Swarm intelligence based dynamic obstacle avoidance for mobile robots under unknown environment using WSN

Transcription

1 J. Cent. South Un. Tehnol. (008) 15: DI: /s Swarm ntellgene based dynam obstale aodane for moble robots under unnown enronment usng WSN XUE Han( 薛晗 ), MA Hong-xu( 马宏绪 ) (College of Eletromehanal Engneerng and Automaton, Natonal Unersty of Defense Tehnology, Changsha , Chna) Abstrat: To sole dynam obstale aodane problem a noel algorthm was put forward wth the adantages of wreless sensor networ (WSN). In ew of mong eloty and dreton of both the obstales and robot a mathemat model was bult based on the exposure model, exposure dreton and rtal speeds of sensors. Ant olony optmzaton (AC) algorthm based on bon swarm ntellgene was used for soluton of the mult-objete optmzaton. Energy onsumpton and topology of the WSN were also dsussed. A pratal mplementaton wth real WSN and real moble robots were arred out. In enronment wth multple obstale the onergene ure of the shortest path length shows that as terate generaton grow the length of the shortest path dereases and fnally reahes a stable and optmal alue. Comparsons show that usng sensor nformaton fuson an greatly mproe the auray n omparson wth sngle sensor. The suessful path of robots wthout ollson aldates the effeny, stablty and auray of the proposed algorthm, whh s proed to be better than tradton genet algorthm (GA) for dynam obstale aodane n real tme. Key words: wreless sensor networ; dynam obstale aodane; moble robot; ant olony algorthm; swarm ntellgene; path plannng; nagaton 1 Introduton WSN s a noel networ tehnque ntegratng modern wreless ommunaton, mro sensor and omputer networ, whh has extense prospets and hgh applaton alue n defene, enronment superson, household automaton, transportaton and many other felds [1 3. WSN omprses of dfferent types of sensor nodes that o-operately perform tass le ollaborate sensng, enronment montorng, et. Most urrent path plannng methods largely omputed offlne are unsutable for applaton to a real-tme senaro wth dynam mong obstale whh s dfferent from pror enronment amongst stat obstales [4 6. Dynam obstale aodane s an extng researh feld and hot top of moble robot wth ts lose ln to sensor desgn, data fuson, map buldng, loalzaton, off-lne optmzed path plannng methods and nagaton methods. A number of solutons hae been proposed n the lterature to dynam obstale aodane problems. Most of the earlest obstale aodane methods were based on the potental feld approah. There hae been many other approahes that hae departed from the potental feld approah. The etor feld hstogram (VFH) method proposed by BENSTEIN and KEN [7 transformed a loal map nto a one-dmensonal dsretzed polar obstale densty funton. FX et al [8 searhed a spae of the robot s translatonal and rotatonal elotes. The TerraSout projet at Carnege Mellon Unersty norporated FAJEN and WAEN s orgnal model nto ther obstale aodane system. Whle potental felds are stll used for dynam obstale aodane, there are a number of other ommonly used tehnques. The formulaton of behaors for autonomous agents s studed usng the dynamal systems approah to behaor. SCHÖNE et al [9 desrbed a framewor for desgnng behaors for autonomous robots. FAJEN et al [10 11 desed a model of human nagaton based upon experments wth human subjets. Subjets walng straght towards a goal were presented wth an obstale loated at dfferent ntal angles and dstanes. In ths ase, angular aeleraton away from the obstale dereased wth the nrease of both obstale angle and dstane. As a noel and deelopng eolutonary algorthm, swarm ntellgene has beome a hotspot of the doman of artfal ntellgene n reent years [1 15. There exsts a speal relatonshp between swarm ntellgene and artfal lfe. Completed theory and applaton researh hae shown that swarm ntellgene s effete n solng Foundaton tem: Projet( ) supported by the Natonal Natural Sene Foundaton of Chna eeed date: ; Aepted date: Correspondng author: XUE Han, PhD; Tel: ; E-mal: xdjs@163.om

