A Novel Grey-RSS Navigation System Design for Mobile Robots

Transcription

1 A vel Grey-RSS Navigation Sytem Deign for Mobile Robot Albert Wen-Long Yao*,, Hin-Te Liao, and Shiou-De Chen Department of Mechanical and Automation Engineering, National Kaohiung Firt Univerity of Science and Technology, Taiwan Department of Mechanical Engineering of Ming-Hin Univerity of Science and Technology, Taiwan (Received January 0; Accepted 3 February 0; Publihed on line June 0) *Correponding author: yao@nkfut.edu.tw DOI: /aumt.vi.37 Abtract: Thi tudy aim at developing a novel intelligent navigation ytem for mobile robot that eliminate cumulative error by uing a conventional optical encoder. The optical encoder for mobile robot wa upplanted by an intelligent Grey-RSS navigation ytem (IGRNS) with a RFID ytem embedded with a RSS (Received Signal Strength) location etimation cheme and a Grey predictor for robot rotation angle prediction. The RFID ytem indicate the target with active RFID tag. The RSS location etimation cheme then calculate the poible route of robot. The Grey controller optimize the traveling route quickly. The imulation reult how that the propoed IGRNS can reduce robot unneceary rotation in order to locate the target. With IGRNS, mobile robot take fewer tep to reach target quickly. The performance of IGRNS i better than the one of RSS navigation ytem. Keyword: Intelligent Grey-RSS navigation ytem; RFID; Navigation ytem; Mobile robot I. Introduction waday, robot have more and more functional application, a well a come in a number of different form, uch a navigational robot, cleaning robot, ecurity robot, nuring care robot, and o on. Thee intelligent robot uually need to move freely in their work area. A uch, it i very important to be able to recognize a robot poition. Generally, GPS technology i able to provide outdoor navigation robot accurate navigation and poitioning function. However, the navigation and poitioning job of an indoor mobile robot become more complicated due to the obtacle of being indoor. Thi ituation ha drawn a lot of attention from reearcher to develop a uitable navigation and poitioning ytem for indoor mobile robot. Generally, the conventional robot navigation and poitioning ytem i compoed of optical encoder and gyrocope (Figure ). The robot ha to know the ditance between the tarting point and the detination. Surveying the topography and environment ha to be done beforehand, and i uually a very time-conuming tak. Another difficulty encountered with indoor robot come from wheel-liding after the robot travel long ditance. Thi can generate eriou poitioning error after long ditance traveling of robot, and the cumulative error become troubleome to deal with. Figure. Conventional mobile robot ausmt Vol.. (0)

