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Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

Published online by Cambridge University Press:  24 February 2021

Saroj Kumar Open the ORCID record for Saroj Kumar [Opens in a new window] ,
Dayal Ramakrushna Parhi ,
Krishna Kant Pandey Open the ORCID record for Krishna Kant Pandey [Opens in a new window]  and
Manoj Kumar Muni Open the ORCID record for Manoj Kumar Muni [Opens in a new window]
Saroj Kumar*
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India 769008
Dayal Ramakrushna Parhi
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India 769008
Krishna Kant Pandey
Affiliation:
Department of Mechanical Engineering, G.H. Raisoni Institute of Engineering and Technology, Pune, Maharashtra, India 412207
Manoj Kumar Muni
Affiliation:
Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India 759146
*
*Corresponding author. E-mail: saroj4sks@gmail.com
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Summary

In this article, hybridization of IWD (intelligent water drop) and GA (genetic algorithm) technique is developed and executed in order to obtain global optimal path by replacing local optimal points. Sensors of mobile robots are used for mapping and detecting the environment and obstacles present. The developed technique is tested in MATLAB simulation platform, and experimental analysis is performed in real-time environments to observe the effectiveness of IWD-GA technique. Furthermore, statistical analysis of obtained results is also performed for testing their linearity and normality. A significant improvement of about 13.14% in terms of path length is reported when the proposed technique is tested against other existing techniques.