2 J. Cent. South Un. Tehnol. (008) 15: most global random optmzaton problems. Swarm ntellgene has proded omplated and dstrbuted strateges for tradtonal struture optmzaton problems and so far has obtaned fne suess n many felds. Through analyzng multple onstrants and utlzng the autonomy and mutaton of swarm ntellgene, the ant olony algorthm based on swarm ntellgene was used for dynam obstale aodane problems. In ths wor, utlzng the adantages of WSN, a noel dynam obstale aodane algorthm for moble robots based on ant olony optmzaton usng swarm ntellgene was proposed. By enhanng the entre performane, a new hoe of mong obstale aodane for moble robots was proded, whh meets the real-tme demand under unnown enronment. The energy onsumpton and topology of WSN were also dsussed. Model buldng and behaour defnton The global oordnate system whh shows the poston relatonshp between the mong obstale and robot s bult, as shown n Fg.1, where the eloty of the robot s denoted by ; the eloty of the obstale s denoted by ; the relate eloty of the robot to the obstale s denoted by ; the omponent of along the dreton from the robot to the obstale s denoted by l ; the omponent of ertal to the dreton from the robot to the obstale s denoted by θ ; the angle of the robot s denoted by symbol α; the angle of the obstale s denoted by β; the angle of dreton from the robot to the obstale s denoted by θ; the angle of dreton from to l s denoted by γ. The relatonshp among these arables an be expressed as follows: γ Then Eqn.(4) an be rewrtten as sn( α β ) + [ os( α β ) os( α β ) α Sne, and form a trangle, aordng to the sne rule and osne rule, Eqn.(6) an be obtaned as follows: + os( β ) (5) α (6) os( α β ) osϕ (7) sn( α β ) snϕ (8) γ Therefore, Eqn.(5) an be smplfed as snϕ + α osϕ Then the remanng wor s to wor out the best solutons (, α) for energy, tme and other optmal performane goals. The spae oordnate system s shown n Fg.. The zonal regon between two lnes s the ollson regon. In order to aod ollson, robot should selet the spae whh s out of the zonal regon and leae the ollson regon as quly as possble. (9) l os (α θ) os (β θ) (1) θ sn (α θ) sn (β θ) () θ tan γ (3) l Fg. Safe zonal regon n spae oordnate system The test enronment s the worng offe n the laboratory. The sze of the test robot s 0.3 m 0.4 m 0.8 m, whle the obstales are the mong ehles that are smaller than the robots. 3 Sensor model and rtal speed Fg.1 Collson model between robot and obstale Through omputaton, Eqn.(4) an be obtaned as: dγ sn( α β )d + [ os( α β )dα os( α β ) (4) 3.1 Sensor model Aordng to the type and apablty of sensor a sensor model s a funton from the enronment state to a set of measurement readngs. The enronment s manly onsttuted of obstales and the postons and orentatons of robots. For the oupany representaton, a probablst model of a sensor s requred to relate dfferent possble measurements to the onfguraton of

3 86 the enronment. When measurng the dstane between sensors and obstale the dstane deteted from the sensor may not aurately reflet the true dstane due to arous reasons suh as orrupton by noses and albraton errors. The probablst sensor model, therefore, s needed to reflet these unertantes nto the dstane deteton. The sensor model s shown n Fg.3. J. Cent. South Un. Tehnol. (008) 15: depends on the dstane between sensors and obstale and the mong tme of obstales. Exposure model n the energy form s expressed as Eqn.(1), whh s the ntegral of sgnal ntensty deteted by the sensor: t t1 E S( p)dt (1) Defne the dreton from sensor to the obstale s the least exposure dreton (LED), and the dreton from the obstale to the sensor s the maxmal exposure dreton (BED), as shown n Fg.4. It has been proed that sensors obtan mnmal exposure alue when obstales moe along LED, whle sensors obtan maxmal exposure alue when obstales moe along BED. Fg.3 Sngle sensor of robot detetng dstane The sensors are not able to detet sgnals when the ntensty of sgnals s weaer than that of noses. Meanwhle, eah sensor has ts maxmal measurng range. Assumng that F mn s the mnmal effete measured alue of a sensor and F max s the maxmal measured alue. The sgnal ntensty at poston p of the deteted target measured by sensor s satsfes: F mn S( p) F max (10) The model of the sensor s: λ 0, f K < Fmn [ d( p) + 1 λ S p) Fmax, f > F K [ d( p) + 1 λ, else K [ d( p) + 1 ( max (11) where d( p) s the Euldean dstane between the sensor s and target p; λ and are parameters dependng on sensor tehnque, λ s the albraton of the sensor and K s the deayng rate of sgnals. Eqn.