An auto-adaptive convex map generating path-finding algorithm: Genetic Convex A
Path-finding is a fundamental problem in many applications, such as robot control, global positioning system and computer games. Since A * is time-consuming when applied to large maps, some abstraction methods have been proposed. Abstractions can greatly speedup on-line path-finding by combing the a...
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| Published in | International journal of machine learning and cybernetics Vol. 4; no. 5; pp. 551 - 563 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2013
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1868-8071 1868-808X |
| DOI | 10.1007/s13042-012-0120-x |
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| Abstract | Path-finding is a fundamental problem in many applications, such as robot control, global positioning system and computer games. Since A
*
is time-consuming when applied to large maps, some abstraction methods have been proposed. Abstractions can greatly speedup on-line path-finding by combing the abstract and the original maps. However, most of these methods do not consider obstacle distributions, which may result in unnecessary storage and non-optimal paths in certain open areas. In this paper, a new abstract graph-based path-finding method named Genetic Convex A
*
is proposed. An important convex map concept which guides the partition of the original map is defined. It is proven that the path length between any two nodes within a convex map is equal to their Manhattan distance. Based on the convex map, a fitness function is defined to improve the extraction of key nodes; and genetic algorithm is employed to optimize the abstraction. Finally, the on-line refinement is accelerated by Convex A
*
, which is a fast alternative to A
*
on convex maps. Experimental results demonstrated that the proposed abstraction generated by Genetic Convex A
*
guarantees the optimality of the path whilst searches less nodes during the on-line processing. |
|---|---|
| AbstractList | Path-finding is a fundamental problem in many applications, such as robot control, global positioning system and computer games. Since A
*
is time-consuming when applied to large maps, some abstraction methods have been proposed. Abstractions can greatly speedup on-line path-finding by combing the abstract and the original maps. However, most of these methods do not consider obstacle distributions, which may result in unnecessary storage and non-optimal paths in certain open areas. In this paper, a new abstract graph-based path-finding method named Genetic Convex A
*
is proposed. An important convex map concept which guides the partition of the original map is defined. It is proven that the path length between any two nodes within a convex map is equal to their Manhattan distance. Based on the convex map, a fitness function is defined to improve the extraction of key nodes; and genetic algorithm is employed to optimize the abstraction. Finally, the on-line refinement is accelerated by Convex A
*
, which is a fast alternative to A
*
on convex maps. Experimental results demonstrated that the proposed abstraction generated by Genetic Convex A
*
guarantees the optimality of the path whilst searches less nodes during the on-line processing. Path-finding is a fundamental problem in many applications, such as robot control, global positioning system and computer games. Since A* is time-consuming when applied to large maps, some abstraction methods have been proposed. Abstractions can greatly speedup on-line path-finding by combing the abstract and the original maps. However, most of these methods do not consider obstacle distributions, which may result in unnecessary storage and non-optimal paths in certain open areas. In this paper, a new abstract graph-based path-finding method named Genetic Convex A* is proposed. An important convex map concept which guides the partition of the original map is defined. It is proven that the path length between any two nodes within a convex map is equal to their Manhattan distance. Based on the convex map, a fitness function is defined to improve the extraction of key nodes; and genetic algorithm is employed to optimize the abstraction. Finally, the on-line refinement is accelerated by Convex A*, which is a fast alternative to A* on convex maps. Experimental results demonstrated that the proposed abstraction generated by Genetic Convex A*guarantees the optimality of the path whilst searches less nodes during the on-line processing. |
| Author | Shiu, Simon Chi-Keung Li, Yan Su, Pan Li, Yingjie |
| Author_xml | – sequence: 1 givenname: Pan surname: Su fullname: Su, Pan organization: Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University – sequence: 2 givenname: Yan surname: Li fullname: Li, Yan organization: Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University – sequence: 3 givenname: Yingjie surname: Li fullname: Li, Yingjie organization: Department of Computing, The Hong Kong Polytechnic University – sequence: 4 givenname: Simon Chi-Keung surname: Shiu fullname: Shiu, Simon Chi-Keung organization: Department of Computing, The Hong Kong Polytechnic University |
