Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 Sensors & Transducers 14 by IFSA Publshng, S. L. http://www.sensorsportal.com Pattern Recognton for Robotc Fsh Swmmng Gats Based on Artfcal Lateral Lne System and Subtractve Clusterng Algorthms 1 Hongl Lu, Kun Zhong, Yatng Fu, 3 Guangmng Xe, 1 Qxn Zhu 1 School of Mechancal Engneerng, Suzhou Unversty of Scence and Technology, Suzhou, 159, Chna School of Electrcal and Electronc Engneerng, East Chna Jaotong Unversty, Nanchang, 3313, Chna 3 College of Engneerng, Pekng Unversty, Bejng, 1871, Chna 1 Tel.: (+86) 153653913, fax: (+86)5168498 Tel.: (+86) 189116937, fax: (+86)5168498 3 Tel.: (+86) 11369359865, fax: (+86)1675181 E-mal: luhl_sz@163.com, kunsty719@163.com, fuyatng13@163.com, xegmng@mech.pku.edu.cn, bob1cn@163.com Receved: 6 August 14 /Accepted: 3 October 14 /Publshed: 3 November 14 Abstract: The complcated and changeable underwater envronment ncreases the dffculty of pattern recognton for robotc fsh swmmng gats. Amng at ths queston, envronment sensng and pattern recognton usng an artfcal lateral system are nvestgated n ths work. Imtatng lateral lne of real fsh n nature, a novel artfcal lateral lne system for robotc fsh s desgned n ths paper. Based on ths novel system, the pressure nformaton around robotc fsh can be sensed when robotc fsh swms n dfferent gats, so the feature ponts can be extracted from the pressure nformaton. And then, based on the feature ponts, a subtractve clusterng algorthm s used to recognze the swmmng gats of robotc fsh. So the pattern state of robotc fsh can be obtaned, whch provdes a bass for the quck control of robotc fsh n water. Fnally, a valdaton experment s conducted wth freely swmmng robotc fsh. The valdty of ths novel system s demonstrated. And the feasblty and accuracy of subtractve clusterng algorthm used n pattern recognton for robotc fsh s verfed too. Copyrght 14 IFSA Publshng, S. L. Keywords: Artfcal lateral lne system, Robotc fsh, Pressure sensng, Pattern recognton. 1. Introducton The realzaton of tradtonal pattern recognton for robotc fsh swmmng gats s usually based ob machne vson and mage processng technology. The above method excessvely depends on the mage capture by camera. When robotc fsh swms n deep sea or dark nght, the accuracy of pattern recognton cannot be guaranteed for there s lttle lght can be used for mage capture. So, the nvestgaton of the novel artfcal lateral lne system (ALLS) s a very mportant work. The fsh lateral lne system s a unque sense organ of fsh and amphbans, helpng to detect hydrodynamc nformaton around. It enables fsh to accomplsh a varety of underwater actvtes, such as http://www.sensorsportal.com/html/digest/p_56.htm 7
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 localzaton of movng prey detecton and capture [1-3], detecton of statonary objects, obstacles avodng, schoolng [4], rheotaxs [5] and socal communcaton [6-8]. In addton, the lateral lne system of fsh feels the stmulaton of water around through the sensory nerve cells, so they can help fshes to response to the envronment quckly. For ths reason, based on ths bologcal functon and mechansm, researcher expect to desgn a bommetc ALLS for robotc fsh to sense the surroundng envronmental nformaton and make prompt reacton on the hydrodynamc change. In recent years, wth the further development of sensng technology, massve flow sensors are desgned to sense the hydrodynamc nformaton [9, 1]. In [11], pressure sensor s used to estmate the hydrodynamc force acted on underwater vehcle and an estmaton model s bult, so that maneuverng accuracy for envronmental montorng can be mproved. A pressure sensor array s dstrbuted parallelly on a rgd body, usng whch the dfference between unform flows and Karman vortex streets can be dscrmnated and the hydrodynamc features, such as vortex sheddng frequency, vortex travellng speed and downstream dstance between vortces and the wavelength, can be computed n [1]. What s more, T. Salumäe and M. Kruusmaa dd researches on