Human-robot collision detection under modeling uncertainty using ...

Journal of Mechanical Science and Technology 28 (11) (2014) 4389~4395 www.springerlink.com/content/1738-494x

DOI 10.1007/s12206-014-1006-5

Human-robot collision detection under modeling uncertainty using frequency boundary of manipulator dynamics† Byung-jin Jung, Ja Choon Koo, Hyouk Ryeol Choi and Hyungpil Moon* School of Mechanical Engineering, Sungkyunkwan University, Suwon, 440-748, Korea (Manuscript Received January 3, 2014; Revised July 13, 2014; Accepted August 20, 2014) ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Abstract This paper presents the development and experimental evaluation of a collision detection method for robotic manipulators sharing a workspace with humans. Fast and robust collision detection is important for guaranteeing safety and preventing false alarms. The main cause of a false alarm is modeling error. We use the characteristic of the maximum frequency boundary of the manipulator's dynamic model. The tendency of the frequency boundary’s location in the frequency domain is applied to the collision detection algorithm using a band pass filter (band designed disturbance observer, BdDOB) with changing frequency windows. Thanks to the band pass filter, which considers the frequency boundary of the dynamic model, our collision detection algorithm can extract the collision caused by the disturbance from the mixed estimation signal. As a result, the collision was successfully detected under the usage conditions of faulty sensors and uncertain model data. The experimental result of a collision between a 7-DOF serial manipulator and a human body is reported. Keywords: Collision detection; Human-robot physical interaction; Manipulator safety; Disturbance observer ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

1. Introduction The areas in which robots can be utilized are everincreasing to include public services, personal health care and cooperative production. These applications all require complementary roles for humans and robots [1-3]. Understandably, the most critical issue in these new workspaces, where humans and robots physically interact, is safety. The safety of a robotic system is directly related to the performance of its reaction to unexpected contact with a human body. This type of unexpected collision has an important characteristic; the duration of the interaction is very short. More specifically, the contact force of a human-robot collision reaches its maximum value in several milliseconds [4]. As the reaction time of average human is around 100 ms, we cannot rely on human reaction only to ensure safety in the event of a collision [5]. The occurrence of a collision should be recognized by the robot and a safe reaction should be carried out. Several methods exist for detecting the occurrence of a collision or for reducing the possibility of collision. For example, popular methods use exteroceptive sensors like vision sensors [6, 7]. Another type of collision detection uses safety mechanisms that are designed to cut the actuator torque if it exceeds a pre*

Corresponding author. Tel.: +82 31 299 4842, Fax.: +82 31 290 7507 E-mail address: [email protected] † This paper was presented at the ISR-2013, KINTEX, Seoul, Korea, October 24-26, 2013. Recommended by Guest Editor Byung Kyu Kim © KSME & Springer 2014

set threshold [8, 9]. These methods, however, are costly and the performance is limited due to the additional hardware and sensing inaccuracy. Other proprioceptive methods using joint torque sensor data for feedback control have been suggested to avoid such problems [10-15]. These methods are called active collision detection methods. A representative method among active detection methods is the Disturbance Observer (DOB) originally proposed by Ohnishi [10]. This method can be applied to each joint independently and performs pseudo-differentiation of the velocity signal using a low pass filter for estimating the inertial torque of each joint [10, 11]. The residual observer (ROB), proposed by Haddadin and Luca in Ref. [12], is another active collision detection method. This method clears away the requirement of acceleration data by using generalized momentum. Moreover, ROB compensates for coupling torque and gravity in the detection signal using a dynamic model of the manipulator. Also, ROB can be applied as the state space model form of a multi-axis manipulator [12-15]. However, the performance of these model-based collision detection methods is negatively affected by inaccuracies in the model and in the sensor data. Further, uncertainties about objects at the end effector inevitably cause estimation error. Estimation error increases the possibility of false alarms which decreases the effectiveness of the collision detection process and, therefore, the safety and productivity of the robot. An intuitive solution to prevent such false alarms is to elon-

