZOU Yanbiao. Robot Collision Detection Based on External Torque Observer[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(3): 84-92.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Table 1

Parameters of covariance matrix in disturbance Kalman filter observer"

$d i a g (a i, i)$	$H$	$S$	$Σ d$	$Σ p$	$Σ o$
$a i, i$	1	0	0.5	0.1	0.075

Table 1

Fig.4

Table 2

Fig.5

Fig.6

Fig.7

Fig.8

References 23

1	FLACCO F， KROEGER T， DE LUCA A，et al ．A depth space approach for evaluating distance to objects with application to human-robot collision avoidance［J］．Journal of Intelligent & Robotic Systems，2015，80（1）：7-22.
2	LI W， HAN Y， WU J H，et al ．Collision detection of robots based on a force/torque sensor at the bedplate［J］．IEEE-ASME Transactions on Mechatronics，2020，25（5）：2565-2573.
3	PARK K M， KIM J， PARK J，et al ．Learning-based real-time detection of robot collisions without joint torque sensors［J］．IEEE Robotics and Automation Letters，2021，6（1）：103-110.
4	PARK K M， PARK Y， YOON S，et al ．Collision detection for robot manipulators using unsupervised anomaly detection algorithms［J］．IEEE-ASME Transactions on Mechatronics，2022，27（5）：2841-2851.
5	TAKAURA S， MURAKAMI T， OHNISHI K ．An approach to collision detection and recovery motion in industrial robot［C］∥Proceeding of the 15th Annual Conference of IEEE Industrial Electronics Society．Philadelphia：IEEE，1989：421-426.
6	HADDADIN S， DE LUCA A， ALBU-SCHAFFER A ．Robot collisions：a survey on detection，isolation，and identification［J］．IEEE Transactions on Robotics，2017，33（6）：1292-1312.
7	ZHANG S L， WANG S， JING F S，et al ．A sensorless hand guiding scheme based on model identification and control for industrial robot［J］．IEEE Transactions on Industrial Informatics，2019，15（9）：5204-5213.
8	DE LUCA A， MATTONE R ．Actuator failure detection and isolation using generalized momenta［C］∥Proceeding of the IEEE International Conference on Robotics and Automation．New York：IEEE，2003：634-639.
9	CHEN S X， LUO M Z， HE F ．A universal algorithm for sensorless collision detection of robot actuator faults［J］．Advances in Mechanical Engineering，2018，10（1）：1-10.
10	LEE S D， SONG J B ．Sensorless collision detection based on friction model for a robot manipulator［J］．International Journal of Precision Engineering and Manufacturing，2016，17（1）：11-17.
11	HAN L Y， MAO J L， CAO P F，et al ．Toward sensorless interaction force estimation for industrial robots using high-order finite-time observers［J］．IEEE Transactions on Industrial Electronics，2022，69（7）：7275-7284.
12	DIMEAS F， AVENDANO-VALENCIA L D，ASPRAGA- THOS N ．Human-robot collision detection and identification based on fuzzy and time series modelling［J］．Robotica，2015，33（9）：1886-1898.
13	XU T， FAN J Z， FANG Q Q ．Robot dynamic calibration on current level：modeling，identification and applications［J］．Nonlinear Dynamics，2022，109（4）：2595-2613.
14	REN T Y， DONG Y F， WU D ．Collision detection and identification for robot manipulators based on extended state observer［J］．Control Engineering Practice，2018，79（1）：144-153.
15	LI Y， LI Y H， ZHU M C ．A nonlinear momentum observer for sensorless robot collision detection under model uncertainties［J］．Mechatronics，2021，78（1）：1-16.
16	WAGNER M， LIU S B， GIUSTI A，et al ．Interval-arithmetic-based trajectory scaling and collision detection for robots with uncertain dynamics［C］∥Proceeding of the IEEE International Conference on Robotic Computing．New York：IEEE，2018：41-48.
17	张铁，梁骁翃，覃彬彬，等．基于牛顿欧拉法的SCARA机器人动力学参数辨识［J］．华南理工大学学报（自然科学报），2017，45（10）：129-136，143.
	ZHANG Tie， LIANG Xiao-hong， QIN Bin-bin，et al ．Dynamic parameter identification of SCARA robot based on newton-euler［J］．Journal of South China University of Technology （Natural Science Edition），2017，45（10）：129-136，143.
18	CHEN W H， YANG J， GUO L，et al ．Disturbance-observer-based control and related methods-an overview［J］．IEEE Transactions on Industrial Electronics，2016，63（2）：1083-1095.
19	MISANTISUK C， OHISHI K， URUSHIHARA S ．Kalman filter-based disturbance observer and its applications to sensorless force control［J］．Advanced Robotics，2011，25（3/4）：335-353.
20	HU J， XIONG R ．Contact force estimation for robot manipulator using semiparametric model and disturbance kalman filter［J］．IEEE Transactions on Industrial Electronics，2018，65（4）：3365-3375.
21	GAUTIER M， KHALIL W ．Direct calculation of minimum set of inertial parameters of serial robots［J］．IEEE Transactions on Robotics and Automation，1990，6（3）：368-373.
22	SWEVERS J， GANSEMAN C， TUKEL D B ．Optimal robot excitation and identification［J］．IEEE Transactions on Robotics and Automation，1997，13（5）：730-740.
23	韩勇．协作机器人模型辨识方法与人机交互控制技术研究［D］．上海：上海交通大学，2020.

关节	均方根误差值/（N·m）
关节	扰动卡尔曼滤波观测器	广义动量观测器
1	1.292 5	3.753 1
2	2.031 2	12.033 5
3	1.683 8	9.058 0
4	0.749 8	1.503 7
5	0.561 0	1.330 7
6	0.314 4	0.233 8

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