Adaptive RFID Indoor Positioning Technology for Wheelchair Home Health Care Robot. T. C. Kuo

Transcription

1 Adaptve RFID Indoor Postonng Technology for Wheelchar Home Health Care Robot

2 Contents Abstract Introducton RFID Indoor Postonng Method Fuzzy Neural Netor System Expermental Result Concluson --

3 Abstract We propose an adaptve rado frequency dentfcaton (RFID ndoor postonng system technology for heelchar home health care robot th reless communcaton. The proposed RFID postonng system uses one reader and four tags hch s lo cost. The adaptve fuzzy neural netor ll adjust the envronmental parameters and revse the eght, the varance and the mean value to overcome effectvely the envronmental parameters varaton and receved sgnal strength ndcaton (RSSI) th uncertanty. --

4 Through the compensaton of the measurement error, the actual heelchar robot locaton-based applcaton can be performed to overcome the uncertan envronmental parameters. In our research or, the successful experment results sho that the proposed system archtecture and postonng system provdes very good accuracy and mae home health care heelchar robot postonng system avalable for navgaton and gudance. --

5 Introducton Many researchers have ored on automatc postonng for years. In order to resolve dfferent ndoor postonng problems, commonly used sensors nclude nfrared sensors, ultrasonc sensors, gyroscopes, accelerometers, rado frequency dentfcaton systems, laser range fnder, camera, and so on [-5]. The hardare strategy s dvded nto actve and passve. The applcaton of passve RFID technology s extensve. -5-

6 RFID Indoor Postonng Method The or ntends to support an on-gong research project n the Gerontechnology Research Center n Yuan Ze Unversty, Taan n developng an ntellgent robotc heelchar (RW) as shon n Fg.. Fg. The prototype of the RW. -6-

7 The concept of the RFID ndoor postonng method s depcted n Fg.. Fg. Locaton map ( reader and tags). -7-

8 Fg. shos the expermental floor on t every 50cm of dstance beng one unt. Fg. Expermental envronment as dvded nto 8 ponts for RSSI measurement. -8-

9 The expermental envronment s a 5 square meters space. There are totally 8 measurng ponts, as shon n Fg.. Fg. The measured RSSI values and the testng ponts. -9-

10 Fg. 5 s the schematc dagram of the RFID locaton sensng. Fg5. Illustraton of the operaton of RFID localzaton.. -0-

11 A-B ntersectons: W and W A B ntersecton W W W ( xa, y ) ( xa x ) + ( ya y ) ( x, y ) ( x x ) + ( y y ) b b b Consder the mult-path fadng and the shadong effect that lead to measurement error due to varyng RSSI values n the expermental envronment. We defned as be the eght beteen the reader and Tag, R R a b R scope mn () R max R. Let Rscope, (7) R ReaderR max --

12 -- ( ) n n y n n x y x A R R A A Reader,, (8) Suppose the target coordnates s A Target (, Target x y Target ), and the error s ( ) ( ) y y x x A -,Target A Target Error y x,e e (9)

13 Fuzzy Neural Netor System x y net net o 5 x 5 o x y net net x x j l net x x j l y exp ( net ) y net x j ( x m ) j ( σ ) j j y x ~ j ~ 5 ~ 5 o Fg6. The structure of the fuzzy neural netor. --

14 Defne the X-axs error be e x, and Y-axs error be e y. The error dfference e& x and e& y are selected to be nput varables. For every neuron n the layer, the net nput and the net output can be represented as x e, x e&, c x, y c c ( net ) x, y f, (0). () --

15 The Gaussan functon s adopted to be the membershp functon. The net output are y j In the rule layer, ( net ) exp ( ) x ( ) mj σ j fj j ( ) net x x j l,,,, K,5, y f, j, l,...,5,,...,5, The overall output s the summaton of all ncomng sgnals, j () () y o f o ( net ) o o x,,...,5,, o () -5-

16 defne a cost functon E as E ( ) (5) Y y The tunng rules are ( Y y ) x Δ η (6) j m j yj ( x ) mj ( σ ) Δm η δ (7) j j yj j ( x ) mj ( σ ) j Δσ η δ (8) σ -6-

17 Intally, FNN off-lne learns the envronment. Then the updated parameters are used for the ndoor localzaton. Tag Tag RSSI -80 RSSI Dstanc(m) 0 0 Tag 6 Dstanc(m) Dstanc(m) 0 0 Tag 6 Dstanc(m) RSSI -00 RSSI Dstanc(m) Dstanc(m) 8 0 Fg7. The D RSSI value curves of each tag Dstanc(m) Dstanc(m)

18 Expermental Result In the experment, the RFID components nclude one.ghz actve reader and four tags. The reader s setup on the heelchar robot. The envronment of the experment s an ndoor closed space th length and dth of 5 meters respectvely. We add bar antenna on RFID tags to reduce the drectonal nfluence. After addng the antenna, the average measurement error reduces to belo 0.7 meters, hle t as meters to.5 meters thout usng the antenna. -8-

19 The central regon n the room s the orst place here the measured error s the largest. Ths s because the RSSI values vary serously and lead to mscalculaton of the dstance beteen the reader and the tag. The FNN s used to adjust the eght, the varance, and the mean. Wth the parameter adjustment by usng the proposed FNN, the average error s reduced. -9-

20 Fg 8. The tracs of the heelchar robot. -0-

21 Concluson Ths paper presentes an adaptve RFID technology for ndoor postonng ssue th the use of fuzzy neural netor for heelchar home health care robots. The adaptve FNN technology for adjustng the postonng system parameters further ncreases the envronmental adaptablty and postonng accuracy. The proposed method can be easly appled to ntellgent robots. Expermental result shos that the localzaton error can be smaller than meters as long as the movng speed of the robots s not more than 0 meters per mnute. --