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gate the collision detection threshold range. This allows more room for the effects of modeling error on the signal before a collision is recognized. However, a large threshold reduces the sensitivity of the collision detection when an external disturbance torque is applied. In this paper, the tendency of the maximum frequency boundary of a manipulator's dynamic model is analyzed and used in the design of a band pass filter for minimizing the effects of model and sensor data uncertainties. Implementing the collision detection algorithm containing this band pass filter for experimentation on a real manipulator obtains both fast performance and robust collision distinction. Following, in Sec. 2, the principles of active collision detection techniques and their characteristics are introduced. Sec. 3 contains the analytic process of the frequency boundary of a manipulator's dynamic model to determine an optimized filter cutoff frequency. Sec. 4 illustrates the simulation-based and experimental evaluation of the algorithm, and Sec. 5 presents the conclusion of this study.

The variable q represents the joint configuration of robot and Ci ( q, w ) , Gi ( q ) represent coupling torque and gravity. 2.2 Residual observer (ROB) The residual observer (ROB) is introduced in Refs. [11-13]. This is defined in state-space using generalized momentum ( p ) and the skew-symmetric characteristic of the robot dynamic model for use in a multi-DOF application. Moreover, ROB contains model-based compensation of coupling torque and gravity as illustrated in Eq. (5).

(

tn

)

r = K R p - ò (t + C T ( q, w ) - G ( q ) + r ) dt . 0

(5)

The estimated residual and real disturbance of the i-th joint has the same relationship with DOB as in Eq. (6). ri =

KR ti . s + KR

(6)

2. Principles of disturbance estimation 2.1 Disturbance observer (DOB) The disturbance observer (DOB) is a disturbance estimation method that compares the actuator input torque and the inertial torque calculated using dynamic model and joint velocity data [10, 11]. This method uses pseudo-differentiation, as in Eqs. (1) and (2), to estimate joint accelerations of the causal system. ui + d i = M iw& i = M i dî =

dwi dt

KD ( ui + K D M iwi ) - K D M iwi s + KD

(1) (2)

{ i < n | n = degree of freedom }

where ui , wi , di , M i are the actuator input torque, joint velocity, disturbance and the inertial term of the i-th joint, respectively. This process generates a low-pass filter element, which is called the 'Q-filter'. The constant K D is the gain on the Qfilter of the disturbance observer. This element determines the relationship between the real and the estimated disturbance as in Eq. (3) in the Laplace domain. dî =

KD di . s + KD

(3)

The DOB can estimate the disturbance torque with a compensating inertial torque. However, this method is defined for a single joint only and does not consider gravity or the coupling torque between joints. Practically, the estimated disturbance torque on i-th joint of the robot is affected by coupling torque and gravity as in Eq. (4). dî =

KD ( di - Ci ( q,w ) - Gi ( q ) ) . s + KD

(4)

The K R , which is the gain of the residual observer, is from the diagonal gain matrix for a multi-axis application. When the model of the manipulator is accurate, ROB can estimate the disturbance caused by a collision successfully. However, when the model has uncertainty, the modeling error affects the estimation signal as in Eq. (7). This effect increases the possibility of false alarms, or a false detection of a collision that did not occur. Therefore, an analysis of the effect of this modeling error and a collision detection algorithm which considers the characteristics of this modeling error is required to solve the problem of collision detection under uncertainty. The t err is the estimation error torque caused by model uncertainty. ri =

KR (t i + t err ) . s + KR

(7)

3. Frequency boundary of a manipulator dynamic model The effects of modeling error can be expressed as in Eq. (8). The terms with a capital delta are the dynamic terms caused by inaccurate modeling data (errors in link mass, inertia, center of mass, link length, etc.). This equation is similar in structure to the serial manipulator's dynamic equation, Eq. (9). t err = DMq&& + DC ( q, q& ) + DG ( q )

(8)

t jnt = Mq&& + C ( q, q& ) + G ( q ) .