2 A vel Grey-RSS Navigation Sytem Deign for Mobile Robot There are a number of tudie in the literature addreing indoor mobile robot and the iue of cumulative error. Yu [] developed an obtacle avoidance robot by uing a wheel drive and optical encoder navigation method. He explained that cumulative error come from wheel liding while a robot i turning. He claimed the error may be avoided by uing larger wheel or high reitance rubber wheel. Jeng [] claimed that poitioning accuracy can be raied by uing optical flow enor intead of uing optical encoder. Bahl and Padmanabhan [3] developed a RADAR (Radio Detection and Ranging) ytem with active RF badge. The active badge pread infrared ignal which were picked up by a receiver. They adopted the actual meaurement and ignal propagation model to identify the poition of the robot. Ni, Liu and Patil [] preented a LANDMARC (Location Identification baed on Dynamic Active RFID Calibration) ytem for indoor robot poitioning. Their ytem ue four reader and 6 active tag for poition calculation and tracking. Thi ytem employ reference tag to improve the accuracy of poitioning. The cumulative error of mobile robot occur becaue of the inherent character of optical encoder and the robot wheel after moving a long ditance. To contruct map information i alo very time-conuming and laboriou. Thi ha lead u to develop an intelligent navigation ytem for indoor mobile robot. Thi paper i organized a follow: Section II decribe the applied methodologie; Section III illutrate the deign of the ytem; Section IV preent and dicue the imulation reult; and Section V preent the concluion. method and Grey prediction theory. The ditance and orientation of the target detination i etimated by meauring the ignal trength of the RFID tag on the target. The poitioning method i done by RSS and Grey prediction theory. Becaue the meaured ignal trength i eaily diturbed by the indoor environment, a lot of error may be generated. A a reult, the robot can, hardly reach the target. Therefore, we adopted Grey prediction theory to improve and optimize the route etimation and the navigation path. RSS Method The RSS method i baed on the power propagation model of a ignal to calculate the ditance between the tranmitter and receiver [5]. In the free pace of electromagnetic wave propagation, the power will decay with ditance. The Equation of radio propagation i Pt Gt Gr Pr () r, () ( ) r L where Pr () r i the received power; P t i the tranmitted power; G t i the tranmit antenna gain; G r i the receiving antenna gain; i wavelength; r i propagation ditance; L i ytem lo factor. In Equation (), only the ditance (r) i unknown, and the other are meaureable or ytem contant. II. Methodology In order to locate the poition of the target, thi propoed ytem adopted RFID technology, the RSS Albert Wen-Long Yao i a profeor in the Department of Mechanical and Automation Engineering of National Kaohiung Firt Univerity of Science and Technology (Firt Tech.), Taiwan. He ha publihed more than 6 journal paper and ha 0 patent. Profeor Yao received hi Ph.D. from New Jerey Intitute of Technology (NJIT) in 998. He i alo a regitered profeional engineer (RPE) in Connecticut, U.S.A. Dr. Yao alo erved a intrumentation engineer of Aero Indutrial Development Center (AIDC), and wa a enior control engineer of the AdvanTech Corporation, NJ, U.S.A. Hi academic interet are automation, robotic and control, ytem integration, mechatronic, revere engineering and rapid prototyping, and virtual reality. Hin-Te Liao i an aociate profeor in the Department of Mechanical Engineering of Ming-Hin Univerity of Science and Technology (MUST). Dr. Liao ha publihed more than 3 paper and own 3 patent. He alo conducted more than 0 indutry-academia collaboration. Dr. Liao alo erved a engineer of Nan Ya Platic Corporation. He received hi Ph.D. degree from NJIT in 995. Hi academic interet are robotic and control, mechatronic, and rapid prototyping. Shiou-De Chen i a enior project engineer of Pegatron Corporation, Taipei, Taiwan. He earned hi Mater degree in Mechanical Engineering from National Kaohiung Firt Univerity of Science and Technology, Kaohiung, Taiwan in 008. r Receiver Receiver Figure. RSS location algorithm diagram. Receiver 3 Tranmitter Figure i the pictorial illutration of the RSS algorithm. For ditance calculation, move the receiver to three place around the tranmitter to meaure their individual trength of receiving the RFID ignal. Then, draw three circle uing the location of the receiver a the center and the ditance from the tranmitter a radiu. The Equation for ditance calculation i: r r ausmt Vol.. (0)

3 Albert Wen-Long Yao, Hin-Te Liao, and Shiou-De Chen r ( x x) ( y y), () i i i where r i i the ditance between tranmitter and receiver; ( xi, y i) i the location of the receiver; ( xy, ) i the location of tranmitter. From thee meaurement and Equation (), the location of tranmitter can be calculated by: x ( T T A A) ( A B) y, (3) where ( x x) ( y y) ( x3 x) ( y3 y) A, ( xn x) ( yn y) r r x y x y r r3 x3 y3 x y B. r rn xn yn x y y y [ in( )] In the ame way, ( x, y) can alo be obtained from ( x, y ). Hence, the n th poition ( xn, y n) can be acertained from ( xn, yn ) : Let x x [ co( ( ))] n n 0 n y y [ in( ( ))] n n 0 n n total i, the Equation can be implified a: i0 xn xn co( total ) () yn yn in( total ) (5) Equation () and (5) are the formula for the trajectory calculation. In conjunction with the RSS algorithm, the coordinate of target ( xy, ) can be calculated. Figure how the flowchart of coordinate calculation algorithm. Start Meaurement RSS Record Data RSSI i =NewRSSI Angle i =NewAngle Figure 3. Robot trajectory coordinate calculation algorithm. Trajectory Calculation Algorithm Adopting the RSS algorithm to get the tranmitter coordinate need three receiver location. The calculation algorithm i hown in Figure 3. The location coordinate can alway be calculated from the trajectory (x, y ), (x, y ),, (x n, y n ) of the robot by the revere calculation method. At the tart, the coordinate of the robot are at the origin, ( x0, y0) (0,0). The rotation angle i zero, 0 0. The next coordinate ( x, y ) can be obtained from ( x, y ) a follow: 0 0 Grey prediction mode? Prediction Rotation Angle GMAngle Reviion Rotation Angle Angle i =Angle i +GMAngle/ End Y N x x [ co( )] S 0 0 Figure. Flowchart of coordinate calculation algorithm ausmt Vol.. (0)

