Implementation of Object Graping Control for a Robot Arm Uing Fuzz Control and Two-CCD Imaging Meaurement Cheng-Hao Huang, en-june ang*, Fellow, IEEE, Patrick Benaidez, Student Member, IEEE, Nai-Jen Li, Chi-Sheng Hu, Po-Chien Tai, and Mo Jamhidi, Fellow, IEEE Abtract An implementation of an object graping control tem for a robot arm i preented in thi paper. The hardware of the tem contain a fabricated robot arm with a gripper, a pair of charge-coupled deice (CCD) camera, and a computer a the control center. The two CCD camera are ued to ee the target object and the two-ee imaging geometr i ued to calculate the target poition in 3-D pace. In the control proce, the inere-kinematic (IK) concept i utilized to drie the robot arm. Firt let the robot arm reach a point round the target object uch that the target object locate inide the effectie graping region. A fuzz baed object approaching control i propoed then to adjut the gripper poition uch that the poition error between the target object and gripper poition i reduced until the target object poition i reached. Thu, the gripper can grap the target object preciel. Two eperimental cae are conducted to demontrate the effectiene of the propoed control, in which the target object are an egg and a bottle, repectiel. Inde Term 3-D Poition Meaurement, Charge-Coupled Deice (CCD) Geometr, Fuzz Control, Inere Kinematic (IK), Object Graping Control, Robot Arm 1
I. INTRODUCTION Robot arm are uuall epected to perform repeated and erant tak which are in need of precie motion control. In thi tud, the robot arm with a gripper (hown in Fig. 1) i deigned to eecute the object graping behaior. Building an appropriate enor feedback control tem i mandator to achiee the pecific control purpoe. Preiou literature regarding to the robot arm motion control are reiewed in the following paragraph. Furuta et al. [1] propoed the dnamic equation of a robot arm; baed on the laer beam enor feedback, a PID controller i deigned to achiee the trajector tracking for a robot arm. hite et al. [2] deeloped a graphic imulator for a robot arm to compute the coordinate of each joint uch that the robot arm can be controlled to track an aigned trajector. Munainghe et al. [3] preented an off-line trajector generation algorithm to ole the contouring problem of indutrial robot arm. Uing the Mitubihi PA-10 robot arm platform, Kenned and Deai [4] propoed a harmonic drie tranmiion model to inetigate the grait and material influence to the robot arm. Then the robot arm can be controlled to track a deired trajector and the motion error can be analzed. In [5], a two-link robot arm wa controlled b a fuzz liding mode controller in which parameter are adjuted b fuzz-neural method. Furthermore, the inere kinematic (IK) i a well-known concept and ha been widel applied in robot arm motion control. Shen et al. [6] applied a elf-configuration fuzz tem to find the IK olution for a robot arm. Feliu et al. [7] emploed the IK-baed two-neted control-loop cheme to control the tip poition b uing joint poition and tip acceleration feedback. The paper [8] propoed an on-line algorithm baed on a econd-order IK to enure the path tracking capabilit under joint limit of a robot arm. Shimizu et al. [9] propoed an analtical methodolog of IK computation for a 7-DOF redundant robot arm with joint limit. 2
