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Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming

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Abstract

As path planning is an NP-hard problem it can be solved by multi-objective algorithms. In this article, we propose a multi-objective path planning algorithm which consists of three steps: (1) the first step consists of optimizing a path by the hybridization of the Grey Wolf optimizer-particle swarm optimization algorithm, it minimizes the path distance and smooths the path. (2) the second step, all optimal and feasible points generated by PSO–GWO algorithm are integrated with Local Search technique to convert any infeasible point into feasible point solution, the last step (3) depends on collision avoidance and detection algorithm, where mobile robot detects the presence of an obstacle in its sensing circle and then avoid them using collision avoidance algorithm. The proposed method is further improved by adding the mutation operators by evolutionary, it further solves path safety, length, and smooths it further for a mobile robot. Different simulations have been performed under numerous environments to test the feasibility of the proposed algorithm and it is shown the algorithm produces a more feasible path with a short distance and thus proves that it overcomes the shortcomings of other conventional techniques.

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References

  • Abbas NH, Ali FM (2014) Path planning of an autonomous mobile robot using directed artificial bee colony algorithm. Int J Comput Appl 96(11):11–16

  • Achour N, Chaalal M (2011) Mobile robots path planning using genetic algorithms. In: The seventh international conference on autonomic and autonomous systems, pp 111–115

  • Ahmed F, Deb K (2013) Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Comput 17(7):1283–1299

    Article  Google Scholar 

  • Ajeil FH, Ibraheem IK, Sahib MA, Humaidi AJ (2020) Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm. Appl Soft Comput 89:106076

    Article  Google Scholar 

  • Albina K, Lee SG (2019) Hybrid stochastic exploration using grey wolf optimizer and coordinated multi-robot exploration algorithms. IEEE Access 7:14246–14255

    Article  Google Scholar 

  • Algfoor ZA, Sunar MS, Kolivand H (2015) A comprehensive study on pathfinding techniques for robotics and video games. Int J Comput Games Technol 2015:7

    Google Scholar 

  • Alomari A, Phillips W, Aslam N, Comeau F (2017) Dynamic fuzzy-logic based path planning for mobility-assisted localization in wireless sensor networks. Sensors 17(8):1904

    Article  Google Scholar 

  • Alomari A, Phillips W, Aslam N, Comeau F (2018) Swarm intelligence optimization techniques for obstacle-avoidance mobility-assisted localization in wireless sensor networks. IEEE Access 6:22368–22385

    Article  Google Scholar 

  • Ayawli BBK, Chellali R, Appiah AY, Kyeremeh F (2018) An overview of nature-inspired, conventional, and hybrid methods of autonomous vehicle path planning. J Adv Transp. https://doi.org/10.1155/2018/8269698

  • Canny J (1988) The complexity of robot motion planning. MIT Press, Cambridge

    MATH  Google Scholar 

  • Chen X, Gao P (2019) Path planning and control of soccer robot based on genetic algorithm. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01635-1

    Article  Google Scholar 

  • Chen Y, Lu S, Chen J, Ren T (2017) Node localization algorithm of wireless sensor networks with mobile beacon node. Peer Peer Netw Appl 10(3):795–807

    Article  Google Scholar 

  • Choset HM, Hutchinson S, Lynch KM, Kantor G, Burgard W, Kavraki LE, Thrun S (2005) Principles of robot motion: theory, algorithms, and implementation. MIT Press, Cambridge

    MATH  Google Scholar 

  • Ciabattoni L, Foresi G, Monteriù A, Pepa L, Pagnotta DP, Spalazzi L, Verdini F (2019) Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and ble beacons. J Ambient Intell Humaniz Comput 10(1):1–12

    Article  Google Scholar 

  • Contreras-Cruz MA, Ayala-Ramirez V, Hernandez-Belmonte UH (2015) Mobile robot path planning using artificial bee colony and evolutionary programming. Appl Soft Comput 30:319–328

    Article  Google Scholar 

  • Dao TK, Pan TS, Pan JS (2016) A multi-objective optimal mobile robot path planning based on whale optimization algorithm. In: 2016 IEEE 13th international conference on signal processing (ICSP). IEEE, pp 337–342

  • Deepak B, Parhi DR, Raju B (2014) Advance particle swarm optimization-based navigational controller for mobile robot. Arab J Sci Eng 39(8):6477–6487

    Article  Google Scholar 

  • Dewang HS, Mohanty PK, Kundu S (2018) A robust path planning for mobile robot using smart particle swarm optimization. Procedia Comput Sci 133:290–297

    Article  Google Scholar 

  • Dw Gong, Zhang Jh, Zhang Y (2011) Multi-objective particle swarm optimization for robot path planning in environment with danger sources. J Comput 6(8):1554–1561

    Google Scholar 

  • Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30(2):413–435

    Article  Google Scholar 

  • Fogel LJ (1999) Intelligence through simulated evolution: forty years of evolutionary programming. Wiley, New York

    MATH  Google Scholar 

  • Gul F, Rahiman W, Nazli Alhady SS (2019) A comprehensive study for robot navigation techniques. Cogent Eng 6(1):1632046