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| Keywords | Convex map Path-finding G-CA Abstract graph Genetic algorithm |
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| References | Su P, Li Y, Li WL (2010) A game map complexity measure based on hamming distance. In: Proceedings of PACIIA. Wuhan, Hubei, pp 332–335 ZhuJLiXPShenWMEffective genetic algorithm for resource-constrained project scheduling with limited preemptionsInt J Mach Learn Cyber201022556510.1007/s13042-011-0014-3 KorfREDepth-first iterative-deepening: an optimal admissible tree searchArtif Intell19852719710981028010.1016/0004-3702(85)90084-00573.68030 Chen CJ (2011) Structural vibration suppression by using neural classifier with genetic algorithm. Int J Mach Learn Cyber. doi:10.1007/s13042-011-0053-9 SametHNeighbor finding techniques for image represented by quadtreesComputer Graphic Image Process1982181375710.1016/0146-664X(82)90098-30531.68041 Michalewicz Z, Janikow C (1991) Handling constraints in genetic algorithms. In: Proceedings of the 4th ICGA. San Diego, CA, pp 151–157 KavraliLESvestkaPLatombeJCOvermarsHMProbabilistic roadmaps for path planning in high dimensional configuration spacesIEEE Trans Robot Automat199612456658010.1109/70.508439 KorfREReidMEdelkampSTime complexity of Iterative-Deepening-A*Artif Intell20011291–2199218183577710.1016/S0004-3702(01)00094-70971.68147 YanHBLiuYCA new algorithm for finding shortcut in a city’s road net based on GIS technologyChinese J Comput20022000–02210215 Sturtevant N (2010) Pathfinding benchmarks. http://www.movingai.com/benchmarks/index.html. Accessed 19 April 2011 PetterssonPODohertyPProbabilistic roadmap based path planning for an autonomous unmanned helicopterJ Intell and Fuzzy Syst2006174395405 Stout B (2000). The basics of A* for path planning. In: DeLoura M (ed) Game Programming Gems. Charles River Media, Rockland, pp 254–263 Samuel E, Johan F (2008) Pathfinding with hard constraints—mobile systems and real time strategy games combined. Master Thesis of Blekinge Institute of Technology, Sweden ChenDZSzczerbaRJUhranJJA framed-quadtree approach for determining Euclidean shortest paths in a 2-D environmentIEEE Trans Robot Automat199713566868110.1109/70.631228 WangXZHeQChenDGYeungDA genetic algorithm for solving the inverse problem of support vector machinesNeurocomputing20056822523810.1016/j.neucom.2005.05.006 Sturtevant N (2007) Memory-efficient Abstraction for Pathfinding. In: Proceedings of the 3rd AIIDE. Stanford, California, pp 31–36 KoenigSLikhachevMFurcyDLifelong Planning A*Artif Intell20041551–293146205292610.1016/j.artint.2003.12.0011085.68674 Demyen D, Buro M (2006) Efficient triangulation-based pathfinding. In: Proceedings of the 21th AAAI. Boston, Massachusetts, pp 942–947 HolteRCMkadmiTZimmerRMMacDonaldAJSpeeding up problem solving by abstraction: a graph oriented approachArtif Intell1996851–232136110.1016/0004-3702(95)00111-5 Yahja A, Stentz A, Singh S, Brummit B (1998) Framed-quadtree path planning for mobile robots operating in sparse environments. In: Proceedings of IEEE Conf on Robot and Automat. Leuven, Belgium, pp 650–655 BoteaAMullerMScheafferJNear-optimal hierarchical pathfindingJ Game Dev200411728 Harabor D, Botea A (2010) Breaking path symmetries on 4-connected grid maps. In: Proceedings of the 6th AIIDE. Stanford, California, pp 33–38 Sturtevant N, Buro M (2005) Partial pathfinding using map abstraction and refinement. In: Proceedings of the 20th NCAI. Pittsburgh, Pennsylvania, pp 1392–1397 TongDLMintramRGenetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selectionInt J Mach Learn Cyber201011–4758710.1007/s13042-010-0004-x Snook G (2000) Simplified 3D movement and pathfinding using navigation meshes. In: DeLoura M (ed) Game programming gems. Charles River Media, Rockland, pp 288–304 Sturtevant N, Jansen R (2007) An analysis of map-based abstraction and refinement. In: Proceedings of the 7th SARA. Whistler, Canada, pp 344–358 WangXZHeYLDongLCZhaoHYParticle swarm optimization for determining fuzzy measures from dataInform Sci2011181194230425210.1016/j.ins.2011.06.0021242.68296 BoehmOHardoonDRManevitzLMClassifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithmsInt J Mach Learn Cyber20112312513410.1007/s13042-011-0030-3 O Boehm (120_CR20) 2011; 2 120_CR11 XZ Wang (120_CR22) 2005; 68 RE Korf (120_CR4) 1985; 27 H Samet (120_CR8) 1982; 18 120_CR27 120_CR28 A Botea (120_CR7) 2004; 1 120_CR24 LE Kavrali (120_CR9) 1996; 12 120_CR26 S Koenig (120_CR6) 2004; 155 120_CR1 DZ Chen (120_CR25) 1997; 13 120_CR16 XZ Wang (120_CR23) 2011; 181 120_CR17 120_CR18 DL Tong (120_CR21) 2010; 1 120_CR12 120_CR13 120_CR14 RC Holte (120_CR10) 1996; 85 120_CR15 PO Pettersson (120_CR2) 2006; 17 J Zhu (120_CR19) 2010; 2 HB Yan (120_CR3) 2002; 2000–02 RE Korf (120_CR5) 2001; 129 |