Keywords

Path optimizationNavigationIWD-GAMobile robotArtificial intelligence

Information

Type
Article
Information
Robotica , Volume 39 , Issue 11 , November 2021 , pp. 2033 - 2060
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Salmanpour, S., Omranpour, H. and Motameni, H., “An intelligent water drops algorithm for solving robot path planning problem,” IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI), (2013) pp. 333–338.Google Scholar
Duan, H., Liu, S and Wu, J., “Novel intelligent water drops optimization approach to single UCAV smooth trajectory planning,” Aerosp. Sci. Tech. 13(8), 442–449 (2009).10.1016/j.ast.2009.07.002CrossRefGoogle Scholar
Shah-Hosseini, H., “An approach to continuous optimization by the intelligent water drops algorithm,” Procedia Soc. Behav. Sci., 32, 224–229 (2012).10.1016/j.sbspro.2012.01.033CrossRefGoogle Scholar
Alijla, B. O., Wong, L. P., Lim, C. P., Khader, A. T. and Al-Betar, M. A., “An ensemble of intelligent water drop algorithms and its application to optimization problems,” Inf. Scie. 325, 175–189 (2015).CrossRefGoogle Scholar
Alijla, B. O., Wong, L. P., Lim, C. P., Khader, A. T. and Al-Betar, M. A., “A modified intelligent water drops algorithm and its application to optimization problems,” Expert Syst. Appl. 41(15), 6555–6569 (2014).CrossRefGoogle Scholar
Kaur, I., Kaur, P. and Verma, A., “Natural terrain feature identification using integrated approach of cuckoo search and intelligent water drops algorithm,” Int. J. Comput. Sci. Inf. Sec. 15(2), 199 (2017).Google Scholar
Shah-Hosseini, H., “The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm,” Int. J. Bio-Inspir. Comput. 1(1–2), 71–79 (2009).CrossRefGoogle Scholar
Shah-Hosseini, H., “An approach to continuous optimization by the intelligent water drops algorithm,” Procedia Soc. Behav. Sci. 32, 224–229 (2012).CrossRefGoogle Scholar
Salmanpour, S., Monfared, H. and Omranpour, H, “Solving robot path planning problem by using a new elitist multi-objective IWD algorithm based on coefficient of variation,” Soft Com. 21(11), 3063–3079 (2017).CrossRefGoogle Scholar
Salmanpour, S. and Motameni, H., “Optimal path planning for mobile robot using Intelligent Water Drops algorithm,” J. Intell. Fuzzy Syst. 27(3), 1519–1531 (2014).CrossRefGoogle Scholar
Bansal, R., Agrawal, H., Afaq, H. and Saini, S., “Implementation of Intelligent Water Drops Algorithm to Solve Graph-Based Travelling Salesman Problem,” In Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28–30, (2014) pp. 137–143.Google Scholar
Rao, D. C., Kabat, M. R., Das, P. K. and Jena, P. K., “Hybrid IWD-DE: a novel approach to model cooperative navigation planning for multi-robot in unknown dynamic environment,” J. of Bionic Eng. 16(2), 235–252 (2019).CrossRefGoogle Scholar
Xue, X. D., Xu, B., Wang, H. L. and Jiang, C. P., “The basic principle and application of ant colony optimization algorithm,” In 2010 International Conference on Artificial Intelligence and Education (ICAIE), (2010) pp. 358–360.CrossRefGoogle Scholar
Chen, X., Zhang, P., Du, G. and Li, F., “Ant colony optimization based memetic algorithm to solve bi-objective multiple traveling salesmen problem for multi-robot systems,” IEEE Access 6, 21745–21757 (2018).CrossRefGoogle Scholar
Ismkhan, H., “Effective heuristics for ant colony optimization to handle large-scale problems,” Swarm Evol. Comput. 32, 140–149 (2017).CrossRefGoogle Scholar
Priyambodo, T. K. and Suhendra, T., “Moving robot path planning algorithm analysis on dynamic environment based difference method update on ant colony algorithm,” In 2018 4th International Conference on Science and Technology (ICST), (2018) pp. 1–6.Google Scholar
Yang, H., Qi, J., Miao, Y., Sun, H. and Li, J., “A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization,” IEEE Trans. Ind. Electron. 66(11), 8557–8566 (2018).CrossRefGoogle Scholar
Parhi, D. R. and Mohanty, P. K., “IWO-based adaptive neuro-fuzzy controller for mobile robot navigation in cluttered environments,” Int. J. Adv. Manuf. Tech. 83(9–12), 1607–1625 (2016).CrossRefGoogle Scholar
Patle, B. K., Parhi, D. R. K., Jagadeesh, A. and Kashyap, S. K., “Application of probability to enhance the performance of fuzzy based mobile robot navigation,” Appl. Soft Comput. 75, 265–283 (2019).10.1016/j.asoc.2018.11.026CrossRefGoogle Scholar
Pandey, A., Sonkar, R. K., Pandey, K. K. and Parhi, D. R., “Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller,” In 2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO), (2014) pp. 39–41.Google Scholar
Parhi, D. R. and Chhotray, A., “Development and analysis of DAYANI arc contour intelligent technique for navigation of two-wheeled mobile robot,” Ind. Rob.: Int. J. 45(5), 688–702 (2018).CrossRefGoogle Scholar
Pandey, A., Kumar, S., Pandey, K. K. and Parhi, D. R., “Mobile robot navigation in unknown static environments using ANFIS controller,” Perspect. Sci. 8, 421–423 (2016).10.1016/j.pisc.2016.04.094CrossRefGoogle Scholar
Tan, P. and Cai, Z., “Modelling and planning of mobile robot navigation control in unknown environment,” In 2015 International Conference on Computational Intelligence and Communication Networks (CICN), (2015) pp. 1532–1536.Google Scholar
Li, G. and Chou, W., “Path planning for mobile robot using self-adaptive learning particle swarm optimization,” Sci. China Inf. Sci. 61(5), 052204 (2018).CrossRefGoogle Scholar
Patle, B. K., Parhi, D. R. K., Jagadeesh, A. and Kashyap, S. K., “Matrix-Binary Codes based Genetic Algorithm for path planning of mobile robot,” Comput. Electr. Eng. 67, 708–728 (2018).CrossRefGoogle Scholar
Kumar, P. B., Sahu, C. and Parhi, D. R., “A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment,” Appl. Soft Comput. 68, 565–585 (2018).CrossRefGoogle Scholar
Nazarahari, M., Khanmirza, E. and Doostie, S., “Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm,” Expert Syst. Appl. 115, 106–120 (2019).10.1016/j.eswa.2018.08.008CrossRefGoogle Scholar
Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O. and Bouzouia, B., “Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control,” Rob. Auton. Syst. 89, 95–109 (2017).CrossRefGoogle Scholar
Lamini, C., Benhlima, S. and Elbekri, A., “Genetic algorithm based approach for autonomous mobile robot path planning,” Procedia Comput. Sci. 127, 180–189 (2018).10.1016/j.procs.2018.01.113CrossRefGoogle Scholar
Liang, J. H. and Lee, C. H., “Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm,” Adv. Eng. Softw. 79, 47–56 (2015).CrossRefGoogle Scholar
Pandey, A. and Parhi, D. R., “Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm,” Def. Tech. 13(1), 47–58 (2017).CrossRefGoogle Scholar
Qu, H., Xing, K. and Alexander, T., “An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots,” Neurocomputing 120, 509–517 (2013).CrossRefGoogle Scholar
Tuncer, A. and Yildirim, M., “Dynamic path planning of mobile robots with improved genetic algorithm,” Comput. Electr. Eng. 38(6), 1564–1572 (2012).10.1016/j.compeleceng.2012.06.016CrossRefGoogle Scholar
Mandava, R. K., Bondada, S. and Vundavilli, P. R., “An optimized path planning for the mobile robot using potential field method and PSO algorithm,” In Soft Computing for Problem Solving, (2019) pp. 139–150.CrossRefGoogle Scholar
Mohanta, J. C. and Keshari, A., “A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation,” Appl. Soft Comput. 79, 391–409 (2019).CrossRefGoogle Scholar
Rath, A. K., Parhi, D. R., Das, H. C., Muni, M. K. and Kumar, P. B., “Analysis and use of fuzzy intelligent technique for navigation of humanoid robot in obstacle prone zone,” Def. Tech. 14(6), 677–682 (2018).CrossRefGoogle Scholar
Rath, A. K., Parhi, D. R., Das, H. C., Kumar, P. B., Muni, M. K. and Salony, K., “Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm,” Int. J. Intell. Unmanned Syst. 7(3), 112–119 (2019).CrossRefGoogle Scholar
Pandey, K. K. and Parhi, D. R., “Trajectory planning and the target search by the mobile robot in an environment using a behavior-based neural network approach,” Robotica, (2019) pp. 1–15.Google Scholar
Panahandeh, P., Alipour, K., Tarvirdizadeh, B. and Hadi, A., “A self-tuning trajectory tracking controller for wheeled mobile robots,” Ind. Rob.: Int. J. Rob. Res. Appl. 46(6), 828–838 (2019).CrossRefGoogle Scholar
Li, L., Xiao, J., Zou, Y. and Zhang, T., “Time-optimal path tracking for robots a numerical integration-like approach combined with an iterative learning algorithm,” Ind. Rob.: Int. J. Rob. Res. Appl. 46(6), 763–778 (2019).CrossRefGoogle Scholar
Chen, G. and Liu, J., “Mobile robot path planning using ant colony algorithm and improved potential field method,” Comput. Intell. Neurosci. 10 (2019), Article ID 1932812. https://doi.org/10.1155/2019/1932812.Google Scholar
Ajeil, F. H., Ibraheem, I. K., Sahib, M. A. and Humaidi, A. J., “Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm,” Appl. Soft Comput. 89, 106076 (2020).CrossRefGoogle Scholar
Holland, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. (MIT Press, Cambridge, MA, 1992).Google Scholar
Goldberg, D. E., “Genetic and evolutionary algorithms come of age,” Commun. ACM 37(3), 113–120 (1994).CrossRefGoogle Scholar
Mohanta, J. C. and Keshari, A., “A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation,” Appl. Soft Comput. 79, 391–409 (2019).CrossRefGoogle Scholar
Low, E. S., Ong, P. and Cheah, K. C., “Solving the optimal path planning of a mobile robot using improved Q-learning,” Rob. Auton. Syst. 115, 143–161 (2019).CrossRefGoogle Scholar
Ni, J., Yin, X., Chen, J. and Li, X., “An improved shuffled frog leaping algorithm for robot path planning,” In 2014 10th International Conference on Natural Computation (ICNC), (2014) pp. 545–549.Google Scholar
Kumar, S., Parhi, D. R., Muni, M. K. and Pandey, K. K., “Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique,” Ind. Rob. 47(4), 535–545 (2020).CrossRefGoogle Scholar
Orozco-Rosas, U., Montiel, O. and Sepúlveda, R., “Mobile robot path planning using membrane evolutionary artificial potential field,” Appl. Soft Comput. 77, 236–251 (2019).CrossRefGoogle Scholar
Montiel, O., Orozco-Rosas, U. and Sepúlveda, R., “Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles,” Expert Syst. Appl. 42(12), 5177–5191 (2015).CrossRefGoogle Scholar
Montiel-Ross, O., Sepúlveda, R., Castillo, O. and Melin, P., “Ant colony test center for planning autonomous mobile robot navigation,” Comput. Appl. Eng. Educ. 21(2), 214–229 (2013).CrossRefGoogle Scholar
Garcia, M. P., Montiel, O., Castillo, O., Sepúlveda, R. and Melin, P., “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Appl. Soft Comput. 9(3), 1102–1110 (2009).CrossRefGoogle Scholar
Castillo, O., Trujillo, L. and Melin, P., “Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots,” Soft Comput. 11(3), 269–279 (2007).CrossRefGoogle Scholar
Pandey, A., Panwar, V. S., Hasan, M. E. and Parhi, D. R., “V-REP-based navigation of automated wheeled robot between obstacles using PSO-tuned feedforward neural network,” J. Comput. Des. Eng. 7(4), 427–434 (2020).Google Scholar
Pandey, A., Kashyap, A. K., Parhi, D. R. and Patle, B. K., “Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture,” World J. Eng. Emerald 16(2), 275–286, 2019.CrossRefGoogle Scholar
Kumar, S., Parhi, D. R., Kashyap, A. K. and Muni, M. K., “Static and dynamic path optimization of multiple mobile robot using hybridized fuzzy logic-whale optimization algorithm,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 0954406220982641. https://doi.org/10.1177/0954406220982641 CrossRefGoogle Scholar
Muni, M. K., Parhi, D. R., Kumar, P. B. and Kumar, S., “Motion control of multiple humanoids using a hybridized prim’s algorithm-fuzzy controller,” Soft Comput. 25, 1159–1180 (2021).CrossRefGoogle Scholar