Baed on Riemannian geometr, the paper [10] propoed an optimal etended Jacobian IK algorithm for robot arm. Uing the IK technique, the robot arm in [11] wa deigned to puh the button of an eleator. Recentl, a iion-baed enor technique for contructing a 3-D meaurement (or called tereo meaurement) i getting popular in etimating the 3-D coordinate of object for robotic application. Nilon and Holmberg [12] combined a 2-D iion camera and an ultraonic range enor together for the robot gripper to etimate the object poition. Baek and Lee [13] ued two camera and one laer to recognize the eleator door and to determine it depth ditance. Okada et al. [14] deigned and implemented a knowledge baed iual 3-D object recognition tem with multi-cue integration and particle filter technique. Moreoer, the photogrammetric method were propoed in [15] [17], which utilize object feature to meaure their ditance. Gao et al. [18] preented a ignal proceing pipeline for 3-D tereocopic camera to do camera calibration and depth etimation. Uing an RGB color hitogram, the paper [19] effectiel applied color image to achiee the 3-D meaurement. Hu et al. [20] propoed an image-baed 3-D meauring tem to meaure ditance and area b uing a ingle charge-coupled deice (CCD) camera and two laer projector. In [21] and [22], ditance and angle meaurement for object were made baed on piel ariation in CCD image. Da and Ahuja [23] compared the performance of the binocular cue of tereo and ergence, and the monocular cue of focu to improe the accurac of range etimation. The paper [24] adopted two camera and a laer projector to meaure the edge of an object regardle of it poition. Object graping control for a robot arm i the main objectie of thi paper. A two-ccd iion tem with the imaging geometr i preented to meaure the object and the gripper poition in 3-D pace. To manipulate the robot arm, the IK technique [6] i utilized to achiee the motion control. Baed on the practical poition error between the gripper and the target object, 3
thi paper propoe a fuzz object approaching control to drie the gripper to reach the target object preciel. Thi i performed uch that the gripper can firml grap the object. ith the object picked up b the robot, graping control i complete. Finall, two real eperiment are implemented to erif the feaibilit of the propoed control cheme. The ret of thi paper i organized a follow; Section II briefl introduce the real robot arm tem, while Section III preent the two-ccd iion tem with it two-ee imaging geometr to calculate the target poition in 3-D pace. In Section IV, the object graping control baed on the IK technique and the fuzz object approaching control are propoed. Practical eperiment are gien in Section V to ealuate the propoed control cheme and Section VI proide concluion on the technique and eperiment. II. DESCRIPTION OF THE ROBOT ARM SYSTEM Thi ection introduce the robot arm tem, which i deigned to implement the object graping behaior in 3-D pace. Figure 1(a) how a photograph of the robot arm tem and Fig. 1(b) depict the framework of the tem. The tem i compried of a fabricated robot arm with a gripper, a et of two-ccd iion deice, and a computer a the control center. Referring to Fig. 1, the hardware of the tem i preciel decribed a follow. A. Robot Arm The robot arm conit of eeral ero motor with metal connection. There are three joint (joint 1, 2, and 3) in the robot arm for the geture motion in 3-D pace (ee Fig. 1). Joint 1, 2, and 3 are deigned for the houlder rotation, lifting moement, and elbow bending, repectiel. At the end of the robot arm, a two-palm gripper drien b two mall ero motor i ued for the object graping. Two piece of kidproof mat are pated inide the gripper palm to 4
increae the graping friction, while two green pattern are pated outide the palm to aid the gripper image detection. Each ero motor i combined with a peed-reducer gearbo to increae the rotar torque, and i controlled b erial recommended tandard-232 (RS-232) command ignal from the computer. B. Two-CCD Viion Deice Senor feedback for the robot arm object graping control depend on the iion capabilit which i proided through ue of two CCD camera (Logitech C905 USB camera) that are mounted in parallel on the top platform (ee Fig. 1). Both the gripper and the target object are generall captured b the two CCD with thi etup. The 3-D poition of the gripper or the target object can now be calculated b the two-ee imaging