(9)

Each element of the M and C matrices for the robot dynamics are composed of a constant and exponentiated trigonometric terms. The general structure of these terms can be expressed as Eqs. (10) and (11).

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B. Jung et al. / Journal of Mechanical Science and Technology 28 (11) (2014) 4389~4395 n

k

M i q&& = å ao Õ sin o =1

boj

( q ) cos ( q )q&& coj

j

j

(10)

j

n

o =1

j =1

å d Õ sin ( q&

e

f oj

( q ) q& j

hoj j

.

(11)

The subscript ‘i’ refers to the number of the row of the matrices M and C. The constant coefficients ( ao , d o ) are determined from dynamic modeling data such as inertia, mass, link length, center of mass location, twist of link, etc. The trigonometric terms are determined from the configuration of manipulator ( q j ) and the exponents ( boj , coj , eoj , f oj , hoj ) depend on degree of freedom ( n ). The subscript ‘o’ is the index of terms which are separated by the summation for each of the elements contained in the inertia or corioli-centrifugal matrix. The ‘j’ is the number of the robot joint. The variables ‘k’ and ‘l’ denote the numbers of the terms which are separated by summation for each of the elements contained in the inertia or corioli-centrifugal matrix. Eqs. (10) and (11) represent the M and C terms of the manipulator dynamics and are the superposition of individual signals that each contain their own amplitude and period. The amplitude (constant) components of these signals ( ao , d o ) are related to the modeling parameters, and the periodic (trigonometric) components are related to the robot configuration and the degree of freedom. In other words, the frequency characteristic of the dynamic terms is not determined by the modeling parameters but by the robot's trajectory and by the degree of freedom. This means that the frequency characteristic of the Eq. (8), which has the same structure as the robot dynamics in Eq. (9), is determined by the robot’s trajectory and the degree of freedom only. When the system has a small control time step and the robot follows a smooth trajectory without large changes of acceleration, the joint angle of the robot can be expressed as Eq. (12). qx +1 = q& x dt + qx .

(12)

The expressions qx and q& x are the position and velocity at the x-th control time step, and qx +1 is the position at the next time step. In this notation, x represents the number of the time step, and dt is the control period. The acceleration and velocity under this assumption can be considered to be constant during a single time step. From Eqs. (10)-(12), the trigonometric terms, which are contained in Mi and Ci, can be expressed in terms of joint velocity and control time step as in Eqs. (13) and (14). The variables annotated with the subscript x are representative of that value at the x-th control time step. The variables aox and d ox are constant terms which include the accelerations and velocities of x-th time step as constants. M ix q&& = n

k

ox

o =1

j =1

boj

eoj

ox

Ci ( q, q& ) = å d o Õ sin oj ( q j ) cos

å a Õ sin ( q&

n

l

j =1

l

Cix ( qx , q& x ) =

dt + q jx -1 ) cos oj ( q& jx -1dt + q jx -1 ) c

jx -1

(13)

o =1

dt + q jx -1 ) cos

jx -1

f oj

( q&

dt + q jx -1 ) .

(14)

jx -1

j =1

The trigonometric functions’ periodic components of these expressions can be simplified to a summation of configuration parameters as in Eqs. (15) and (16). M ix q&& = æ n ö sin ç å ( boj + coj )( q& jx -1dt + q jx -1 ) ÷ o =1 è j =1 ø & Cix ( qx , qx ) = k

åu

ox

l

åv

ox

o =1

(

)

æ n ö sin ç å ( eoj + f oj )( q& jx -1dt + q jx -1 ) ÷ . è j =1 ø

(

)

(15)

(16)

