Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms
A multi-objective vehicle path planning method has been proposed to optimize path length, path safety, and path smoothness using the elitist non-dominated sorting genetic algorithm—a well-known soft computing approach. Four different path representation schemes that begin their coding from the start...
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| Veröffentlicht in: | Soft computing (Berlin, Germany) Jg. 17; H. 7; S. 1283 - 1299 |
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| Sprache: | Englisch |
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Springer-Verlag
01.07.2013
Springer Nature B.V |
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | A multi-objective vehicle path planning method has been proposed to optimize path length, path safety, and path smoothness using the elitist non-dominated sorting genetic algorithm—a well-known soft computing approach. Four different path representation schemes that begin their coding from the start point and move one grid at a time towards the destination point are proposed. Minimization of traveled distance and maximization of path safety are considered as objectives of this study while path smoothness is considered as a secondary objective. This study makes an extensive analysis of a number of issues related to the optimization of path planning task-handling of constraints associated with the problem, identifying an efficient path representation scheme, handling single versus multiple objectives, and evaluating the proposed algorithm on large-sized grids and having a dense set of obstacles. The study also compares the performance of the proposed algorithm with an existing GA-based approach. The evaluation of the proposed procedure against extreme conditions having a dense (as high as 91 %) placement of obstacles indicates its robustness and efficiency in solving complex path planning problems. The paper demonstrates the flexibility of evolutionary computing approaches in dealing with large-scale and multi-objective optimization problems. |
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| AbstractList | A multi-objective vehicle path planning method has been proposed to optimize path length, path safety, and path smoothness using the elitist non-dominated sorting genetic algorithm—a well-known soft computing approach. Four different path representation schemes that begin their coding from the start point and move one grid at a time towards the destination point are proposed. Minimization of traveled distance and maximization of path safety are considered as objectives of this study while path smoothness is considered as a secondary objective. This study makes an extensive analysis of a number of issues related to the optimization of path planning task-handling of constraints associated with the problem, identifying an efficient path representation scheme, handling single versus multiple objectives, and evaluating the proposed algorithm on large-sized grids and having a dense set of obstacles. The study also compares the performance of the proposed algorithm with an existing GA-based approach. The evaluation of the proposed procedure against extreme conditions having a dense (as high as 91 %) placement of obstacles indicates its robustness and efficiency in solving complex path planning problems. The paper demonstrates the flexibility of evolutionary computing approaches in dealing with large-scale and multi-objective optimization problems. A multi-objective vehicle path planning method has been proposed to optimize path length, path safety, and path smoothness using the elitist non-dominated sorting genetic algorithm—a well-known soft computing approach. Four different path representation schemes that begin their coding from the start point and move one grid at a time towards the destination point are proposed. Minimization of traveled distance and maximization of path safety are considered as objectives of this study while path smoothness is considered as a secondary objective. This study makes an extensive analysis of a number of issues related to the optimization of path planning task-handling of constraints associated with the problem, identifying an efficient path representation scheme, handling single versus multiple objectives, and evaluating the proposed algorithm on large-sized grids and having a dense set of obstacles. The study also compares the performance of the proposed algorithm with an existing GA-based approach. The evaluation of the proposed procedure against extreme conditions having a dense (as high as 91 %) placement of obstacles indicates its robustness and efficiency in solving complex path planning problems. The paper demonstrates the flexibility of evolutionary computing approaches in dealing with large-scale and multi-objective optimization problems. |
| Author | Ahmed, Faez Deb, Kalyanmoy |