geometr. According to the pecification of the CCD camera, the camera reolution i 640 320 piel and the capturing rate i 30 frame per econd. C. Control Center The control center of the robot arm tem i a laptop computer with an Intel Core 2 Duo 2.4 GHz CPU and 2 GB random-acce memor (RAM). The computer i emploed to eecute the two-ee image proceing, pattern recognition, imaging geometr calculation, 3-D poition meaurement, and the motion control for the robot arm. Borland C++ Builder i the integrated deelopment enironment (IDE) ued to deelop the control oftware. III. TO-CCD IMAGING MEASUREMENTS The main objectie in thi paper i to control the robot arm to reach the accurate poition uch that the robot gripper can grap the target object uccefull. Hence, the firt tak i to 5
meaure the 3-D poition of the robot gripper and the target object b uing the two-ccd iion deice. Before the geometric anali, three 3-D coordinate tem in the working pace are defined (ee Fig. 1). The firt i the world coordinate [X, Y, Z] T a the main coordinate, and the econd i the iion coordinate [X, Y, Z ] T V for the CCD meaurement. Notabl, the aboe two coordinate hae the ame origin O(0, 0, 0) O (0, 0, 0) V locating at the center poition of the iion deice (ee Fig. 1), where O and O repreent the world and the iion origin, repectiel. Furthermore, the lat i the kinematic coordinate [X k, Y k, Z k] T K for the motion control of the robot arm, where the kinematic origin O k(0, 0, 0) K locate at the houlder poition. A. Single-CCD Imaging Geometr Let the imaging geometr for a ingle CCD be firtl dicued. Suppoe that the iew plane of a target 3-D poition i perpendicular to the optical ai of a fied CCD camera, and the captured image can be projected onto a 2-D imaging frame. Figure 2 how the chematic diagram of a ingle CCD camera capturing a patial image baed on a pinhole camera model, in which f repreent the CCD focal length and repreent the photographic ditance between the CCD and the iew plane. The center of the piel-baed imaging frame i denoted b C p, C i the CCD optical point on the iew plane, and the iion origin O i at the CCD len. Furthermore, (,, z ) repreent the poition of the robot gripper or the target object V on the iew plane and (, z ) repreent the reflected coordinate on the imaging frame. Once p p the 2-D ector ( p, z p) i found b the piel computation, the alue of and z are deried from (1) baed on the imilar triangle concept. 6
f z p p z f (1) Howeer, ince the photographic ditance can not be meaured from an image captured b the ingle CCD, the alue of can not be eactl meaured. That mean a ingle CCD i difficult to meaure the 3-D poition (,, z ). Therefore, b epanding the ingle CCD meauring V method to a two-ccd iion tem, a 3-D poition meaurement i obtained. A two-ccd tem i uggeted in thi paper. B. Two-CCD Imaging Geometr Two CCD camera are et up in parallel and at the ame height to capture a perpendicular iew plane. Pair of different imaging frame can be obtained from the camera for further geometric anali. Figure 3 illutrate the chematic diagram of the two-ee imaging geometr for the 3-D poition meaurement, in which f and 1 f 2 are focal length of CCD1 and CCD2, repectiel, and D i the eparated ditance between CCD1 and CCD2. Moreoer, C p1 and C p2 are the center of piel-baed imaging frame 1 and 2, repectiel. Then C 1 and C 2 denote two CCD optical point on the iew plane, repectiel, and the iion origin O i the midpoint between CCD1 and CCD2. A hown in Fig. 3, the 3-D poition meaurement obtain (,, z ) uing the two 2-D V piel ector ( p 1, z p1) and ( p2, z p2), which are the reflected coordinate on the imaging frame 1 and 2, repectiel. Referring to Fig. 3(b), the eparated ditance D proide a method to meaure the photographic ditance. 7