The terms uox , vox are the modified constant coefficients containing the constants which were generated by trigonometric summation. With this simplification, it is seen that the frequency of each component can be represented as a 1st order equation composed of the velocity of the robot joints and the exponents of the trigonometric terms. As the terms of the robot dynamic model are a superposition of individual signals, the maximum frequency boundary can be derived from the characteristics of a superposition that conserves the frequency of the superimposed signals. In other words, the signal with the fastest frequency among the signal elements represents the maximum frequency boundary of the dynamic model. However, in manipulators with multiple degrees of freedom and configurations, the velocity of each robot joint is changed during each control time. Therefore, the signal which has the maximum frequency may also change. Naturally, searching for and comparing every signal element requires heavy computational load for a manipulator with a high number of degrees of freedom. This problem can be solved by determining a pseudo-optimized maximum frequency boundary using wmax , which is defined in Eq. (17).

wmax = max ( wi

)

(17)

(wi = velocity of i-th joint ) . When all joint velocity variables are replaced with wmax , the multiplier of velocity variables of each combined trigonometric terms can be summed up in a single number. In the result, Eqs. (15) and (16) can be changed to Eqs. (18) and (19). This change is performed for every degree of freedom of the manipulator model. The summed multiplier, A, D have a direct relationship with the frequency of these terms. More specifically, these terms represent the sum of the exponents of each trigonometric term. These sums have the same value as the numbers of the trigonometric functions used to express each periodic component. The variables B, E , however, do not affect the frequency.

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Table 1. Maximum frequency of M and C terms for manipulator.

k

M i = å ao sin ( ( Awmax ) dt + B ) o =1 n

(18)

n

Degree of freedom

Max. freq. bound. of M

Max. freq. bound. of C

2

2 wmax

2 wmax

3

4 wmax

4 wmax

4

6 wmax

6 wmax

( A = å ( boj + coj ), B = å ( boj + coj ) q jx -1 ) j =1

j =1

l

Ci ( q, q& ) = å d o sin ( ( Dwmax ) dt + E ) o =1

n

(19)

n

( D = å ( eoj + f oj ), E = å ( eoj + f oj ) q jx -1 ) . j =1

The maximum frequency of each component is determined by the maximum value of A and D . The overall maximum value of constants A, D for the manipulator is determined by the manipulator structure. Generally, the maximum number of trigonometric functions in a single dynamic term is 2 ( n - 1) components for an n-dof manipulator with the structure of serial roll-pitch-roll chain. In this case, the maximum frequency boundary of a serial manipulator with several degrees of freedom can be displayed as is in Table 1. When a manipulator follows a smooth trajectory, the disturbance caused by a collision has a higher frequency than that of the modeling error in the estimation signal. Because of this, the collision can be extracted from the mixed estimation signal using a filter which has a low pass cutoff frequency that references the maximum frequency boundary of the manipulator model.

8 wmax

8 wmax

6

10 wmax

10 wmax

n

2(n-1) wmax

2(n-1) wmax

Fig. 1. Simulation environment.

Joint1 0

-5 ROB signal BdDOB signal -10

0

0.5

1

1.5

2

Collision Peak

The collision detection performance of the band designed disturbance observer (BdDOB) is evaluated in simulation and experiment. BdDOB is a collision detection algorithm that contains a band pass filter in the estimation process [15]. The maximum frequency of the manipulator dynamic model is calculated based on the manipulator degree of freedom and the maximum velocity in each control time step during the experiment. Moreover, the pass band of the BdDOB is determined using the calculated maximum frequency. Specifically, the band pass filter used in simulation and in the experiment is established using a combination of a 1st order high pass filter and a low pass Butterworth filter. The low pass filter has a fixed cutoff frequency, K noise , to filter out the sensor noise. Meanwhile, the high pass filter has a cutoff frequency which depends on wmax . Additionally, the cutoff frequency of the high pass filter has an initial offset for the zero-velocity condition when the robot is not moving. The structure of the applied filter can be represented as Eq. (20).

Qband =

sK noise

( s + K noise )( s + V wmax + U )

.