an ALLS formed by pressure sensors array usng n an underwater vehcles for detectng hydrodynamc regmens and for controllng the robot s moton wth respect to the flow [13]. These researches usually used the sensng system to sense the hydrodynamc nformaton, the drecton and the speed of flow around, whch can promote the development of flud mechancs study. However, the sensors n those ALLSs above are mostly fxed on a column, whch contans some defects such as smple structure, low dstrbuton regularty, weak n bommetc, and t s dffcult to mmc the sensng ablty of real fsh. In addton, the lateral lne research normally apples on flow sense, and there s lttle research on the pattern recognton for the status of robotc fsh tself. Thus, wthout ths accuracy status nformaton from outsde, t s dffcult to complete the tasks for robotc fsh quckly. To resolve ths problem above, n ths paper, we absorbed the dstrbuton feature of real fsh n nature, and desgned a novel ALLS usng pressure sensors array for robotc fsh. By applyng ths system, the pressure nformaton around robotc fsh can be sensed, and provde a bass for pattern recognton of robotc fsh swmmng gats. The applcaton of subtractve clusterng algorthm n pattern recognton s very wdespread. Subtractve clusterng algorthm s a ratonal means to dvde the nput and output data, quckly mplementng exact and effectve classfcaton for massve databases. At the same tme, subtractve clusterng algorthm s able to generate a more structured class set, and the clusterng results are completely ndependent of sequence ntal arrangement or nput order, also that has nothng to do wth the order of clusterng process. An effcent technque for mnng web usage profles based on subtractve clusterng that scales to large datasets n proposed n [14]. Data classfcaton can be mplemented wthout the dependence of nput parameters. Paper [15] developed a new two-phase clusterng algorthm and appled t to sense generaton of abbrevatons n clncal text, whch has a result of 85 % senses on average. Subtractve clusterng algorthm consders every data pont as possble cluster center, and t overcomes the calculaton problems of other clusterng algorthms. And the result of the subtractve clusterng s not assocated wth data dmensons. Paper [16] developed a control dynamc model to capture the moton of the hgh-speed electrcal multple unts (EMU) and then uses t to desgn a desrable speed trackng controller for EMU. Subtractve clusterng algorthm s exploted to model the runnng process and the modelng accuracy s greatly mproved. The man contrbutons of ths paper are as follows. 1) Imtatng lateral lne of real fsh n nature, a novel artfcal lateral lne system s desgned and s used n the self-developed robotc fsh. ) The ALLS s used to sense the pressure nformaton around robotc fsh n dfferent swmmng gats. And then, usng the pressure nformaton, a subtractve clusterng algorthm s proposed to recognze the swmmng gats of robotc fsh and obtan the pattern state of robotc fsh, whch provdes a bass for the quck control of robotc fsh. The remander of ths paper s organzed as follows. Secton descrbes the robotc fsh and the novel ALLS. The pressure feature around the robot n dfferent swmmng gat obtaned by ALLS s gven n Secton 3. Then, the subtractve clusterng algorthm s presented to recognze the swmmng gats n Secton 4. Secton 5 shows the result of valdaton experment. Fnally, Secton 6 summarzed ths paper.. The Robotc Fsh and Novel Artfcal Lateral Lne System.1. The Robotc Fsh The fsh robot mmcs the geometry and swmmng mode of an ostracform boxfsh (as n Fg. 1). Fg. 1(c) shows mechancal confguratons of the boxfsh robot, whch conssts of a well-streamlned man body, two degree-offreedom propulsve unt of pared pectoral fns and one degree-of-freedom propulsve unt of caudal fn. The robotc fsh mounts ALLS, machne vson system, nerta measurement unt, nfrared measurement unt, helpng to acqure a good sensng percepton of the external envronment. The robotc fsh uses ARM 11 as man controller whch contans a lnux operatng system. The ARM processor has a 8