(11) ndates that sensng ablty of sensors dereases wth the nrease of dstane between sensors and targets n an exponental way. 3. Exposure model and exposure dreton The onept of exposure desrbes the exposure degree of the mong obstales n sensor networ. If the exposure of an obstale n gen tme s larger than the threshold alue of a sensor, the sensor an dsoer the mong obstale. In some ase exposure alue only Fg.4 Dagram of BED and LED 3.3 Crtal speeds From Eqn.(11), the dstane from whh sensors an detet effetely s omputed as: 1/ λ d( p) 1 Fmn (13) When the eloty of obstales s zero, sensors obtan the maxmal exposure alue: S p)dt dt E T ( T λ λt (14) 0 0 When E t >λt, sensor networ annot detet the obstale. So t s desred that: E t λt (15) Gen the threshold E t, when the exposure alue of obstales under maxmal exposure model s equal to E t, the orrespondng eloty s the mmeasurably rtal speed, whh satsfes Eqn.(16): E T T / 0 t / S( p)dt 0 T λ 1 Fmn ( t + 1) λ dt where s the rtal speed. When, the rtal speed an be omputed as (16)

4 J. Cent. South Un. Tehnol. (008) 15: λ (17) E T t 4 WSN and robot system 4.1 WSN and robots Although WSN s a noel networ tehnque, the data proessng ablty and exeute power of WSN are qute lmted, and strt onstrant on energy s one of ts sgnfant restrtons. Moble robot an automatally deploy and albrate sensor detet and dspose the errors and rratonal deployment of the sensor mae dynam response to the hardware or enronment hange enable dstrbuted WSN systems to wor well ontnually n longer tme and mproe the stablty of the entre system. As smart aton performer moble robots an reharge sensor thus greatly mprong the adaptablty and maneuerablty of moble nodes and enablng the nodes to be freely deployed n enronment wthout hargng establshment. Meanwhle, moble robots hae powerful omputng ablty and great moblty, but the defeny on perepton ablty lmts the deelopment of ts ntellgene. WSN prodes moble robots wth the real-tme global perepton to montor ontnuous and long-rage enronment and searh optmal path as a medum of ommunaton and omputaton. Therefore, robots equpped wth WSN system mae the two systems ooperate wth eah other, enhane the perepton ablty of moble robots and mproe enronment ontrol ablty of WSN. 4. Sensor networs arhteture The module of sensor nodes s shown n Fg.5. Eah of these sattered sensor nodes has the apabltes n olletng data and transmttng data ba to the sn and the end users. Data are routed ba to the end user by a multhop nfrastruture through the sn, whh may ommunate wth the tas manager node a Internet or Satellte. Fg.5 Sensor node module The protool sta used by the sn and all sensor nodes s gen n Fg.6. Ths protool sta ombnes power and routng awarenes ntegrates data wth networng protool ommunates power effently through the wreless medum, and promotes ooperate efforts of sensor nodes. The protool sta onssts of the applaton layer, transport layer, networ layer, data ln layer, physal layer, power management plane, moblty management plane, and tas management plane. Dependng on the sensng tas dfferent types of applaton software an be bult and used on the applaton layer. Fg.6 Sensor networs protool sta 4.3 Map update based on sensor fuson The requred enronment nformaton for moble robots n path plannng under unnown dynam enronment s olleted from sensors. Due to nondetermnst error enronment nfluene, ommunaton tme delay and transmsson mstae, t s dffult for sngle sensor to guarantee the auray and relablty of nput nformaton and exat desrpton of worng enronments. The grd oupany nformaton from multple sensors s redundant, omplementary, real-tme and low-ost, whh enable robots to update maps n the proess of ontnuous enronment deteton and effetely redue sensor errors and norret readngs for qu and parallel analyss of feld enronment. Wth wreless sensor networ, moble robots detet and ollet loal enronment nformaton n real-tme mode, frequently regulate paths and re-plan aordng to new enronment nformaton. Moble robots mantan global map of enronment and plan path aordng to ther urrent map. Then they forward along the path for a whle. Durng ths perod, they update global map based on new nformaton olleted, and re-plan or regulate exstng paths followng the updated global map. They ontnue the loop mentoned aboe untl they reah the target poston. Hstogramm n moton mappng, Bayesan approah [16, Kalman flter, Dempster-Shafer theory and fuzzy log algorthm [17 are four standard mult-sensor