(20)

The coefficient V depends on the DOF of manipulator as shown in Table 1. The constant U reflects the initial offset,

2.5 Time(sec)

3

3.5

4

4.5

5

Modeling Error Fluctuation

Joint2 2 Torque(Nm)

4. Application of maximum frequency boundary analysis

Modeling Error Fluctuation

Collision Peak

Torque(Nm)

j =1

5

1 0 -1 ROB signal BdDOB signal

-2 0

0.5

1

1.5

2

2.5 Time(sec)

3

3.5

4

4.5

5

Fig. 2. Collision detection data from simulation.

which is set for the stable output when manipulator does not actuated. 4.1 Simulation Simulation is performed for a 2-DOF manipulator as illustrated in Fig. 1. The end effector is equipped with an additional 5 kg element, which is not included in the dynamic model, to evaluate the filtering performance under dynamic uncertainty. Since a perfect model of any tool is difficult to derive in a practical situation, this condition is reasonable. The collision occurs at a random location on link 1 at 1.005 sec while the manipulator remains at its home position. The ROB data is estimated along with the BdDOB data. The collision estimation signals are shown in Fig. 2. Additionally, the collision detection threshold is set to 0.02 Nm at joint 1 and 0.01 Nm at joint 2, which sets the maximum allowed value of the BdDOB estimation signal while moving without collision.


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Table 2. Manipulator trajectory used for experiment. Joint

Start position (Deg)

End position (Deg)

Travel time (ms)

1

0

0

2000

2

0

-30

2000

3

0

-30

2000

4

0

30

2000

5

0

-30

2000

6

0

-30

2000

7

0

30

2000

Joint 7 (Roll)

1

6

2

7

3

8

4

9

5

10

50.0 mm 136.0 mm

Joint 6 (Pitch) 108.1 mm Joint 5 (Roll) 161.9 mm Joint 4 (Pitch) 114.4 mm Joint 3 (Roll) 165.6 mm Joint 2 (Pitch) 140.0 mm Joint 1 (Roll)

78.0 mm 36.0 mm

Fig. 3. 7-DOF manipulator used in the experiment.

The simulation is performed using RecurDyn and MATLAB Simulink with a simulator time step of 100us and a 1ms control time step. For the simulation results, ROB data is illustrated using a dashed line and BdDOB data is illustrated using solid line. The ROB data shows fluctuations of around 0.4 Nm at joint 1 and 0.2 Nm at joint 2 caused by the model error after the collision's peak. For BdDOB, however, the fluctuation signal is filtered out and just the clear collision signal remains. 4.2 Experiment The collision detection experiment is performed using a 7degree of freedom manipulator that was developed at the Korea Electronics Technology Institute (Fig. 3). The manipulator is equipped with an encoder and a joint torque sensor at each joint. The overall weight of manipulator is 8.482 kg and maximum length is 0.876 m. To reduce collision detection delay caused by communication speed, the manipulator is consisted with joint drive module and host controller which use EtherCAT for communication protocol and real time module which is built with TWINCAT 3.0. The

Fig. 4. Human-Robot collision detection experiment.

communication and control time step is set to 1 ms. The joint torque sensor signal and modeling parameters are used under uncertainty to evaluate the robustness of the BdDOB collision detection algorithm. Specifically, each joint torque sensor has a random offset value as an initial condition. Moreover, the modeling parameters have errors of around 30% for the mass and inertia of each link. The manipulator is commanded to repeatedly follow the trajectory between the positions displayed in Table 2 as shown in numbered pictures 1-5 in Fig. 4. The occurrence of collision is illustrated in numbered pictures 6-10 in Fig. 4. The manipulator is set to stop immediately when a collision is recognized. The threshold is set to 1 Nm which is determined using the estimation signal of the robot following the trajectory with no collision. The estimated collision detection signal for this experiment is shown in Fig. 5. The data shown is gathered from joint 1, which shows the effect of modeling error most significantly. The torque sensor registers the effect of the collision at 1.567

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(MOTIE) and by the Korea Evaluation Institute of Industrial Technology (KEIT) under program number “10040210”. Special thanks to B. S. Kim, T. K. Kim and D. Y. Kim at Korea Electronics Technology Institute (KETI) for sharing their experimental setup.

