THE PATH PLANNING ALGORITHM AND SIMULATION FOR MOBILE ROBOT

Transcription

1 Journal of Theoretcal and Appled Informaton Technology 30 th Aprl 013. Vol. 50 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: THE PATH PLANNING ALGORITHM AND SIMULATION FOR MOBILE ROBOT 1 ZHANCHUN MA XIAOMEI NING 1 Lecturer College of Scence Qqhar Unversty Qqhar Helongjang Provnce Chna Lecturer Modern Educaton Technology Center Qqhar Medcal Unversty Qqhar Helongjang Provnce Chna E-mal: 1 mazhanchun9803@163.com nngxaome00@163.com ABSTRACT Research the global path optmzaton of moble robot. The tradtonal evoluton algorthm s shortcomng s precocty n order to overcome the shortcomng and mprove the speed and accuracy of the tradtonal evoluton algorthm. The paper combned cloud theory wth rough set to use n path plannng of moble robot. In smulaton experment the envronment was descrbed by grd method and random produced ntal path group the frst rough set used tranng ntal path group a seres of feasble paths calculated by the mnmum decson rule tranng. The path populaton optmzed by cloud model fnally acqured the best path to walk. The smulaton results verfed specal when the ntal group was large the convergence speed and search qualty had mproved by the algorthm combned wth the evolutonary algorthm. Keywords: Cloud Model Rough Set Robots Path Plannng 1. INTRODUCTION The depth and breadth n the research of moble robot path plannng had a great development. All knds of algorthms had ther own advantages and dsadvantages. The tradtonal evolutonary algorthm search ablty s lmted and Easy to converge to a local optmal path [1-]. Improved evolutonary algorthm for robot path plannng was proposed. The path wll lead to large scale when the path searchng space s larger the ablty to remove redundant path s poorer and affect the speed of path plannng [3-4]. Rough set theory s a new mathematcal tool and has become both at home and abroad n the feld of artfcal ntellgence s a relatvely new academc hotspot [5-6]. Cloud model has been appled n mnng space space database query ntellgent control mage processng network technology [7-8] etc In order to put the rough sets and the advantages of cloud evolutonary algorthm fuson together n order to acheve better effect ths paper puts forward based on rough set and cloud model evolutonary algorthm. Secton presented the theory of robot path plannng by ntegrated cloud model and rough set algorthm. In secton 3 we proposed smulaton results. Secton 4 gave the concluson to ths paper.. THE PRINCIPLE OF ROBOT PATH PLANNING Moble robot path plannng refers to an ndcator for the startng pont to the end of the optmal collson-free In order to smplfy the problem of path plannng the condton was lmted: (1) Movement envronment was statc. () Obstacles stll was statc. Fgure 1. Grd Envronment.1 Envronment descrpton 601

2 Journal of Theoretcal and Appled Informaton Technology 30 th Aprl 013. Vol. 50 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: Envronment was descrbed grd method Robot actvty area was a two dmensonal area and coded the grd dvson. Robot could from a grd along the eght drectons to the adjacent grds. Establsh plane rectangular coordnate system the horzontal drecton for x axs vertcal drecton for the y axs. In fgure 1 each grd set a number Index. Startng pont s (0 0) and target (x y). Between (0 0) and (x y) two ponts the grd method random generated many paths and produced the ntal path group. Then the ftness of ntal path group was calculated by the formula (1) to (4). n ( v ) = ( x x ) + ( y y ) j= 0 j j 1 j j 1 D (1) (x y ) (x j-1 y j-1 ) were the robot postons. = 1 ( p ) = 0 ( p ) ϕ { obstacle m} ft ( v ) () n 1 ( ) v = = φ ϕ (3) 0 ( ) + pow( 0.9ϕ( v )) 1 = (4) D v After calculated ftness functon the hgher ftness path group as the basc data of the rough set tranng.. Rough set tranng path group If a robot for the current poston of the grd P number was ( P dd not belong to the boundary pont) robot has eght drecton s feasble as followng fgure. Fgure. Path Fgure Feasble path as condton attrbutes C = { X 1 X X X X X X X 8} ( )was quantzed 1 representatve obstacles grd P representatve free grd 1 3 representatve free grd. Intal decson grd descrbed as table 1. Table 1. Intal Decson Grd U X 1 X X 3 X 4 X 5 X 6 X 7 X 8 Y To compatble decson the data n the table wth rough set to calculate the mnmum number of attrbutes the optmal attrbute set. C = { X 1 X.. X n} was attrbute set f ( C X ) = H c X could omt n C else X could not omt. Accordng to these characterstcs t can smplfy the ntal decson dagram; get a smplfed decson dagram as shown n table shows. Table. Removng The Redundant Attrbutes U X 1 X X 3 X 5 X 7 Y Does not affect the decson of the custom process decson rules can be the elmnaton fnally to obtan the mnmum decson rules as shown n table 3. 60