5 864 fuson approahes appled to path plannng of moble robots. The Bayesan model was used n ths wor. The updated oupany probablty s determned usng the Bayesan ondtonal probablty theory aordng to the dstane from the grd to the sensor. The Bayesan model supports a theoretal bass for the probablty map r Let z and be the th atual and measured dstanes from the sensor to grd, respetely. Let S {1, 0} be the random arable that the state of eah grd s ouped (S 0) or not (S 0). The probablty of oupany of grd s as follows: +1 1, + 1 p P[ S 1 r (18) By the Bayesan rule, S an be alulated by: + 1 1, 1, + 1 Pr [ S 1 PS [ 1 r r + 1 1, Pr [ S PS [ r S PS [ , 1, + 1 Pr [ S 0 PS [ 0 r r + 1 1, Pr [ S PS [ r S PS [ 0 The quotent of Eqns.(19) and (0) s: 1, , PS [ 1 r Pr [ S 1 PS [ 1 r 1, , PS [ 0 r Pr [ S 0 PS [ 0 r The ondtonal probablty s formulated as: E (19) (0) (1) PS [ 1 [1 + m ( S) m ( S)/ () Assumng that the data are ndependent, Eqn.() s smplfed to the maxmum entropy formula as follows: PS [ 1 r where 1, + 1 P1 P P1 P + P1 P (1 ) (1 ) (3) 1, P1 P[ S 1 r (4) 1 P P[ S 1 r + (5) The probablty that pont A les wthn grd C, represented by the spae from r 1 to r, an be obtaned from the equaton: r P( A C ) p( r s )dx (6) r 1 A Assumng that the probablty that ether A or B s n the grd C s gen by: P[( A C ) U ( B C ) P( A C ) + P( B C ) P( A C ) P( B C ) (7) 4.4 Energy onsumpton analyss The WSN s subjeted to a ertan set of resoure onstrants suh as lmted on-board battery power and networ ommunaton bandwdth. Clusterng has been well reeed as an effete way to redue the energy J. Cent. South Un. Tehnol. (008) 15: onsumpton of WSN. Clusterng s defned as the proess of hoosng a subset of wreless sensor nodes as luster heads for a gen WSN. Therefore, data traff generated at eah sensor node an be routed a luster heads to the networ sn. Clusterng an enhane the energy effeny n many aspets and has been wdely used n sensor herarhal routng and topology ontrol. Cluster heads reee and forward the traff orgnated by luster members to the networ sn. Clusterng s also used for data aggregaton and fuson. Cluster heads aggregate and fuse the nformaton olleted at the luster member mang the oerall networ traff sgnfantly redued. Thu the energy effeny wll be greatly enhaned. In the sngle hop ommunaton sheme where seeral sensor nodes of the same type dstrbute n a rular luster doman, the role of luster-head sensor node s ated by eah sensor node n turn. Energy onsumpton s omputed as follows: E N( l + µ r ) (8) s where N s the number of data transmtted n a perod; r s the radus of the luster; l s the energy onsumpton of transmsson rut; s the transmsson ost exponent and µ s the fxed fator of transmsson ost. In the mult-hop sheme, the number of relay data paet of eah sensor node n the nth rle s: n a n (n 1) (9) where s the thness of eah rle n the mult-hop sheme. Eah sensor node n the nth rle not only relays the n data paet, but also transmts ts own data paet. In eah perod wth N data transmtted, the energy onsumpton s: E ( n) N[ ( l+ µ ) + ( l+ u ) (30) m n Sensor nodes n the frst leer onsume the most amount of energy: E ( ) N[( l+ µ )( a )/ + ( l+ u ) (31) m In the ase of a, Eqn.(31) s smplfed to the same as sngle hop sheme whh osts the least energy. The mult-hop networ s parttoned and the luster-heads hosen wth four stages are shown n Fg.7. For example, gen the system topology of sensor networ, the neghbourhood relaton s represented by the lns onnetng nodes n Fg.7(a). In Fg.7(b), the part of mult-hop networ s parttoned nto fe lusters. The dentfaton of a luster-head denotes whh luster t belongs to. In the hoosng stage, eah luster dedes ts new head aordng to the remanng battery power.