| Author_xml | – sequence: 1 givenname: Faez surname: Ahmed fullname: Ahmed, Faez organization: Department of Mechanical Engineering, Indian Institute of Technology Kanpur – sequence: 2 givenname: Kalyanmoy surname: Deb fullname: Deb, Kalyanmoy email: deb@iitk.ac.in organization: Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Department of Information and Service Economy, Aalto University School of Economics |
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| Cites_doi | 10.1002/(SICI)1097-4563(199703)14:3<179::AID-ROB3>3.0.CO;2-O 10.1109/TEVC.2007.892759 10.1109/ROBIO.2011.6181426 10.1145/136035.136037 10.1007/BF00339664 10.1109/CEC.2006.1688529 10.1016/0893-6080(94)E0045-M 10.1080/03052159908941294 10.1007/s10514-005-4052-0 10.1007/s00500-006-0068-4 10.1016/S0045-7825(99)00389-8 10.1145/544741.544841 10.1145/359156.359164 10.1115/1.2826954 10.1109/4235.996017 10.1109/CCECE.2004.1345203 10.1017/CBO9780511546877 10.1080/0305215X.2010.548863 10.1177/027836499701600301 10.1007/BF00339662 10.1023/A:1020564024509 10.1177/027836498600500106 10.1007/BF00735436 10.1023/A:1008920117364 10.1109/34.574801 10.1109/ROBOT.1990.126315 |
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| Keywords | Path smoothness NSGA-II Potential field Multi-objective path planning Path length Path safety Genetic algorithms |
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ACM, pp 434–440 GlasiusRKomodaAGielenSNeural network dynamics for path planning and obstacle avoidanceNeural Networks19958112513310.1016/0893-6080(94)E0045-M GeSSCuiYJDynamic motion planning for mobile robots using potential field methodAuton Robots20021332072221030.6867510.1023/A:1020564024509 BisseEBentounesMBoukasEKOptimal path generation for a simulated autonomous mobile robotAuton Robots199521112710.1007/BF00735436 DebKGuptaSUnderstanding knee points in bicriteria problems and their implications as preferred solution principlesEng Optim2011431111751204284538010.1080/0305215X.2010.548863 YangSXMengMReal-time collision-free path planning of robot manipulators using neural network approachesAuton Robots200091273910.1023/A:1008920117364 DebKAn efficient constraint handling method for genetic algorithmsComput Methods Appl Mech Eng20001862–43113381028.9053310.1016/S0045-7825(99)00389-8 ZhangQLiHMOEA/D: a multiobjective evolutionary algorithm based on decompositionIEEE Trans Evol Comput200711671273110.1109/TEVC.2007.892759 HwangYAhujaNGross motion planninga surveyACM Comput Surveys (CSUR)199224321929110.1145/136035.136037 Choset H (1996) Sensor based motion planning: the hierarchical generalized Voronoi graph. 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Soft Computing in Mechatronics, pp 117–128 PratiharDDebKGhoshAFuzzy-genetic algorithms and time-optimal obstacle-free path generation for mobile robotsEng Optim19993211714210.1080/03052159908941294 Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester SugiharaKSmithJGenetic algorithms for adaptive planning of path and trajectory of a mobile robot in 2D terrainsIEEE Trans Inf Syst1999821309317 Gerke M (1999) Genetic path planning for mobile robots. In: Proceedings of the American control conference, vol 4. IEEE, pp 2424–2429 Connolly C, Burns J, Weiss R (1990) Path planning using laplace’s equation. In: Proceedings of IEEE international conference on robotics and automation. IEEE, pp 2102–2106 Burchardt H, Saloman R (2006) Implementation of path planning using genetic algorithms on mobile robots. In: Proceedings of the World Congress on evolutionary computation (WCCI-2006), pp 1831–1836 KanayamaYHartmanBSmooth local-path planning for autonomous vehiclesInt J Robot Res199716326310.1177/027836499701600301 KhatibOReal-time obstacle avoidance for manipulators and mobile robotsInt J Robot Res1986519084680110.1177/027836498600500106 O Castillo (964_CR6) 2005; 6 SS Ge (964_CR17) 2002; 13 E Bisse (964_CR4) 1995; 2 964_CR8 K Deb (964_CR14) 1998; 120 964_CR34 964_CR9 K Deb (964_CR13) 2002; 6 964_CR16 T Lozano-Pérez (964_CR24) 1979; 22 964_CR18 N Ahuja (964_CR2) 1997; 19 D Pratihar (964_CR27) 1999; 32 964_CR30 964_CR11 Q Xue (964_CR31) 2010; 17 SX Yang (964_CR32) 2000; 9 K Deb (964_CR15) 2011; 43 K Deb (964_CR10) 2000; 186 K Deb (964_CR12) 1995; 9 964_CR1 Y Kanayama (964_CR21) 1997; 16 964_CR5 R Glasius (964_CR19) 1995; 8 Y Hwang (964_CR20) 1992; 24 964_CR23 R Murrieta-Cid (964_CR25) 2005; 19 964_CR28 O Khatib (964_CR22) 1986; 5 Q Zhang (964_CR33) 2007; 11 KS Al-Sultan (964_CR3) 2010; 17 O Castillo (964_CR7) 2007; 11 K Sugihara (964_CR29) 1999; 82 G Oriolo (964_CR26) 1997; 14 |
| References_xml | – reference: DebKAgrawalSPratapAMeyarivanTA fast and elitist multi-objective genetic algorithm: NSGA-IIIEEE Trans Evol Comput20026218219710.1109/4235.996017 – reference: DebKGoyalMA robust optimization procedure for mechanical component design based on genetic adaptive searchTrans ASME: J Mech Des1998120216216410.1115/1.2826954 – reference: XueQSheuPCYMaciejewskiAAChienSYPPlanning of collision-free paths for a reconfigurable dual manipulator equipped mobile robotJ Intell Robot Syst201017322324210.1007/BF00339662 – reference: Ahmed F, Deb K (2011) Multi-objective path planning using spline representation. 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