4 A vel Grey-RSS Navigation Sytem Deign for Mobile Robot Grey Prediction Algorithm Grey theory wa firt introduced by Prof. Deng in 98 [6]. The main merit of Grey theory i that to build up a Grey model (GM), one only need at leat four hitorical data. The Grey prediction model i a follow: Step. Form a row matrix: x x x x n { (), (),, ( )} ( x ( k), k,,3,, n) (6) Step. Accumulated Generating Operation (AGO): () () () () x x x x n { (), (),, ( )} () ( x ( k), k,,3,, n) (7) Figure 5. Signal trength-ditance curve. where k () x ( k) x ( m). Step 3. Form Grey differential Equation: where () () () () z z z z n ( (), (3),, ( )) (8) () () () z k x k x k ( ) 0.5 ( ) 0.5 ( ). Step. Form data matrix B and data vector YN: III. Sytem Deign RFID Sytem Deign RFID i a non-contact automatic identification ytem. A RFID reader with. GHz frequency and active tag are ued in thi tudy. Figure 5 how the curve of meaured ignal trength veru ditance of the RFID ytem. The meaured data are illutrated a circle. A curve fitting Equation () i hown in olid line a follow: z B z z () () () () (3), () y N x x x Step 5. Solve model parameter: a b T B B T B yn () (3) () Step 6. Form Grey prediction model: (9) x 0.05 x RSS 0 To olve Equation ():. () (9.903 RSS) x 570. (5) Thi Equation (5) i the relation of ignal trength and ditance in the program. Navigation Sytem Deign b b a a () () () xˆ ( k ) xˆ ( k ) xˆ ( k) () () ak xˆ ( k ) x () e Due to the environment and the limitation of RFID, the ignal trength of RFID will be affected. In undertaking our experiment, we propoed three rule baed on ignal trength data for analyi: Step 7. Retore the demand prediction value: ˆ a b X ( k ) ( e ) X () e a ak where ^ denote a Grey prediction value. (3) Rule : Signal trength change uddenly: When the lope of the curve i larger, i.e., the ignal trength rate i higher, and the ignal trength hould be averaged, we will take the mean of the pat five data a the referred ignal trength ausmt Vol.. (0)

5 Albert Wen-Long Yao, Hin-Te Liao, and Shiou-De Chen Rule : Signal trength decreae dramatically: When the ignal trength decreae dramatically, the robot may go atray. The ytem hould be retarted for a new route. Rule 3: Signal trength increae dramatically: When the ignal trength increae dramatically, it implie that the robot i approaching the target detination. It hould top rotating. The mechanim i a follow in Figure 6. Start Target ( x, y ) 3 Target 3 ( x, y) 5 5 Tranmitter Target ( a3, b3) Target 3 temp temp or NewRSSI NewRSSI Ye NewRSSI 5 i NewRSSI 5 i 0 ( x, y ) ( x0, y0) ( x, y) Figure 7. Robot navigation diagram. ( x, y ) 3 3 Target RSSI RSSI RSSI 3 RSSI > End Figure 6. Mechanim for IGRNS. Ye Ye Grey Prediction Controller Deign Angel Of Rotation =GM(Angel Of Rotation) Angel Of Rotation =0 A Grey prediction controller will ait the robot to optimize it navigation rout and horten it path to the target. The robot tep of movement are hown in Table, and it navigation diagram i hown in Figure 7. The ignal trength and ditance converion are calculated by Equation (5). The explanation i a follow: Step : From tart point, robot initial coordinate i ( x0, y 0) with meaured ignal trength RSS0. Let the rotation angle be 0 0. The robot doen t rotate, and the aumed target i ( a, b ). Robot will move a ditance S. Step : The robot i at a new location ( x, y ), with meaured ignal trength RSS. With RSS0, RSS, and S, the new target ( a, b) can be calculated by the coine theorem. It i olved to get ( r, ) of ( x, y ) to ( a, b ). The robot will rotate angle and move S ditance to location ( x, y ). Step 3: The robot i at new location ( x, y ), with meaured ignal trength RSS. With RSS0, RSS, RSS, and S, the new target ( a3, b 3) can be calculated. It i olved to get ( r, ) from ( x, y ) to ( a3, b 3). The robot will rotate angle and move S ditance to location ( x 3, y 3 ). Step : The robot i at new location ( x3, y 3), with meaured ignal trength RSS3. With RSS, RSS, RSS3, and S, the new target ( a, b) can be calculated. It i olved to get ( r3, 3) from ( x3, y 3) to ( a, b ). From thi tep, the algorithm will check the condition RSS 0 < RSS < RSS < RSS 3, which mean the robot i on the right track toward the target, to tart the Grey prediction mechanim. If the condition are met, the Grey prediction mechanim will be tarted ausmt Vol.. (0)