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 strong calculatng capacty, helpng to analyss the pressure nformaton quckly... Novel Artfcal Lateral Lne System In order to make the robotc fsh have the pressure sensng ablty lke real fsh, we absorbed the dstrbuton feature of real fsh n nature, and desgned a novel ALLS usng pressure sensors array for robotc fsh. The dstrbuton of sensng unt manly draws lessons from the dstrbuton feature n boxfsh, completely complyng wth boncs characterstc, so the system can obtan the flow nformaton more effcently and accurately. The dstrbutons of lateral lne n boxfsh and robotc fsh are shown as Fg.. Fg. 1. (a) boxfsh n nature, (b) fns dstrbuton of boxfsh, (c) bonc robotc fsh. Robotc fsh swms by the thrust generated from pectoral fn and caudal fn. The servomotor creates vbratons so drve the fn swng. As snusodal sgnal can create smooth vbratons and permt adjustment of the control parameter n a flexble way, so a snusodal sgnal s chosen as the control sgnal to motvate the servomotor. A central pattern generator (CPG) can generate coordnatve and perodc vbratons wth a smple nput. Therefore, CPG model s used n ths paper to make the system return to a steady state rapdly even t subjects to small transent perturbatons. The moton equatons of harmonc oscllaton for robotc fsh are equaton (1) to (4). N x = ββ ( ( X x) x ), (1) α = α( α( A α ) x α ), () φ = μ ( μ α ( φ φ ) ( φ π f )), (3) j j j j j= 1, j θ = x + α cos( φ ), (4) where x, α and φ are the state varables representng offset, ampltude, and phase of the th oscllator, and varable θ s ts output. The value of x, α and φ s determned by equaton (1), () and f, X and A are (3) respectvely. The parameter control parameters for the desred frequency, offset and ampltude of oscllatons. The thrust generated from pectoral fn and caudal fn s determned by ampltude α and frequency f, and offset x changes the drecton of thrust, and phase dfference s used to couple the moton of varous jonts and to express the dfferent swmmng models of robotc fsh. Fg.. (a) The black dotted lne s the dstrbuton of boxfsh lateral lne (cted from [17]) (b) Red lne s the dstrbuton of robotc fsh lateral lne. The novel ALLS s composed of 9 pressure sensors array, and they are dstrbuted symmetrcally along each sde of the robotc fsh: P1, P and P3 locate n front of the head of the robot; P4, P5 and P6 le n the left sde of body; P7, P8 and P9 le n the rght sde of body, shown as Fg. 3. The pressure sensor s welded onto a prnted crcut board and embedded n the body of robot. These sensors buld the core unt of ALLS, and can sense the flow nformaton and hydrodynamc feature, whch provde a new way for underwater nformaton-obtanng. CPS131 s a mnature hgh-senstvty, low-nose, hgh-lnearty, IIC supported sensor and s selected n ths system. The measurng range of the sensor s 1 KPa and the senstvty s.15 Pa wth a 4-bt dgtal sgnal. The sensors are connected to a mnature ARM7 computer. The ARM7 processor collects the pressure sgnal from IIC bus and sends the sgnal to the man controller ARM11. All the hgher level control and processng s mplemented by ARM11 processor, and ts hgh performance 9
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 makes sure the well compute and control for the robotc fsh. a rudder. By controllng the pectoral fn and caudal fn n dfferent oscllatng state, the robotc fsh swms n dfferent swmmng gats: forward swmmng, turnng left, turnng rght, swmmng ascendng and swmmng dvng. Fg. 4 shows the fve swmmng gats and the correspondng oscllatng state of fns. It s a very complcated physcal model that fsh swms n water, whch nvolves statcs, dynamc, mechancs, and other dscplne. At the same tme, hydrodynamc forces actng on the robotc fsh are segregated nto added mass terms, vscous dampng terms, and dsturbance terms from the non-statc background flow. Based on the feature of hydromechancs, the ALLS can sense the hydrodynamc nformaton around the robot