6 J. Cent. South Un. Tehnol. (008) 15: Fg.7 Cluster-heads hosen wth four stages: (a) Tree stage; (b) Parttonng stage; () Choosng stage; (d) Herarhy lusterng stage Fg.7() shows the hange of these lusters whle others reman the same. Fnally, these luster-heads onsttute a hgher-leel luster n Fg.7(d). 5 Algorthm based on swarm ntellgene 5.1 Ant olony optmzaton algorthm Ant olony algorthm s a noel heurst eolutonary optmzaton algorthm proposed by Italan sholar DIG n 1991 [18 0. It mms ant behaors n nature and has the haratersts of dstrbuted omputng, poste feedba of nformaton and heurst searh [1. Ant olony algorthm s rendered sutable for a arety of wdespread applatons n multple ombnatoral optmzaton problem not only n dsrete systems but also n ontnuous system suh as shedulng, quadrat assgnment, networ rootng, et. The frame of multple onstraned optmzaton soluton based on ant olony algorthm s as follows. Ant Colony Algorthm Input: Weghted graph, neghborhood nto. Whle: Termnaton not met do Compute-ntal pheromone, node dst potental Shedule attes Ant based soluton onstruton Pheromone update Node dstane potental update End attes Update and reord the best soluton n the populaton utput: Best, anddate to optmal soluton 5. AC algorthm for robot obstale aodane Usng AC algorthm desrbed aboe, the optmal solutons (, α) for energy, tme and other optmal performane goals are wored out. When the robot ponts to the mong obstale, t should adjust ts dreton. To dre the robot to reah the goal as quly as possble, the dreton of the robot and the lne from the robot to ts goal, that s α+θ, should be mnmum. Meanwhle, the faster the speed of the robot, the better performane of ollson aodane. The obstale aodane funton an be represented as follows: f(, α)w 1 +w θ+ α (3) The proedures of optmal algorthm are lsted as follows. Step 1 System ntalzaton: ntalze the tme, terate tme and pheromone. Plae m ants on eah ntal poston. Step Seleton: the transton probablty of ant from poston to poston j at tme t s gen by p, j α β τ, j() t η, j() t, f j S α β () t [ τr, ( t) ηr, ( t) (33) r S 0, else where η, j (t) s the reproal of objete funton; α s the nfluenng weght of τ, j (t) to the whole transton probablty; β s the nfluenng weght of η, j (t) to the whole transton probablty; and S s the set of postons onneted wth node and not sted by ant. Step 3 Compute the objete funtons of eah ant and reord the urrent optmal soluton of the problem. Step 4 Update the loal pheromone and the onentraton of pheromone. Update loally the pheromone of the hosen parameter as follows:

7 866 τ ( t+ 1) (1 ρ) τ ( t) (34) j j where ρ s the olatle fator of pheromone of the parameter, 0<ρ<1. Step 5 eord optmal solutons. The pheromone s updated globally as follows: τ, j( t 1) (1 ρ) τ, j( t) a1 + + (35) where a 1 s a onstant. Step 6 Che the termnaton ondton: f the yle ndex s less than preset tmes or the optmzaton target s not reahed, go to Step ). Step 7 utput the optmal soluton and termnate the proedure. 6 Expermental results 6.1 Hardware onfguraton and results The algorthm was mplemented on real moble nodes. The man omponents on the top layer were MICAz sensor nodes omposed of MCU, ATMega18L, and the wreless ommunaton hp, CC40. A sensor board ould be onneted wth MICAz addtonally. The top layer was assgned to gettng the sensng data and ommunatng wth other node nformng bottom layer wth seral ports. The WSN bult ould wor well for a long tme. The detals of sensor nodes used are spefed n Table 1. J. Cent. South Un. Tehnol. (008) 15: Table 1 Sensor node spefaton Parameter Value emar CPU CC40 Compatble CPU speed/ghz.4 Confguraton EEPM/b 4 AM/b 4 Program flash memory/b 18 Measurement seral flash/b 51 > Current draw/µa Ate mode <15 Sleepng mode ado frequeny band/mhz ISM band Data rate/(b s 1 ) 50 Power/W eeng ado urrent draw/µa mode 1 Sleepng mode ado range/m >40 utdoor ther nterfae Dgtal I/, IC, SPI The algorthm was tested under dynam enronment wth mong enronment as shown n Fg.8, where the squares represent the robot and the rles represent mong objets. The results show that the robot an go from the ntal poston to the destnaton wthout Fg.8 Mong states under dynam enronment wth mong obstales: (a) Intal state; (b) Mong state 1; () Mong state ; (d) Mong state 3; (e) Mong state 4; (f) Fnal state