D (2) p1 p2 1 2 f1 f2 From (2), the ditance i firtl obtained in (3a). Then the alue of and z are obtained from (3b) and (3c), repectiel. D p1 p2 f f 1 2 (3a) 1 2 f p1 p2 f 1 2 (3b) zp 1 zp2 z or z f1 f2 Conequentl, the meaurement of 3-D poition V (3c) (,, z ) for the robot gripper or the target object i achieed. C. Coordinate Tranformation In the working pace, the iion origin O i equall aigned a the world origin, i.e., O O. Howeer, the iion deice i capable to rotate it iual direction. There eit a three-phae difference in angle between the iion and the world coordinate, which can be diided into a pitch angle, a roll angle, and a aw angle z, a depicted in Fig. 4. Herein, the three-phae rotation matrice in [12], [25] can be ued to tranform the iion coordinate [X, Y, Z ] T V to the world coordinate [X, Y, Z] T. co z in z 0 co 0 in 1 0 0 in z co z 0 0 1 0 0 co in z 0 0 1 in 0 co 0 in co z (4) Conequentl, a deired 3-D poition (,, z ) in iion coordinate can be decribed a a V 8
calibrated world coordinate (,, z ). IV. OBJECT GRASPING CONTROL DESIGN A. Inere Kinematic Baed Motion Control Recall to the fabricated robot arm (hown in Fig.1), thi paper applie the IK technique to manipulate the robot arm uch that the robot gripper can reach the acceptabl accurate poition and grap the object. Figure 5 how the IK concept which i a mapping from the working pace to the joint pace [6]. ith the current poition of the robot gripper i (,, z ) in the k k k K kinematic coordinate, the mapping joint angle ( 1, 2, 3) for the robot arm can be obtained b geometric deriation uing IK. Figure 6 illutrate the kinematic coordinate of the robot arm, where O k i the kinematic origin, Q i the elbow node, and G (,, z ) i the gripper IK k k k K poition. The length of the link 1 and 2 are denoted a d 1 and d 2, repectiel. Thu, the two link with the three joint ( 1, 2, 3) are etablihed o that the robot arm can hae fleibilit in 3-D pace (ee Fig. 1 and Fig. 6). For further geometric deriation, a projectie robot arm can be found on the Yk-Z k plane, where R and S repreent the projectie point of G IK and Q, repectiel. Additionall, the ai repreent the direction along 1 and 2 0 on the Y -Z plane. The G i the projectie point of G IK along the link 1 direction, and the G i k k the projectie point of G along the ai. Let u proide three figure (Fig. 7(a) (c)) to introduce the deiation of the rigid kinematic of the robot arm. Figure 7(a) how the two-link arm plane, where L z i the 2 2 2 k k k ditance between the gripper G IK and the kinematic origin O k. The elbow bending joint angle 3 i obtained a follow. 9
3 2 2 2 1 d1 d2 L co, where 0 3 2 2dd 1 2 (5) In the Xk - plane hown in Fig. 7(b), the lifting moement joint angle 2 can be deried a 1 k 2 2 d1 d2 co3 in, where 0 2. (6) Furthermore, Fig. 7(c) depict the projected arm on the Yk-Z k plane where L z. 2 2 z k k Conequentl, the houlder rotation joint angle 1 i obtained. 1 2 2 2 L 1 z ( d1 d2 co 3) co 2 ( d2 in 2) 1 z k co in, where 0 1 2 2 Lz ( d1 d2 co 3)co2 L z ith the tranform of ( 1, 2, 3) in (5) (7) into the motor control ignal, the robot gripper can (7) be ecepted to reach the deired poition G (,, z ). Moreoer, ince the kinematic origin IK k k k K O locate at the houlder poition which ha a hifting ditance O k k O awa from the iion origin O, the hifting relationhip between the kinematic coordinate [X k, Y k, Z k] T K and the world coordinate [X, Y, Z] T i a follow. (,, z) O O (,, z ) (8) k k k k Therefore, a gripper poition G (,, ) IK k k z k K can ield the correponding G (, IK, z ) in the world coordinate. Remark 1: Practicall, the motor backlah problem and the hardware uncertaintie ma affect the 3-D poition accurac of the IK technique, i.e., the robot gripper can not accuratel approach a deired poition b onl uing the IK method. Hence, the two-ccd deice i uggeted to 10