3 Journal of Theoretcal and Appled Informaton Technology 30 th Aprl 013. Vol. 50 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: Table 3. Mnmum Decson Rules U X1 X X3 X5 X7 Y Through the table 1 to table 3 treatment process can be seen that the rough set tranng after the populaton scale quckly narrow the guarantee s not lost on the bass of feasble soluton to mprove the effcency of the algorthm. Rough set tranng get path group as a cloud model evoluton of the ntal path of evoluton and varaton..3 Cloud model evoluton path group In lfe there are many problems attrbutes can use "cloud" concept to descrbe. One-dmensonal normal cloud C ( Ex En He )descrbed the "young" the qualtatve lngustc value as fgure 3 and 4. Fgure 3. Cloud Fgure The ntal path group through the rough set tranng The algorthm got the hghest ftness before artcle N path called the best Onedmensonal normal cloud operator produced nextgeneraton Θ = X = Π = { t Norm( En He) = 1... N} { x Norm( Ex t ) t Θ = 1... N} {( x y ) x X y = exp( ( x Ex) /(t ))} Norm ( µδ ) (5) s normal random varable µ s expectaton δ s varance and N s ndvdual number. Fgure 4. Cloud Fgure wth Dfferent Parameters Defne 1 Feasble path refers to the ntal path n the path of rough set tranng get the hghest ftness before artcle N Defne Optmal feasble path s populaton evoluton the process of gettng the ftness of the hghest path dvded nto contemporary optmal feasble path and cross generaton optmal feasble Defne 3 Contemporary optmal feasble path s n an evolutonary path all of the hghest degree of vable Defne 4 Cross generaton optmal feasble path refers to multple evoluton and ftness of the hghest. The result of the algorthm for all evoluton and the cross-cultural generaton optmal feasble Defne 5 evolutonary generaton of the optmal path across the generatons called nontrval evolutonary generaton. Defne 6 The evoluton no cross generaton optmal path called the ordnary evoluton generaton. Defne 7 Two optmal path across generatons between the evoluton of algebra are called contnuous ordnary algebra. Feasble path as a father calculated the next generaton of path by formula (5) cloud operator. The evolutonary process f appear cross generaton optmal feasble path the algorthm may 603