8 J. Cent. South Un. Tehnol. (008) 15: olldng wth any dynam obstale. The omparson between genet algorthm and ant olony optmzaton algorthm on the tme robots spend n obstale aodane and the length of path robots wal s lsted n Table. The omputer equpment s Pentum 4 CPU 1.4GHz. Through the effent and useful algorthm, the optmal path an soon be omputed. The results show that the method based on ant olony optmzaton algorthm s superor to the genet algorthm. Table Performane omparson between GA and AC Algorthm untme/ms Path length/m GA AC Effet of terate tmes on length of shortest path The length of shortest path after dfferent terate tmes s shown n Table 3. It an be seen that as terate tmes grow, the length of shortest path dereases. It reahes 3.01 after 40 tmes of teraton. Table 3 Length of shortest path after dfferent terate tmes Iterate tmes Path length/m Effet of WSN on measurement error The measurement auray of sngle sensor and nformaton fuson based on WSN was ompared. Fg.9 shows the omparson results on dstane errors of the two methods. The result shows that usng sensor nformaton fuson an greatly mproe the auray n omparson wth sngle sensor. 7 Conlusons 1) A noel dynam obstale aodane algorthm s proposed utlzng the adantages of WSN. A pratal mplementaton wth real WSN and real moble robots are arred out. esults show that usng sensor nformaton fuson an greatly mproe the auray n omparson wth usng sngle sensor. Fg.9 Error omparson between sngle sensor and nformaton fuson based on WSN ) Ant olony algorthm based on bon swarm ntellgene s ntegrated. The onergene ure of the shortest path length shows the onergene of the algorthm. The algorthm s proed to enhane the entre performane of tradton genet algorthm under real-tme enronment wth multple dynam obstales. 3) As a noel approah for real-tme dynam obstale aodane, the algorthm promotes the ooperaton between moble robots and WSN. eferenes [1 GACANIN D. A sere-entr modei for wreless sensor networs [J. IEEE Journal on Seleted Areas n Communaton 005, 3(6): [ AKYILDIZ I F, SU W, SANKAASUBAMANIAM Y, CAYICI E. Wreless sensor networs: A surey [J. Computer Networ 00, 38(4): [3 BAGINSKY D, ESTIN D. umor routng algorthm for sensor networs [C// Proeedngs of the 1st Worshop on Sensor Networs and Applatons. Atlanta: ACM Pres 00: 31. [4 KHATIB. eal-tme obstale aodane for manpulators and moble robots [J. Journal of obots esearh, 1986, 5(1): [5 GEN P, LENAD N E. A onergent dynam wndow approah to obstale aodane [J. IEEE Trans on obots and Automaton, 005, 1(): [6 TANG Png, ZHANG Q, YANG Y-mn. Studyng on path plannng and dynam obstale aodng of soer robot [C// Proeedngs of the 3rd World Congress on Intellgent Control and Automaton. Dsataway IEEE, 000: (n Chnese) [7 BENSTEIN J, KEN Y. The etor feld hstogram-fast obstale aodane for moble robots [J. IEEE Transatons on obots and Automaton, 1991, 7(3): [8 FX D, BUGAD W, THUN S. The dynam wndow approah to ollson aodane [J. IEEE obots & Automaton Magazne, 1997, 4(1): [9 SCHÖNE G, DSEM, ENGELS C. Dynams of behaor: Theory and applatons for autonomous robot arhtetures [J. obots and Autonomous System 1995, 16(1):

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