proide a iion-baed enor feedback to improe the open-loop IK technique, a the two-ee imaging meaurement can be utilized to determine the poition error. To reduce the poition error and to achiee the object graping motion, an object approaching trateg i further propoed in the following ubection. B. Object Approaching Strateg Thi paper preent a pecific object approaching method for the two-palm gripper (ee Fig. 1) to guarantee a ucceful graping behaior. Figure 8 how an effectie graping region for the gripper. Firt, the gripper reache a preet point around the target object uch that the target object i inide the front region of the gripper. Net, the gripper i controlled to approach and grap the target object. In the approaching proce, thi paper mut highlight three 3-D point. The firt i the deired IK point G IK for the gripper, which i aigned to manipulate the robot arm. The remaining two are G and T, which are the practical poition of the gripper and the target object, repectiel. Notabl, G and T can be recognized and meaured b the two-ccd iion deice. Furthermore, let the poition error be defined a e (G T ) ( e, e, e ), z (9) where e denote the error on the -ai, where,, z. The approaching control ue a fuzz rule bae (FRB) (10) in which the antecedent i e and the conequent i the tep parameter for adjuting the deired IK point G IK. Both are decompoed into fie fuzz et, including NB (negatie big), NS (negatie mall), ZO (zero), PS (poitie mall), and PB (poitie big). 11
Rule1: If e i NB, then i NB; Rule 2: If e i NS, then i NS; FRB: Rule 3: If e i ZO, then i ZO; Rule 4: If e ips, then ips; Rule 5: If e ipb, then ipb; (,, z) (10) Figure 9 how the memberhip function of e and, where,, z. The defuzzification trateg i implemented b the weighted aerage method [26], [27]. 5 r r ( e ) u r1, (,, z) (11) r where ( ) i the fired weight of e and e r u i the upport of the conequent fuzz et in the rth rule, and 5 r ( e ) 1. r1 C. Object Graping Control Deign Referring to Fig. 8, the IK technique and the fuzz baed approaching control are ummarized, and the complete object graping control procedure are preented a the following: Step 1: Recognition and meaurement of the target object point T. Step 2: Let the initial IK poition be a hifting point around the target object, i.e., (0) G IK (T T), where T i a preet hifting ector. B uing the IK method, the robot gripper i controlled and epected to approach (0) G IK and hae the target object be located inide the effectie graping region. Step 3: Ue the two-ccd iion tem to meaure the practical poition () G i of the gripper. Then the poition error can be ealuated a e (G T ). If the poition error ( i) ( i) () i e atifie the accurate condition e () i, for all,, z (12) 12
the control i terminated. That mean, within thi mall poition error, the gripper can grap the target object. Here, repreent the error threhold on the -ai, where,, z. Otherwie, continue to Step 4. Step 4: Ue the FRB (10) and the defuzzification (11) to determine the approaching tep parameter, (,, z). The adjuting tep i computed a e on the -ai, () i where,, z. Then the net IK poition can be updated a follow. ( 1) ( ) ( ) ( ) ( ) G i G i ( i, i, i IK IK e e z ez ) (13) Step 5: Baed on the change of the IK poition, the robot gripper i controlled to take motion and keep approaching target object. Repeat Step 3. Oerall, i denote the control iteration count. According to the aboe fie tep, the object graping control i completed, i.e., the target object can be graped uccefull. V. EXPERIMENTAL RESULTS Two eperimental cae are performed to erif the feaibilit of the propoed object graping control for the robot arm (hown in Fig. 1). In the robot arm, the two link are repreented b the length d1 17 cm and d2 26 cm. The target object to be graped in the eperiment are an egg and a metal drinking bottle. The two-ccd iion deice i et a Fig. 3 (recall Section III.B). The eparated ditance D 3 cm eit between CCD1 and CCD2. The focal length of CCD1 and CCD2 are et the ame a f1 f2 265 piel. Moreoer, the rotation difference between [X, Y, Z ] T V and [X, Y, Z] T i (,, ) (27, 0, 60 ) (recall Section III.C and Fig. 4). z Figure 10 i a erie of frame to demontrate that the robot arm achiee an egg graping 13