| References_xml | – reference: Chen CJ (2011) Structural vibration suppression by using neural classifier with genetic algorithm. Int J Mach Learn Cyber. doi:10.1007/s13042-011-0053-9 – reference: BoteaAMullerMScheafferJNear-optimal hierarchical pathfindingJ Game Dev200411728 – reference: KorfREDepth-first iterative-deepening: an optimal admissible tree searchArtif Intell19852719710981028010.1016/0004-3702(85)90084-00573.68030 – reference: KorfREReidMEdelkampSTime complexity of Iterative-Deepening-A*Artif Intell20011291–2199218183577710.1016/S0004-3702(01)00094-70971.68147 – reference: BoehmOHardoonDRManevitzLMClassifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithmsInt J Mach Learn Cyber20112312513410.1007/s13042-011-0030-3 – reference: Sturtevant N (2007) Memory-efficient Abstraction for Pathfinding. In: Proceedings of the 3rd AIIDE. Stanford, California, pp 31–36 – reference: Harabor D, Botea A (2010) Breaking path symmetries on 4-connected grid maps. In: Proceedings of the 6th AIIDE. Stanford, California, pp 33–38 – reference: KavraliLESvestkaPLatombeJCOvermarsHMProbabilistic roadmaps for path planning in high dimensional configuration spacesIEEE Trans Robot Automat199612456658010.1109/70.508439 – reference: Stout B (2000). The basics of A* for path planning. In: DeLoura M (ed) Game Programming Gems. Charles River Media, Rockland, pp 254–263 – reference: Demyen D, Buro M (2006) Efficient triangulation-based pathfinding. In: Proceedings of the 21th AAAI. Boston, Massachusetts, pp 942–947 – reference: YanHBLiuYCA new algorithm for finding shortcut in a city’s road net based on GIS technologyChinese J Comput20022000–02210215 – reference: ZhuJLiXPShenWMEffective genetic algorithm for resource-constrained project scheduling with limited preemptionsInt J Mach Learn Cyber201022556510.1007/s13042-011-0014-3 – reference: KoenigSLikhachevMFurcyDLifelong Planning A*Artif Intell20041551–293146205292610.1016/j.artint.2003.12.0011085.68674 – reference: PetterssonPODohertyPProbabilistic roadmap based path planning for an autonomous unmanned helicopterJ Intell and Fuzzy Syst2006174395405 – reference: Sturtevant N, Jansen R (2007) An analysis of map-based abstraction and refinement. In: Proceedings of the 7th SARA. Whistler, Canada, pp 344–358 – reference: WangXZHeQChenDGYeungDA genetic algorithm for solving the inverse problem of support vector machinesNeurocomputing20056822523810.1016/j.neucom.2005.05.006 – reference: SametHNeighbor finding techniques for image represented by quadtreesComputer Graphic Image Process1982181375710.1016/0146-664X(82)90098-30531.68041 – reference: Michalewicz Z, Janikow C (1991) Handling constraints in genetic algorithms. In: Proceedings of the 4th ICGA. San Diego, CA, pp 151–157 – reference: Sturtevant N (2010) Pathfinding benchmarks. http://www.movingai.com/benchmarks/index.html. Accessed 19 April 2011 – reference: TongDLMintramRGenetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selectionInt J Mach Learn Cyber201011–4758710.1007/s13042-010-0004-x – reference: WangXZHeYLDongLCZhaoHYParticle swarm optimization for determining fuzzy measures from dataInform Sci2011181194230425210.1016/j.ins.2011.06.0021242.68296 – reference: Su P, Li Y, Li WL (2010) A game map complexity measure based on hamming distance. In: Proceedings of PACIIA. Wuhan, Hubei, pp 332–335 – reference: Yahja A, Stentz A, Singh S, Brummit B (1998) Framed-quadtree path planning for mobile robots operating in sparse environments. In: Proceedings of IEEE Conf on Robot and Automat. Leuven, Belgium, pp 650–655 – reference: Snook G (2000) Simplified 3D movement and pathfinding using navigation meshes. In: DeLoura M (ed) Game programming gems. Charles River Media, Rockland, pp 288–304 – reference: Samuel E, Johan F (2008) Pathfinding with hard constraints—mobile systems and real time strategy games combined. Master Thesis of Blekinge Institute of Technology, Sweden – reference: ChenDZSzczerbaRJUhranJJA framed-quadtree approach for determining Euclidean shortest paths in a 2-D environmentIEEE Trans Robot Automat199713566868110.1109/70.631228 – reference: HolteRCMkadmiTZimmerRMMacDonaldAJSpeeding up problem solving by abstraction: a graph oriented approachArtif Intell1996851–232136110.1016/0004-3702(95)00111-5 – reference: Sturtevant N, Buro M (2005) Partial pathfinding using map abstraction and refinement. In: Proceedings of the 20th NCAI. 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*
is time-consuming... Path-finding is a fundamental problem in many applications, such as robot control, global positioning system and computer games. Since A* is time-consuming... |
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| SubjectTerms | Artificial Intelligence Complex Systems Computational Intelligence Computer & video games Control Engineering Genetic algorithms Global positioning systems GPS Graphs Heuristic Mechatronics Nodes Optimization Original Article Pattern Recognition Robot control Robotics Systems Biology |
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| Title | An auto-adaptive convex map generating path-finding algorithm: Genetic Convex A |
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