when t swms n dfferent swmmng gats. Fg. 3. Locaton of pressure sensors n robotc fsh. 3. Sensng the Pressure Feature Around the Robotc Fsh n Dfferent Swmmng Gats Based on ALLS Fsh swms n water through coordnate moton of body and tal, and t s world famous for excellent propulsve effcency and extraordnary flexblty. Robotc fsh swms by the thrust generated from pectoral fn and caudal fn. The two pectoral fns provde thrust for multmodal swmmng whle the caudal fn can acqure hgh swmmng speed or act as 3.1. The Pressure Varatons around the Robot under the State of Forward Swmmng Gat When the robotc fsh swms n a forward swmmng gat, the ALLS sense the pressure nformaton and transport t to upper platforms for data analyss. Fg. 5 shows the data nformaton of 9 sensors when n forward swmmng gat, where P1 denotes the frst sensor, smlarly to others. From the fgure we know that the sgnal fluctuaton shows the force change around the robot. As the sensors n front of the head are deeper n water and are face to flow drecton, the pressure s bgger than other sensors. Fg. 4. Illustratons of typcal swmmng gats of the robot: (a) forward swmmng, (b) turnng left, (c) turnng rght, (d) swmmng ascendng, (e) swmmng dvng. 1
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 P1 P 5 P3 15 1 18 16 14 1 15 5 5 1 15 5 1 5 1 15 5 1 5 1 15 5 P4 8 P5 15 P6 1 6 1-1 4 5 - -3 5 1 15 5-5 1 15 5-5 5 1 15 5 18 P7 15 P8 5 P9 16 14 1 1 15 1 1 8 5 1 15 5 5 5 1 15 5 5 5 1 15 5 Fg. 5. The pressure varaton under the state of forward swmmng gat. 3.. The Pressure Varatons around the Robot under the State of Turnng Left and Turnng Rght Gat When the robot swms n turnng gat, the length of movng path s dfferent between two sdes of the robot: turn left, movng path n rght sde s longer than left sde; turn rght, movng path n left sde s longer than rght sde. So there s a dfferent feature n two sde sensors, shown as Fg. 6. In ths fgure, P4~P6 represents the sensors located n the left sde of body, marked n red lne; P7~P9 denoted the sensors located n the rght sde of body, marked n blue lne. These three pctures n the frst row are the pressure varatons around the body of the robot when turns left; the three pctures n the second row are the pressure varatons around the body of the robot when turns rght. From Fg. 6 we know that when the robot turns left, the pressure n the rght sde s greater than the left sde, and smlarly, when turns rght, the pressure n the left sde s greater than the rght sde. 5 turn left P4 & P7 5 turn left P5 & P8 5 turn left P6 & P9 15 15 15 1 1 5 1 5 5 1 3 4 5-5 1 3 4 5-5 1 3 4 5 5 turn rght P4 & P7 5 turn rght P5 & P8 turn rght P6 & P9 15 15 15 1 1 1 5 5 5-5 1 3 4 5-5 1 3 4 5-1 1 3 4 5 Fg. 6. The pressure varaton of each sde of body when the robot turns. 11
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 3.3. The Pressure Varatons around the Robot under the State of Ascendng and Dvng Gat When the robotc fsh ascends or dves, the depth of the robot would change, whch result n the pressure varaton along wth the depth change. Fg. 7 shows the pressure varatons of each pressure sensor when the robot n the state of ascendng and dvng gat. In the Fg. 7, red lne denotes the pressure varatons when the robotc fsh swms dvng, and the pressure ncreases wth the ncrease of the depth. In addton, the blue lne represents pressure varatons when the robotc fsh swms ascendng, and the pressure decreases along wth the reducng of the depth. 3 P1 3 P 3 P3 1 1 1 5 1 15 5 1 15 5 1 15 3 P4 3 P5 3 P6 1 1 1 5 1 15 5 1 15 5 1 15 3 P7 3 P8 3 P9 1 1 1 5 1 15 5 1 15 5 1 15 Fg. 7. The pressure varaton under the state of ascendng and dvng gat. By analyzng the pressure nformaton above, ALLS s able to measure and present the hydrodynamc pressure nformaton when the robot s under the state of dfferent swmmng gats. The ALLS based on pressure sensors can sense the flow nformaton effectvely, whch fully explaned the valdty and reasonableness of the desgn of ths system, and provdes a new way for underwater flow nformaton obtanng. 