4 Journal of Theoretcal and Appled Informaton Technology 30 th Aprl 013. Vol. 50 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: be n neghborhood found a new optmal feasble path the need to reduce En and He values thus ncreasng the search accuracy and stablty ths s the local refnement operaton. Some evolutonary generaton found no new cross generaton optmal feasble path contnuous ordnary algebra to acheve a certan threshold λ value local the algorthm nto a local optmum feasble path neghborhood to fnd than the current optmal feasble path ftness hgher feasble path the need to jump out of the small local varaton operaton the operaton method s to mprove En and He value. After several generatons to acheve the global λ global threshold evoluton has not been ftness hgher feasble path then varaton operaton falure the algorthm needs mutaton operaton here take hstory cross generaton optmal feasble path mean mutaton as the best possble route to produce the next generaton. 3. THE SIMULATION RESULTS In matlab7.0 envronment the algorthm smulaton results the algorthm of parameter for grd sad workng envronment. (00) s ntal poston (4040) s end. The populaton sze for 100 n the photo black for obstacles whte for free grd. In the cloud model plannng path experment En and He values s nonlnear emprcal value for dfferent En and He values the test result as shown n table 4 shows. Improper parameters can lead to local optmum even make algorthm falure n the calculaton of the optmal Table 4. The Results wth Dfferent En and He Parameters ftness result En=He= local optmal En=He= Global suboptmal En=He= local optmal En=3He= local optmal En=3He= local optmal En=3He= No global soluton En=1He= No global soluton En=1He= local optmal En=1He= local optmal Combned wth rough set and cloud model path plannng random generaton ntal populaton wth rough set tranng ntal populaton t s genetc group of smplfed agan by cloud operator on heredty and varaton n ths algorthm the evoluton and varaton s unfed evoluton type varaton s the evoluton and varaton fuson the algorthm can tell the current evoluton condton and can be adaptvely adjusted. Expermental set up many obstacles to observe the algorthm can effcently complete path plannng. Fgure 5. Rough Set Tranng The Optmal Path Fgure 6. The Algorthm of The Optmal Path From fgure 5 and fgure 6 plannng results can see n rough set after tranng the optmal path based on cloud operator evoluton algorthm varaton and fnally the best path generaton and relable. In order to valdate the effcency of the algorthm ths paper for ffty tmes of tral and error the structure s as shown n table 5 shows cloud evolutonary algorthm and tme are obtaned and the optmal feasble path algorthm has 46 tmes get a feasble optmal feasble From that the algorthm n the optmzaton effcency s mproved. 604

5 Journal of Theoretcal and Appled Informaton Technology 30 th Aprl 013. Vol. 50 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: Table 5. Contrast Test Results Algorthm tradtonal algorthm ths algorthm Optmal path Optmal rate 76% 9% Average path length CONCLUSIONS Ths paper puts forward the rough set and cloud model ntroduced n evolutonary algorthm for path plannng. When the ntal path group of search space s very large through the rough set tranng effectvely remove redundant path mprove the search effcency so as to avod the evolutonary algorthm convergence speed s low easy to fall nto the local extreme faults. The smulaton results verfy the path n a large group the algorthm of average cost smaller more short Usng ths algorthm can acheve satsfactory plannng effect and convergence speed. ACKNOWLEDGMENT: [5] Kmak Shrahama Yuta Matsuok Kunak Uehara. Event retreval n vdeo archves usng rough set theory and partally supervsed learnng. Multmed Tools Appl pp [6] Huo Jangtao. MOBILE ROBOT VISION TRACKING AND PATH PLANNING BASED ON ROUGH SET. Hebe Unversty. 010 [7] Huang Han Ln Zhyong Hao Zhfeng Zhang Yushan L Xueqang. Convergence Analyss and Comparson of Evolutonary Algorthms Based on Relaton Model. CHINESE JOURNAL OF COMPUTERS. May 011. pp [8] Hu Shyuan L DerenLu Yaoln L Dey. Mnng Weghts of Land Evaluaton Factors Based on Cloud Model and Correlaton Analyss. Geo-spatal Informaton Scence (3) pp: 18-. The research s funded by young teachers' scentfc and technology project under grand #100k-M6 of Qqhar Unversty. REFRENCES: [1] Yang Xanfeng Fu Junhu Moble Robot Path Plannng Based on Grd Algorthm and CGA Computer Smulaton July 01 pp [] Long Chengfeng He Guangpng Chen Jajun. Path Plannng of Robots under the Dynamc Stuaton Journal of Guangdong Unversty of Petrochemcal Technology Dec.011 pp [3] Lu Chunan A Method of Dynamc Mult- Objectve Optmzaton Based on Evolutonary Mechansm MICROELECTRONICS & COMPUTER Vol.6 No.1 January 009. pp [4] Ma Zhanchun Nng Xaome Cloud Model Based Evolutonary Algorthm for Robot Path Plannng BULLETIN OF SCIENCE AND TECHNOLOGY Vol.8 No.10 Oct. 01 pp