Abstract
The global path planning problem is very challenging NP-complete problem in the domain of robotics. Many metaheuristic approaches have been developed up to date, to provide an optimal solution to this problem. In this work we present a novel Quad-Harmony Search (QHS) algorithm based on Quad-tree free space decomposition methodology and Harmony Search optimization. The developed algorithm has been evaluated on various grid based environments with different percentage of obstacle coverage. The results have demonstrated that it is superior in terms of time and optimality of the solution compared to other known metaheuristic algorithms.
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References
Lau, B., Sprunk, C., Burgard, W.: Efficient grid-based spatial representations for robot navigation in dynamic environments. Robotics and Autonomous Systems (2012)
Aarts, E.H., Korst, J., Van Laarhoven, P.J.: Simulated annealing. Local Search in Combinatorial Optimization, 91–120 (1997)
Ho, Y.J., Liu, J.S.: Simulated annealing based algorithm for smooth robot path planning with different kinematic constraints. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1277–1281. ACM (2010)
Liang, Y., Xu, L.: Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm. In: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 303–308. ACM (2009)
Du, Z.Z., Liu, G.D.: Path Planning of Mobile Robot Based on Genetically Simulated Annealing Algorithm 12, 36 (2009)
Wang, H.B., Yang, W.J., Wang, J.H.: Research on Path Planning for Mobile Robot Based on Grid and Hybrid of GA/SA, vol. 479, pp. 1499–1503. Trans. Tech. Publ. (2012)
Hussein, A., Mostafa, H., Badrel-din, M., Sultan, O., Khamis, A.: Metaheuristic optimization approach to mobile robot path planning. In: 2012 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE (2012)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intelligence 1(1), 33–57 (2007)
Liu, W.K.Z.H.Y., Zhi-lei, C.: Path Planning for Robots Based on Quantum-behaved Particle Swarm Optimization. Microcomputer Information 11, 066 (2010)
Huang, H.C., Tsai, C.C.: Global path planning for autonomous robot navigation using hybrid metaheuristic GA-PSO algorithm. In: 2011 Proceedings. SICE Annual Conference (SICE), pp. 1339–1343. IEEE (2011)
Qian-Zhi, M., Xiu-Juan, L.: The application of hybrid orthogonal particle swarm optimization in robotic path planning. In: 2010 Sixth International Conference on Natural Computation (ICNC), vol. 7, pp. 3536–3540. IEEE (2010)
Li, W., Wang, G.Y.: Application of improved PSO in mobile robotic path planning. In: 2010 International Conference on Intelligent Computing and Integrated Systems (ICISS), pp. 45–48. IEEE (2010)
Ma, Y., Zamirian, M., Yang, Y., Xu, Y., Zhang, J.: Path Planning for Mobile Objects in Four-Dimension Based on Particle Swarm Optimization Method with Penalty Function. Mathematical Problems in Engineering (2013)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)
Chaari, I., Koubaa, A., Bennaceur, H., Trigui, S., Al-Shalfan, K.: smartPATH: A hybrid ACO-GA algorithm for robot path planning. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)
Xianlun, T.A.N.G., et al.: Ant Colony Optimization Based on Maximum Selection Probability for Path Planning in Unknown Environment. Journal of Computational Information Systems 8(24), 10325–10332 (2012)
Wang, P.D., Tang, G.Y., Li, Y., Yang, X.X.: Ant colony algorithm using endpoint approximation for robot path planning, pp. 4960–4965. IEEE (2012)
Luo, D.L., Wu, S.X.: Ant colony optimization with potential field heuristic for robot path planning. Systems Engineering and Electronics 32(6), 1277–1280 (2010)
Wu, Y.F., Zhang, X.X., Wu, J.Q.: Using Cellular Ant Colony Algorithm for Path-Planning of Robots. Applied Mechanics and Materials 182, 1776–1780 (2012)
Qiao, R., Zhang, X.B., Guang-xing, Z.H.A.O.: Global Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm. Journal of Anhui University of Technology (Natural Science) 1 (2009)
Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning (1989)
Lucas, D., Crane, C.: Development of a multi-resolution parallel genetic algorithm for autonomous robotic path planning. In: 2012 12th International Conference on Control, Automation and Systems (ICCAS), pp. 1002–1006 (2012)
Liu, C., et al.: Dynamic path planning for mobile robot based on improved genetic algorithm. Chinese Journal of Electronics 19(2), 2010–2014 (2010)
Hua, J.M.W.H.Z., Xingzhe, X.: Applying improved genetic algorithm to global path planning for mobile robot. Computer Applications and Software 8, 033 (2011)
Yan, X.: An Improved Robot Path Planning Algorithm. TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 10(4), 629–636 (2012)
Xu, X., Xie, J., Xie, K.: Path planning and obstacle-avoidance for soccer robot based on artificial potential field and genetic algorithm. In: The Sixth World Congress Intelligent Control and Automation, WCICA 2006, vol. 1, pp. 3494–3498 (2006)
Schrijver, A.: Theory of linear and integer programming. Wiley (1998)
Geem, Z.W., Kim, J.H., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)
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Panov, S., Koceska, N. (2014). Global Path Planning in Grid-Based Environments Using Novel Metaheuristic Algorithm. In: Trajkovik, V., Anastas, M. (eds) ICT Innovations 2013. ICT Innovations 2013. Advances in Intelligent Systems and Computing, vol 231. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01466-1_11
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DOI: https://doi.org/10.1007/978-3-319-01466-1_11
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-01465-4
Online ISBN: 978-3-319-01466-1
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