4. Moton Pattern Recognton of Robotc Fsh Based on Subtractve Clusterng Algorthms Subtractve clusterng algorthm s a fast algorthm for estmatng the number of clusters and cluster centers. It s an effectve means to dvde the nput and output space and s wdely used n pattern recognton feld. Based on the pressure data of robotc fsh wth dfferent swmmng gat obtaned by the ALLS, we explot subtractve clusterng algorthm to estmate the cluster centers of pressure data wth dfferent swmmng gat. And then we effectvely dvde the pressure data to determne the swmmng gats of the robotc fsh. Wth n groups of pressure data M = M,..., M,..., M (where, data pont { 1 n} {,,...,,,,..., } M = P1 P P9 Pr1 Pr Pr ) obtaned from 9 Secton 3, the subtractve clusterng of robotc fsh s carred out as follows. 4.1. Subtractve Clusterng 1) Set ntal parameters, set an effectve neghborhood radus of a clusterng center ra = r, mn and choose a step ncrement ε, and ε > ; ) Calculate the densty crteron of each data pont M ( = 1,..., n) n M M j C = exp, (5) j= 1 ( ra /) 3) Fnd out the max densty crteron Cmax1 = max C, the correspondng data pont can be confrmed as the frst clusterng center c M = M 1 maxc 1
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 4) Choose r b = λr a, use equaton (6) to adjust the densty crteron c M M1 C = C Cmax1 exp, (6) ( rb /) And fnd the maxmum densty crteron C max j, so the correspondng data pont s chosen as the new cluster center. Among the equaton, r s a b neghborhood radus wth a smaller densty crteron, t s a postve constant. C C max j max j 1 < θ, (7) Judge equaton (7), f t s not rght, go to Step 5) to contnue to choose clusterng center. If t s rght, then ext the procedure. 6) Then f cluster centers can be obtaned, and f < n. Ths paper uses the above steps 1)~6) to dvde the pressure data of robotc fsh wth 5 swmmng gats. In each swmmng gat, f cluster centers can be obtaned. So we can obtan 5 f cluster centers. (Forward swmmng gat: cluster centers 1~ f ; Turnng left gat: cluster centers f + 1~f ; Turnng rght gat: cluster centers f + 1~3f ; Swmmng ascendng gat: cluster centers 3f + 1~ 4f ; Swmmng dvng gat: cluster centers 4f + 1~5f ). 5. Experments 5.1. The Determnaton of Cluster Center Ths secton presents a valdaton experment wth the self-developed swmmng robotc fsh to verfy the valdty of the proposed method. Fg. 8 shows robotc fsh swms n a pattern of number 8. Frstly, we adopt the ALLS (9 sensors) to obtan the pressure data and collect 3 groups of the pressure data t t t t M = M, M,, M ( t = 1,,...,5 ) of fve { 1 3} swmmng gats respectvely. In each swmmng gat, the data are equally dstrbuted n tme. Secondly, we use the subtractve clusterng algorthm n subsecton 4.1 to calculate the cluster centers for 3 groups of data n each gat. In order to confrm the optmal number f of cluster centers, we compare the accuracy of pattern recognton n dfferent number of cluster centers. Settng the turn of swmmng gats of the robotc fsh as follows, turnng rght- forward swmmng- turnng left- swmmng dvng- swmmng ascendng. Fg. 9 shows the onlne pattern recognton process when f = 5, 6, 7,8. Fg. 1 shows the accuracy curve of pattern recognton wth 5 swmmng gats when f changes. (In Fg. 9 and Fg. 1, SD denotes Swmmng Dvng; SA denotes Swmmng Ascendng; TR denotes Turnng Rght; TL denotes Turnng Left; FS denotes Forward Swmmng). 4.. Classfcaton In order to classfy the pressure data nto correspondng swmmng gat whch s the pattern recognton for robotc fsh swmmng gats, the membershp functons u pq of are defned by 1 μ pq = 5 f c p q M M c k= 1 M p Mk ( p 1,..., lq ; 1,...,5 f) = =, (8) Fg. 8. The robot swms n a pattern of number 8. where l s the number of pressure data. The membershp functons of each swmmng gat are obtaned by equaton (8), and then we should put the pressure data nto the correspondng swmmng gat. The ndex functon s defned by L = arg max{ μ pq }, (1) where L s the nearest cluster center of the th pressure data. We can fnd the sequence number