6 A vel Grey-RSS Navigation Sytem Deign for Mobile Robot Table. Step for Grey controller. Robot coordinate RSS Target Direction Prediction Coordinate Prediction Direction ( x0, y 0) RSS 0 ( a, b ) ( r0, 0) ( x, y ) RSS ( a, b ) ( r, ) - - ( x, y ) RSS ( a3, b 3) ( r, ) - - Rotation Angle ( x3, y 3) RSS 3 ( a, b ) ( r3, 3) ( x, y ) ( r, ) 3 : : : : : : : Figure 8. Simulation reult with RSS algorithm. Figure 9. Simulation reult with IGRNS algorithm ausmt Vol.. (0)

7 Albert Wen-Long Yao, Hin-Te Liao, and Shiou-De Chen When RSS 0 < RSS < RSS < RSS 3, the program will calculate the next point ( x, y ) from ( x3, y 3) and 3. However, the Grey controller will predict the next angle according to the input of 0, and 3. With the predicted angle and location ( x, y ), the next point ( x5, y5 ) can be obtained. If the robot move from ( x3, y 3) to ( x5, y5 ) with adjuted angle 3, it will reduce the robot unneceary rotating. The Grey controller will repeat Step a long a the ignal trength continually increaed. Otherwie, it will follow the RSS algorithm to guide the robot to the target. IV. Simulation and Reult The imulation program with GUI wa developed in Window API (Application Programming Interface) and VB 6.0 to evaluate the performance of the traditional RSS navigation algorithm and novel IGRNS algorithm. In the imulation, the RSS value wa the real ignal trength (60) with random diturbance with tolerance ±, ±, and ±3. The unit troke of robot wa 0 cm in the imulation experiment. From the GUI, the RSS/IGRNS mode, target location, and diturbance level could eaily be elected or entered (ee Figure 8 and 9). Figure 8 and 9 how the imulation reult of RSS and IGRNS algorithm with target ditance 00 cm and 60 angle. The robot took 9 tep to reach the target with RSS algorithm, uing 6 tep to adjut it direction. On the other hand, the robot only took tep to reach the target with IGRNS algorithm. Table how the experimental reult of RSS algorithm and IGRNS algorithm with different rotation angle and diturbance level. It indicate that performance of IGRNS i uperior to that of the RSS. Table. Experiment reult of IGRNS v. RSS. ± ± ±3 RSS IGRNS RSS IGRNS RSS IGRNS Avg Improve.70% 3.% 9.7% V. Concluion optical encoder-baed navigation. With the intelligence of Grey prediction, the IGRNS can reduce a robot unneceary rotation in order to locate the target detination. With IGRNS, the mobile robot take fewer tep to reach a target quickly. The performance of IGRNS i better than that of RSS navigation ytem by.70%, 3.%, and 9.7% under diturbance level of ±, ±, and ±3 repectively. Acknowledgement The author thank National Science Council for partial upport under the Grant no. (NSC-00--E ). Reference [] C. T. Yu, "Obtacle avoiding device for car-like mobile robot with fuzzy theorem realized by digital ignal proceor," M.S. Thei, Intitute of Automatic Control Engineering, Feng Chia Univerity, Taichung, Taiwan, 005. [] J. H. Cheng, "Spatial path tracking ytem deign for an omnidirectional platform uing optical flow enor," M.S. Thei, Intitute of Electrical Control Engineering, National Chiao Tung Univerity, Hinchu, Taiwan, 006. [3] P. Bahl and V. N. Padmanabhan, "Radar: An in-building RF-baed uer location and tracking ytem," in Nineteenth Annual Joint Conference of the IEEE Computer and Communication Societie (INFOCOM 000), Tel Aviv, Irael, 000, pp vol vol.77. doi: 0.09/INFCOM [] L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, "LANDMARC: Indoor location ening uing active RFID," Wirele Network, vol. 0, no. 6, pp , 00. doi: 0.03/B:WINE dd [5] E. E. L. Lau and W. Y. Chung, "Enhanced RSSI-baed real-time uer location tracking ytem for indoor and outdoor environment," in International Conference on Convergence Information Technology, Gyeongju, Korea, 007, pp doi: 0.09/ICCIT [6] J. L. Deng, "Introduction to grey ytem theory," The Journal of Grey Sytem, vol., no., pp. -, 989. A novel IGRNS algorithm wa preented in thi paper. The imulation reult how that the propoed IGRNS algorithm can eliminate the cumulative error of 69 ausmt Vol.. (0)