behaior in the firt eperiment. Initiall, two CCD capture an egg and it poition i meaured b the two-ee imaging geometr a T (5.37, 39.42, 15) (ee the iteration i 0 in Fig. 10). The hifting ector i gien a T (0, 0, 10), and the initial IK point i (0) G IK (TV T) (5.37, 39.42, 5). Then the robot gripper i firtl manipulated to reach the point (1) G (7.71, 37.81, 6.97) where the coordinate alue are obtained b the two-ee meaurement (ee the iteration i 1 in Fig. 10). Moreoer, the poition error i calculated a e (G T ) (2.34, 1.61, 8.02), where the error threhold are predefined a 2.5, (1) (1) 1.5, and 1.5. Since the poition error z (1) e doe not atif the accurate requirement (due to e (1) 1.61 and e (1) z 8.02 ), a new IK poition i generated a z (1) G IK (4.77, 40.24, 10.21) b (13) uing the fuzz object approaching control (10) (11). Therefore the robot arm i controlled again to adjut the gripper poition to approach the target object. After three iteration (moement), the robot gripper reache the poition (3) G (7.17, 40.62, 15.19) uch that the poition error i hortened to meet the accurate requet (ee the iteration i 1 to i 3 in Fig. 10). Then the object approaching control i terminated, ince the gripper poition (3) G i cloe enough to the target poition T. Finall, the robot gripper can firml grap the egg and lift it (ee image after the iteration i 3 in Fig. 10). Figure 11 how the econd eperiment that the robot arm perform the bottle graping tak. In thi cae, the hifting ector i gien a T (1.5, 5, 3). The error threhold are again defined a 2.5, 1.5, and z 1.5. Similar to the control proce of the firt eperiment, the bottle poition i meaured a T (11.52, 41.98, 8.5) (ee the iteration 14
i 0 in Fig. 11). Baed on the IK motion control and the object graping control, the gripper i controlled to reach and grap the bottle uccefull (ee Fig. 11). The two eperiment conducted how that with the IK technique, two-ee meaurement, and fuzz object approaching controller, the robot arm can uccefull achiee the object graping tak. VI. CONCLUSION Thi paper ha preented a two-ee iion-baed method for the target object graping with a robot arm. The adopted technique contain the IK motion control, two-ee imaging meaurement, and fuzz object approaching control. The completed control cheme ha been implemented into a laptop computer to manipulate the robot arm for achieing the deired behaior. The eperimental reult hae indeed erified that the propoed control cheme i ueful and effectie for the real robot arm. ACKNOLEDGMENT Thi work wa upported b National Science Council, Taiwan, under the Grant NSC 98-2221-E-008-118-MY3 and NSC 100-2917-I-008-004. REFERENCE [1] K. Furuta, K. Kouge, and N. Mukai, Control of articulated robot arm with enor feedback: Laer beam tracking tem, IEEE Tran. Ind. Electron., ol. 35, no. 1, pp. 31 39, Feb. 1988. 15
[2] R. B. hite, R. K. Read, M.. Koch, and R. J. Schilling, A graphic imulator for a robot arm, IEEE Tran. Educ., ol. 32, no. 4, pp. 417 429, No. 1989. [3] S. R. Munahnghe, M. Nakamura, S. Goto, and N. Kura, Optimum contouring of indutrial robot arm under aigned elocit and torque contraint, IEEE Tran. St. Man Cbern. Part C-Appl. Re., ol. 31, no. 2, pp. 159 167, Ma 2001. [4] C.. Kenned and J. P. Deai, Modeling and control of the Mitubihi PA-10 robot arm harmonic drie tem, IEEE-ASME Tran. Mechatron., ol. 10, no. 3, pp. 263 274, Jun. 2005. [5] M. Ö. Efe, Fractional fuzz adaptie liding-mode control of a 2-DOF direct-drie robot arm, IEEE Tran. St. Man Cbern. Part B-Cben., ol. 38, no. 6, pp. 1561 1570, Dec. 2008. [6]. Shen, J. Gu, and E. E. Milio, Self-configuration fuzz tem for inere kinematic of robot manipulator, in Proc. NAFIPS Annu. meeting N. Am., Montreal, QC, Canada, Jun. 2006, pp. 41 45. [7] V. Feliu, J. A. Somolino, and A. Garcia, Inere dnamic baed control tem for a three-degree-freedom fleible arm, IEEE Tran. Robot. Autom., ol. 19, no. 6, pp. 1007 1014, Dec. 2003. [8] G. Antonelli, S. Chiaerini, and G. Fuco, A new on-line algorithm for inere kinematic of robot manipulator enuring path tracking capabilit under joint limit, IEEE Tran. Robot. Autom., ol. 19, no. 1, pp. 162 167, Feb. 2003. [9] M. Shimizu, H. Kakua,.