of swmmng gat for the th pressure data by confrmng the class number of L. Fg. 9 shows the results of pattern recognton based on subtractve clusterng algorthm. When settng f = 5, the accuracy rates of each swmmng gats are not hgh enough. We can see that some turnng rght gats are recognzed as turnng left gats and some swmmng dvng gats are recognzed as swmmng ascendng gats or turnng rght gats. Wth the number of f ncreases, the accuracy rates ncreases too. Fg. 1 further shows that the accuracy rates ncrease wth the ncrease of the number of cluster centers. When f = 7, we can obtan the optmal accuracy rates. The accuracy rates of 13
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 5 swmmng gats respectvely are as follows. forward swmmng gat: 98 %; turnng left gat: 98 %; turnng rght gat: 96 %; swmmng ascendng gat: 96 %; swmmng dvng gat: 96 %. But when f = 8, the accuracy rates have a downward trend, the accuracy rate of forward swmmng gat decreases to 94 %. Therefore, we confrm the optmal number of f as 7. The cluster centers data of turnng rght gat are lsted n Table 1. Pattern Recognton (f=5) Pattern Recognton (f=6) 5 3 4 ndex functon L 15 1 ndex functon L 18 1 5 6 5 1 15 5 1 15 (a) (b) 35 Pattern Recognton (f=7) 4 Pattern Recognton (f=8) 8 3 ndex functon L 1 14 ndex functon L 4 16 7 8 5 1 15 5 1 15 (c) (d) Fg. 9. Fgures (a), (b), (c), (d) represent the onlne recognton process when f = 5,6,7,8. Table 1. The cluster centers data of robotc fsh wth turnng rght gat. The cluster centers number Cluster center 1 Cluster center Cluster center 3 Cluster center 4 Cluster center 5 Cluster center 6 Cluster center 7 Cluster centers data P 189.5 1.1 14. 15 48.1 43.9 115.4 87.9 79.6 Pr -7.6-16.4-131.6-49.6 74.4 16.4 6.4-38.4-318.4 P 63. 166. 178.5 146.9 76.9 34.4 63. 6.4 14.4 Pr 1.8 16.4-44 -164.8-16.4 14.8 137. 55. -38.4 P 166. 195 155. 133. 136 7 115.4 85.7-39.8 Pr -11. -38.4 14.8 14.4-137.6-181. -17.4-11.6 41.6 P 16.7 199.1 155. 97.5 116.7 89.3 115.4 38.5 8. Pr -55. -93. -44 13 38.4-14.4-11. -8.4-14.8 P 131.8 153.8 159.3 159.3 141.4 85.1 8.4 9.8 3.3 Pr -74.4 49.6 76.8-49.6-58 -3. -76-19 34 P 144. 58. 6.9 3.8 11.6-9.6 115.4 56.3 76.9 Pr 14.4 6.4 44-131.6-98.8 159. 1.8 17.4 6.4 P 19.9 173 16 139.3 114 6.4 9.6 95.1-39.8 Pr 164.8 164.8 8.8 38.4-164.8-181. -6.4-178 538.4 14
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 accuracy (%) 1 9 8 7 6 5 FS TL TR SA SD algorthm. It can be appled n the pattern recognton of actual robotc fsh. And the curves of pressure P, P 4, P 7 durng the recognton process meet the pressure change rules n Fg. 5, Fg. 6 and Fg. 7, whch shows the effectveness of the proposed valdaton experment. 1 P 4 3 1 3 4 5 6 7 8 f Fg. 1. The accuracy curve of pattern recognton wth 5 swmmng gats when f changes. pressure 5.. Verfcaton Process In order to further verfy the accuracy rate of the pattern recognton based on subtractve clusterng algorthm when f = 7, ths paper randomly sets the swmmng gats of robotc fsh (We change the swmmng gats every 1 seconds, set the turn of the swmmng gats as follows, swmmng dvngswmmng ascendng- turnng left- forward swmmng- turnng rght- turnng left- turnng rght). The verfcaton process of pattern recognton s shown n Fg. 11, the accuracy rates of 5 swmmng gats are lsted n Table. And Fg. 1 lsts the curves of pressure P, P 4, P 7 durng the recognton process. pressure 3-5 1 4 8 1 16 4 8 (a) P4 P7 35 Pattern Recognton (f=7) 3 ndex functon L 8 1 14 4 8 1 16 4 8 (b) Fg. 1. (a), (b) The curves of pressure, durng the recognton process. 7 6. Conclusons 4 8 1 16 4 8 Fg. 11. The verfcaton process of pattern recognton wth 5 swmmng gats. In Fg. 11 and Fg. 1, the accuracy rates of 5 swmmng gats are hgher than 9 % when f = 7, whch shows the effectve generalzaton ablty of pattern recognton based on subtractve clusterng Amng at the exstng problem of pattern recognton for robotc fsh swmmng gats, a novel ALLS for pressure sensng around robot s desgned when robotc fsh swms n dfferent gats. The subtractve clusterng algorthm s ntroduced to recognze the swmmng gats of robotc fsh, and the pattern state of robotc fsh can be obtaned successfully, whch provdes a bass for quck control of robotc fsh. The Envronment percepton and envronmental cognton technology of robotc fsh wll be nvestgated n the future. 15