25

Joint Torque ROB Band designed DOB

5

20

Torque(Nm)

4

Torque(Nm)

15

10

Band designed DOB Collision sensing Detection Signal

3 2 1 0 -1

1.54

1.56

5

1.58 Time(sec)

1.6

1.62

References

0

-5

-10

0

0.5

1

1.5

2

2.5

3

3.5

4

Time(sec)

Fig. 5. Collision detection experiment data.

sec and the estimation data (BdDOB data) exceeds the threshold at 1.572 sec. The estimation signal at the moment of the collision is magnified and shown in the small graph in Fig. 5. Among the three plots, the joint torque sensor signal has an initial offset of about 2 Nm when stopped after collision. This offset is not filtered out by the ROB as is shown in its signal. However, in the BdDOB signal, the initial offset, with a low frequency boundary within the cutoff frequency of BdDOB, is filtered out. Moreover, the ROB signal shows a large fluctuation of around -10 ~ 2 Nm before 1.4 sec, which is caused by the robot's motion and modeling error. When the threshold is configured to be 1 Nm, this fluctuation is large enough to cause the false collision detection during motion. However, the BdDOB, with an optimized cutoff frequency determined using the algorithm presented in this paper, successfully reduces about 90% of the modeling error effects.

5. Conclusion The tendency of the dynamic manipulator model's maximum frequency boundary is analyzed to solve the collision detection problem of false alarms caused by uncertainty in the dynamic model. The discovered tendency of the frequency boundary is applied to enhance the performance of the author’s previous collision detection algorithm, BdDOB, by determining an optimized pass band. The algorithm developed was evaluated experimentally by testing a collision between a multi-axis manipulator and a human body. In the experiment, the proposed collision detection algorithm not only quickly distinguishes the collision from other disturbances, but also shows robust performance under sensor data uncertainty. When this algorithm is applied to robots that share a workspace with humans, it is expected to enhancing the safety of human workers while maintaining the high performance and productivity of the robot.

Acknowledgment This work is supported by the Ministry of Trade & Energy

[1] A. Pervez and J. Ryu, Safe physical human robot interaction-past, present and future, Journal of Mechanical Science and Technology, 22 (3) (2008) 469-483. [2] O. Stasse et al., Integration of humanoid robots in collaborative working environment: a case study on motion generation, Intelligent Service Robotics, 2 (3) (2009) 153-160. [3] J. Too et al., Human-robot collaboration in cellular manufacturing: Design and development, Proc. of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, USA (2009). [4] S. Haddadin, A. Albu-Schaffer and G. Hirzinger, Safety evaluation of physical human-robot interaction via crashtesting, Robotics: Science and Systems (2007) 217-224. [5] P. P. Lele, D. C. Sinclair and G. Weddell. The reaction time to touch, The Journal of Physiology, 123 (1) (1954) 187-203. [6] L. Bascetta, G. Ferretti and P. Rocco, Towards safe human-robot interaction in robotic cells: an approach based on visual tracking and intention estimation, Proc. of International Conference on Intelligent Robots and System, USA (2011) 2971-2978. [7] L. Wang, Collaborative robot monitoring and control for enhanced sustainability, International Journal of Advanced Manufacturing (2013) 1-13. [8] J. Park, H. Kim and J. Song, Safe robot arm with safe joint mechanism using nonlinear spring system for collision safety, 2009 IEEE International Conference on Robotics and Automation, Japan (2009) 3371-3376. [9] J. Park, B. Kim, J. Song and H. Kim, Safe link mechanism based on nonlinear stiffness for collision safety, Mechanism and Machine Theory, 43 (10) (2008) 1332-1348. [10] S. Takakura, T. Murakami and K. Ohnishi, A robust decentralized joint control based on interference estimation, Proc. of 1987 IEEE International Conference on Robotics and Automation, USA (1987) 326-331. [11] S. Takakura, T. Murakami and K. Ohnishi, An approach to collision detection and recovery motion in industrial robot, Proc. of 15th Annual Conference of IEEE Industrial Electronics Society, USA (1989) 421-426. [12] A. D. Luca and R. Mattone, Sensorless robot collision detection and hybrid force/motion control, Proc. of the 2005 IEEE International Conference on Robotics and Automation, Spain (2005) 999-1004. [13] A. D. Luca and R. Mattone, Actuator failure detection and isolation using generalized momenta, Porc. of 2003 IEEE International Conference on Robotics and Automation, Taiwan (2003) 634-639.