-K. Yoon, K. Kitagaki, and K. Kouge, Analtical inere kinematic computation for 7-DOF redundant manipulator with joint limit and it application to redundanc reolution, IEEE Tran. Robot., ol. 24, no. 5, pp. 1131 1142, Oct. 2008. 16
[10] K. Tchoń, Optimal etended Jacobian inere kinematic algorithm for robotic manipulator, IEEE Tran. Robot., ol. 24, no. 6, pp. 1440 1445, Dec. 2008. [11].-J. ang, C.-H. Huang, I.-H. Lai, and H.-C. Chen, A robot arm for puhing eleator button, in Proc. SICE Annu. Conf., Taipei, Taiwan, Aug. 2010, pp. 1844 1848. [12] A. Nilon and P. Holmberg, Combining a table 2-D iion camera and an ultraonic range detector for 3-D poition etimation, IEEE Tran. Intrum. Mea., ol. 43, no. 2, pp. 272 276, Apr. 1994. [13] J.-Y. Baek and M.-C. Lee, A tud on detecting eleator entrance door uing tereo iion in multi floor enironment, in Proc. ICROS-SICE Int. Joint Conf., Fukuoka, Japan, Aug. 2009, pp. 1370 1373. [14] K. Okada, M. Kojima, S. Tokutu, T. Maki, Y. Mori, and M. Inaba, Multi-cue 3D object recognition in knowledge-baed iion-guided humanoid robot tem, in Proc. IEEE/RSJ Int. Conf. Intell. Robot. St., San Diego, CA, USA, Oct. 2007, pp. 3217 3222. [15] C. S. Fraer and S. Cronk, A hbrid meaurement approach for cloe-range photogrammetr, ISPRS J. Photogramm. Remote Sen., ol. 64, no. 3, pp. 328 333, Ma 2009. [16] F. A. an den Heuel, 3D recontruction from a ingle image uing geometric contraint, ISPRS J. Photogramm. Remote Sen., ol. 53, no. 6, pp. 354 368, Dec. 1998. [17] D.-H. Zhang, J. Liang, and C. Guo, Photogrammetric 3D meaurement method appling to automobile panel, in Proc. 2nd Int. Conf. Comput. Automat. Eng. (ICCAE), Feb. 2010, pp. 70 74. [18] Z.-. Gao,.-K. Lin, Y.-S. Shen, C.-Y. Lin, and.-c. Kao, Deign of ignal proceing pipeline for tereocopic camera, IEEE Tran. Conum. Ellectron., ol. 56, no. 2, pp. 324 3331, Ma 2010. 17
[19] T. Egami, S. Oe, K. Terada, and T. Kahiwagi, Three dimenional meaurement uing color image and moable CCD tem, in Proc. 27th Annu. Conf. IEEE Ind. Electron. Soc., Dener, CO, No. 2001, pp. 1932 1936. [20] C.-C. Hu, M.-C. Lu,.-Y. ang, and Y.-Y. Lu, Three-dimenional meaurement of ditant object baed on laer-projected CCD image, IET Sci. Mea. Technol., ol. 3, no. 3, pp. 197 207, Ma 2009. [21] C.-C. Hu, M.-C. Lu,.-Y. ang, and Y.-Y. Lu, Ditance meaurement baed on piel ariation of CCD image, ISA Tran., ol. 48, no. 4, pp. 389 395, Oct. 2009. [22] C.-C. J. Hu, M.-C. Lu, and Y.-Y. Lu, Ditance and angle meaurement of object on an oblique plane baed on piel number ariation of CCD image, IEEE Tran. Intrum. Mea., ol. 60, no. 5, pp. 1779 1794, Ma 2011. [23] S. Da and N. Ahuja, A comparatie tud of tereo, ergence, and focu a depth cue for actie iion, in Proc. IEEE Comput. Soc. Conf. Comput. Vi. Pattern Recognit. (CVPR 93), Jun. 1993, pp. 194 199. [24] J. J. Aguilar, F, Torre, and M. A. Lope, Stereo iion for 3D meaurement: accurac anali, calibration and indutrial application, Meaurement, ol. 18, no. 4, pp. 193 200, Aug. 1996. [25] L. Sciaicco and B. Siciliano, Modelling and Control of Robot Manipulator. Springer, 2000. [26] T.-H. S. Li, S.-J. Chang, and. Tong, Fuzz target tracking control of autonomou mobile robot b uing infrared enor, IEEE Tran. Fuzz St., ol. 12, no. 4, pp. 491 501, Aug. 2004. [27] C.-H. Huang,.-J. ang, and C.-H. Chiu, Velocit control realiation for a elf-balancing tranporter, IET Contr. Theor Appl., ol. 5, no. 13, pp. 1551 1560, 2011. 18