Sensors & Transducers, Vol. 18, Issue 11, November 14, pp. 7-16 The number of cluster centers Table. The accuracy rates of 5 swmmng gats n valdaton process. The swmmng gats of robotc fsh Forward Swmmng Turnng left Turnng rght swmmng ascendng 7 95 % 91.5 % 91.5 % 1 % 95 % Swmmng dvng Acknowledgements Ths work was partly supported by Natonal Nature Scence Foundaton of Chna (6116414, 5137533) and Qng Lan Project of Jangsu Provnce, Chna. References [1]. J. C. Montgomery, J. A. Macdonald, Sensory tunng of lateral lne receptors n Antarctc fsh to the movements of planktonc prey, Scence, Vol. 35, Issue 4785, 1987, pp. 195-196. []. K. Pohlmann, J. Atema, T. Brethaupt, The mportance of the lateral lne n nocturnal predaton of pscvorous catfsh, The Journal of Expermental Bology, Vol. 7, Issue 17, 4, pp. 971-978. [3]. S. Coombs, C. B. Braun, B. Donovan, The orentng response of Lake Mchgan mottled sculpn s medated by canal neuromasts, The Journal of Expermental Bology, Vol. 4, Issue, 1, pp. 337-348. [4]. T. J. Ptcher, B. L. Partrdge, C. S. Wardle, A blnd fsh can school, Scence, Vol. 194, Issue 468, 1976, pp. 963-965. [5]. J. C. Montgomery, C. F. Baker, A. G. Carton, The lateral lne can medate rheotaxs n fsh, Nature, Vol. 389, Issue 6654, 1997, pp. 96-963. [6]. J. F. Webb, Gross morphology and evoluton of the mechanoreceptve lateral-lne system n teleost fshes, Bran Behavor and Evoluton, Vol. 33, No. 1, 1989, pp. 34-43. [7]. D. Bodanck, G. R. Northcutt, Electrorecepton n lampreys: evdence that the earlest vertebrates were electroreceptve, Scence, Vol. 1, Issue 4493, 1981, pp. 465-467. [8]. A. G. Melssa, Lateral lne receptors: where do they come from developmentally and where s out research gong, Bran Behavor and Evoluton, Vol. 64, Issue 3, 4, pp. 163-181. [9]. P. Lu, R. Zhu, R. Que, A flexble flow sensor system and ts characterstcs for flud Mechancs measurements, Sensors, Vol. 9, Issue 1, 9, pp. 9533-9543. [1]. N. N. Chen, C. Tucker, et al, Desgn and characterzaton of artfcal harcell sensor for flow sensng wth ultrahgh velocty and angular senstvty, Journal of Mcroelectromechancal Systems, Vol. 16, Issue 5, 7, pp. 999-114. [11]. Y. M. Xu, E. Stegman, B. Hodgknson, K. Mohsen, Lateral lne nspred hydrodynamc force estmaton for autonomous underwater vehcle control, n Proceedngs of the IEEE Conference on Decson and Control, 13, pp. 6156-6161. [1]. R. Venturell, O. Akanyet, F. Vsentn, et al, Hydrodynamc pressure sensng wth an artfcal lateral lne n steady and unsteady flows, Bonspraton & Bommetcs, Vol. 7, Issue 3, 1, Artcle ID 364. [13]. T. Salumäe, M. Kruusmaa, Flow-relatve control of an underwater robot, Proceedngs of the Royal Socety, Vol. 469, Issue 15, 13, Artcle ID 1671. [14]. B. S. Suryavansh, Nematollaah Shr, Sudhr P. Mudur, An effcent technque for mnng usage profles usng relatonal fuzzy subtractve clusterng, n Proceedngs of the Internatonal Workshop on Challenges n Web Informaton Retreval and Integraton, 5, pp. 3-9. [15]. H. Xu, Y. H. Wu, N. Elhadad, et al, A new clusterng method for detectng rare senses of abbrevatons n clncal notes, Journal of Bomedcal Informatcs, Vol. 45, Issue 6, 1, pp. 175-183. [16]. H. Yang, Y.-T. Fu, K.-P. Zhang, Z.-Q. L, Speed trackng control usng an ANFIS model for hghspeed electrc multple unt, Control Engneerng Practce, Vol. 3, Issue 1, 14, pp. 57-65. [17]. M. Nakae, K. Sasak, The lateral lne system and ts nnervaton n the boxfsh Ostracon mmaculatus (Tetraodontformes: Ostracdae): descrpton and comparsons wth other tetraodontform and percform condtons, Ichthyologcal Research, Vol. 5, Issue 4, 5, pp. 343-353. 14 Copyrght, Internatonal Frequency Sensor Assocaton (IFSA) Publshng, S. L. All rghts reserved. (http://www.sensorsportal.com) 16