[14] A. D. Luca, A. Albu-Schaffer, S. Haddadin and G. Hirzinger, Collision detection and safe reaction with the DLRIII lightweight manipulator arm, Porc. of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing (2006) 1623-1630. [15] B. Jung, H. R. Choi, J. C. Koo and H. Moon, Collision detection using band designed Disturbance Observer, Proc. of 2012 IEEE International Conference on Automation Science and Engineering, Korea (2012) 1080-1085.

Byung-jin Jung Received his B.S. degree in mechanical engineering from Sungkyunkwan University in 2011. He is currently on the Ph.D. collaboration course in mechanical engineering at Sungkyunkwan University. His research topics are Human-robot collaboration, safe manipulator, low-cost joint torque sensing and hydraulic actuator force control. Ja Choon Koo Received his B.S. degree from Hanyang University, Seoul, Korea, in 1989, and his M.S. and Ph.D. degrees from the University of Texas, Austin, in 1992 and 1997, respectively. He was the Advisory Engineer of International Machines Corporation (IBM), San Jose, CA, and a Staff Engineer of Samsung Information Systems America, San Jose, CA. He is currently a Professor in the School of Mechanical Engineering, Sungkyunkwan University, Suwon, Korea. His current research interests include design, analysis and control of dynamic systems, robotics, sensors and actuators.

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Hyouk Ryeol Choi Received his B.S. degree from Seoul National University, Seoul, Korea, in 1984, his M.S. from Korea Advanced Institute of Science and Technology (KAIST), Daejon, Korea, in 1986, and his Ph.D. degree from Pohang University of Science and Technology (POSTECH), Pohang, Korea, in 1994, all in mechanical engineering. From 1986 to 1989, he was an associate Engineer at LG Electronics Central Research Laboratory, Seoul. From 1993 to 1995, he was at Kyoto University, Kyoto, Japan, as a Grantee of a scholarship from the Japanese Educational Ministry. From 2000 to 2001, he visited the Advanced Institute of Industrial Science Technology (AIST), Tsukuba, Japan, as a Japan Society for the Promotion of Sciences (JSPS) Fellow. Since 1995, he has been with Sungkyunkwan University, Suwon, Korea, where he is currently a Professor in the School of Mechanical Engineering. He was an Associate Editor of IEEE Transactions on Robotics (TRO). He is currently an Associate Editor of the Journal of Intelligent Service Robotics, International Journal of Precision Engineering and Manufacturing (IJPEM) and the Editor of International Journal of Control, Automation and Systems (IJCAS). His current research interests include dexterous mechanisms, field application of robots, and soft mechatronics. Hyungpil Moon Received his B.S. and M.S. degrees in mechanical engineering from Pohang Science and Technology Institute in 1998, and obtained his Ph.D. in mechanical engineering from the University of Michigan, Ann Arbor. He is working as an associate Professor in the department of mechanical engineering at Sungkyunkwan University. His current research topics are robotic manipulation, robot hands, SLAM, hydraulic robots and polymer-based sensors and actuators.