Abstract
The goal of mobile robot path planning (MRPP) is to generate a collision-free path while considering certain objective functions, such as path length and smoothness. Nowadays, the application areas of mobile robots have widened significantly. So, it is essential to develop algorithms that will allow them to maneuver effectively in complex environments with obstacles. In this paper, we have proposed a new methodology based on a metaheuristic approach named chemical reaction optimization (CRO) to solve this NP-hard problem. We have reconstructed the four fundamental operators and designed two new repair operators to obtain better performance. The results of our proposed algorithm are compared with ant colony optimization algorithm (ACO), particle swarm optimization (PSO) algorithm, and genetic algorithm (GA) to show its efficiency in terms of solution quality and computational time.
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References
Al-Jarrah R, Shahzad A, Roth H (2015) Path planning and motion coordination for multi-robots system using probabilistic neuro-fuzzy. IFAC-PapersOnLine 48(10):46–51
Brand M, Masuda M, Wehner N, Yu XH (2010) Ant colony optimization algorithm for robot path planning. In: 2010 International Conference on Computer Design and Applications (ICCDA), vol 3. IEEE, pp V3–436
Chen X, Kong Y, Fang X, Wu Q (2013) A fast two-stage aco algorithm for robotic path planning. Neural Comput Appl 22(2):313–319
Davoodi M, Panahi F, Mohades A, Hashemi SN (2015) Clear and smooth path planning. Appl Soft Comput 32:568–579
Han J, Seo Y (2017) Mobile robot path planning with surrounding point set and path improvement. Appl Soft Comput 57:35–47
Islam MR, Saifullah CK, Asha ZT, Ahamed R (2018) Chemical reaction optimization for solving longest common subsequence problem for multiple string. Soft Comput 1–25. https://doi.org/10.1007/s00500-018-3200-3
James J, Lam AY, Li VO (2011) Evolutionary artificial neural network based on chemical reaction optimization. In: 2011 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 2083–2090 (2011)
Kabir R, Islam R (2018) Chemical reaction optimization for RNA structure prediction. Appl Intell 1–24. https://doi.org/10.1007/s10489-018-1281-4
Karami AH, Hasanzadeh M (2015) An adaptive genetic algorithm for robot motion planning in 2d complex environments. Comput Electr Eng 43:317–329
Nasrollahy AZ, Javadi HHS (2009) Using particle swarm optimization for robot path planning in dynamic environments with moving obstacles and target. In: 2009 Third UKSim European symposium on computer modeling and simulation. IEEE, pp. 60–65
Saifullah CK, Islam MR (2016) Chemical reaction optimization for solving shortest common supersequence problem. Comput Biol Chem 64:82–93. https://doi.org/10.1016/j.compbiolchem.2016.05.004
Truong TK, Li K, Xu Y (2013) Chemical reaction optimization with greedy strategy for the 0–1 knapsack problem. Appl Soft Comput 13(4):1774–1780
Tuncer A, Yildirim M (2012) Dynamic path planning of mobile robots with improved genetic algorithm. Comput Electr Eng 38(6):1564–1572
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Protik, P., Das, S., Rafiqul Islam, M. (2020). Chemical Reaction Optimization for Mobile Robot Path Planning. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3607-6_15
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DOI: https://doi.org/10.1007/978-981-15-3607-6_15
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Online ISBN: 978-981-15-3607-6
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