    Article  Google Scholar 

  • Hershberger J, Kumar N, Suri S (2020) Shortest paths in the plane with obstacle violations. Algorithmica 82:1813–1832

  • Hossain MA, Ferdous I (2015) Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique. Robot Auton Syst 64:137–141

    Article  Google Scholar 

  • Huang HC, Tsai CC (2011) Global path planning for autonomous robot navigation using hybrid metaheuristic GA-PSO algorithm. In: SICE annual conference 2011. IEEE, pp 1338–1343

  • Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceedings of 1997 IEEE international conference on evolutionary computation (ICEC'97). IEEE, pp 303–308

  • Kennedy J, Eberhart RC (2010) Particle swarm optimization. Encycl Mach Learn 4:760–766

    Google Scholar 

  • Konar A, Chakraborty IG, Singh SJ, Jain LC, Nagar AK (2013) A deterministic improved q-learning for path planning of a mobile robot. IEEE Trans Syst Man Cybern Syst 43(5):1141–1153

    Article  Google Scholar 

  • LaValle SM (2006) Planning algorithms. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Mac TT, Copot C, Tran DT, De Keyser R (2016) Heuristic approaches in robot path planning: a survey. Robot Auton Syst 86:13–28

    Article  Google Scholar 

  • Masehian E, Sedighizadeh D (2007) Classic and heuristic approaches in robot motion planning—a chronological review. World Acad Sci Eng Technol 23(5):101–106

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119

    Article  Google Scholar 

  • Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput 2016:8

    Google Scholar 

  • Pandey HM (2019) A modified whale optimization algorithm with multi-objective criteria for optimal robot path planning

  • Parhi DR, Mohanty PK (2016) Iwo-based adaptive neuro-fuzzy controller for mobile robot navigation in cluttered environments. Int J Adv Manuf Technol 83(9–12):1607–1625

    Article  Google Scholar 

  • Rainer JJ, Cobos-Guzman S, Galán R (2018) Decision making algorithm for an autonomous guide-robot using fuzzy logic. J Ambient Intell Humaniz Comput 9(4):1177–1189

    Article  Google Scholar 

  • Rajpurohit J, Sharma TK, Abraham A, Vaishali A (2017) Glossary of metaheuristic algorithms. Int J Comput Inf Syst Ind Manag Appl 9:181–205

    Google Scholar 

  • Rath MK, Deepak BBVL (2015) PSO based system architecture for path planning of mobile robot in dynamic environment. In: 2015 global conference on communication technologies (GCCT). IEEE, pp 797–801

  • Salmanpour S, Monfared H, Omranpour H (2017) Solving robot path planning problem by using a new elitist multi-objective IWD algorithm based on coefficient of variation. Soft Comput 21(11):3063–3079

    Article  Google Scholar 

  • Saraswathi M, Murali GB, Deepak B (2018) Optimal path planning of mobile robot using hybrid cuckoo search-bat algorithm. Procedia Comput Sci 133:510–517

    Article  Google Scholar 

  • Sedighi KH, Ashenayi K, Manikas TW, Wainwright RL, Tai HM (2004) Autonomous local path planning for a mobile robot using a genetic algorithm. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753), vol 2. IEEE, pp 1338–1345

  • Sierakowski CA, Coelho LDS (2005) Study of two swarm intelligence techniques for path planning of mobile robots. In: 16th IFAC world congress, Prague

  • Singh N, Singh SB (2017) Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance. J Appl Math. https://doi.org/10.1155/2017/2030489

  • Sombolestan S, Rasooli A, Khodaygan S (2019) Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning. J Ambient Intell Humaniz Comput 10(5):1841–1850

    Article  Google Scholar 

  • Teimoori H, Savkin AV (2010) A biologically inspired method for robot navigation in a cluttered environment. Robotica 28(5):637–648

    Article  Google Scholar 

  • Tsai CC, Huang HC, Chan CK (2011) Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans Ind Electron 58(10):4813–4821

    Article  Google Scholar 

  • Wang G, Guo L, Duan H, Liu L, Wang H (2012) A bat algorithm with mutation for UCAV path planning. Sci World J. https://doi.org/10.1100/2012/418946

  • Wang W, Cao M, Ma S, Ren C, Zhu X, Lu H (2016) Multi-robot odor source search based on cuckoo search algorithm in ventilated indoor environment. In: 2016 12th world congress on intelligent control and automation (WCICA). IEEE, pp 1496–1501

  • Xiao J, Michalewicz Z, Zhang L, Trojanowski K (1997) Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans Evol Comput 1(1):18–28

    Article  Google Scholar 

  • Zhu Z, Xiao J, Li JQ, Wang F, Zhang Q (2015) Global path planning of wheeled robots using multi-objective memetic algorithms. Integr Comput Aided Eng 22(4):387–404

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Universiti Sains Malaysia under the Bridging Grant (303\PELECT\6316121).

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Correspondence to Wan Rahiman.

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Gul, F., Rahiman, W., Alhady, S.S.N. et al. Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming. J Ambient Intell Human Comput 12, 7873–7890 (2021). https://doi.org/10.1007/s12652-020-02514-w

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