Skip to main content

Advertisement

Log in

Mobile robot path planning with obstacle avoidance using chemical reaction optimization

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The usability of robots is expanding day by day as it is capable of doing complex and hazardous tasks better and faster than human beings. To increase the capacity of robot modernization of its motion is inexorable, and finding the obstacle-free and shortest path for the robot in minimum time becomes essential. To solve this mobile robot path planning problem, many exact, heuristic and metaheuristic algorithms were designed and developed. Here, a metaheuristic algorithm based on chemical reaction optimization (CRO) is proposed to find the obstacle-free minimum path in minimum computational time. To get this outcome, basic operators of CRO are redesigned and two new repair operators have been introduced. These repair operators help to reduce the path length, increase the path smoothness and minimize the number of points in a path, respectively,. They have great influence because the four fundamental operators of CRO are not sufficient enough to produce better results in all situations. To prove the supremacy of the proposed algorithm, the results are compared with ant colony optimization algorithm, probabilistic road map method, particle swarm optimization algorithm and genetic algorithm. The comparison shows that the proposed algorithm has the best results in the improvement of path length, smoothness and execution time. The superiority of the proposed algorithm over the compared algorithms has been proven using a statistical test. Besides this, the empirical outcomes of 10 complex maps are revealed in this research work for which nobody did any experiment in our conjecture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  • Al-Jarrah R, Shahzad A, Roth H (2015) Path planning and motion coordination for multi-robots system using probabilistic neuro-fuzzy. IFAC Pap OnLine 48(10):46–51

    Article  Google Scholar 

  • Bhattacharjee A, Mannan SR, Islam MR (2018) Phylogenetic tree construction using chemical reaction optimization. In: International conference on intelligent systems design and applications, Springer, pp 915–924

  • 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), IEEE, vol 3, 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

    Article  Google Scholar 

  • Chiang HT, Malone N, Lesser K, Oishi M, Tapia L (2015) Path-guided artificial potential fields with stochastic reachable sets for motion planning in highly dynamic environments. In: 2015 IEEE international conference on robotics and automation (ICRA), IEEE, pp 2347–2354

  • 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 

  • Davoodi M, Panahi F, Mohades A, Hashemi SN (2015) Clear and smooth path planning. Appl Soft Comput 32:568–579

    Article  Google Scholar 

  • Duan H, Huang L (2014) Imperialist competitive algorithm optimized artificial neural networks for ucav global path planning. Neurocomputing 125:166–171

    Article  Google Scholar 

  • Ghita N, Kloetzer M (2012) Trajectory planning for a car-like robot by environment abstraction. Robot Auton Syst 60(4):609–619

    Article  Google Scholar 

  • Han J, Seo Y (2017) Mobile robot path planning with surrounding point set and path improvement. Appl Soft Comput 57:35–47

    Article  Google Scholar 

  • Islam MR, Arif IH, Shuvo RH (2019a) Generalized vertex cover using chemical reaction optimization. Appl Intel 49(7):2546–2566

    Article  Google Scholar 

  • Islam MR, Islam MS, Sakeef N (2019b) Rna secondary structure prediction with pseudoknots using chemical reaction optimization algorithm. IEEE/ACM transactions on computational biology and bioinformatics

  • Islam MR, Smrity RA, Chatterjee S, Mahmud MR (2019c) Optimization of protein folding using chemical reaction optimization in hp cubic lattice model. In: Neural computing and applications, pp 1–18

  • James J, Lam AY, Li VO (2011) (2011) Evolutionary artificial neural network based on chemical reaction optimization. In: IEEE congress on evolutionary computation (CEC), IEEE, pp 2083–2090

  • Kala R, Shukla A, Tiwari R (2011) Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness. Neurocomputing 74(14–15):2314–2335

    Article  Google Scholar 

  • Karami AH, Hasanzadeh M (2015) An adaptive genetic algorithm for robot motion planning in 2d complex environments. Comput Electr Eng 43:317–329

    Article  Google Scholar 

  • Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning, Springer, pp 760–766

  • Ladd AM, Kavraki LE (2004) Measure theoretic analysis of probabilistic path planning. IEEE Trans Robot Autom 20(2):229–242

    Article  Google Scholar 

  • Lam A, Xu J, Li V (2010) Chemical reaction optimization for population transition in

  • Lam AY, Li VO (2012) Chemical reaction optimization: a tutorial. Memetic Comput 4(1):3–17

    Article  Google Scholar 

  • Lamini C, Benhlima S, Elbekri A (2018) Genetic algorithm based approach for autonomous mobile robot path planning. Proc Comput Sci 127:180–189

    Article  Google Scholar 

  • Liang Y, Xu L (2009) 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, ACM, pp 303–308

  • Mac TT, Copot C, Tran DT, De Keyser R (2017) A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl Soft Comput 59:68–76

    Article  Google Scholar 

  • 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

  • Nazif AN, Davoodi A, Pasquier P (2010) Multi-agent area coverage using a single query roadmap: A swarm intelligence approach. In: Advances in practical multi-agent systems, Springer, pp 95–112

  • Pan B, Lam AY, Li VO (2011) Network coding optimization based on chemical reaction optimization. In: 2011 IEEE global telecommunications conference-GLOBECOM 2011, IEEE, pp 1–5

  • Tang B, Zhu Z, Luo J (2016) Hybridizing particle swarm optimization and differential evolution for the mobile robot global path planning. Int J Adv Robot Syst 13(3):86

    Article  Google Scholar 

  • Tuncer A, Yildirim M (2012) Dynamic path planning of mobile robots with improved genetic algorithm. Comput Electr Eng 38(6):1564–1572

    Article  Google Scholar 

  • Wadud MS, Islam MR, Kundu N, Kabir MR (2018) Multiple sequence alignment using chemical reaction optimization algorithm. In: International conference on intelligent systems design and applications, Springer, pp 1065–1074

  • Zhang Y, Gong DW, Zhang JH (2013) Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103:172–185

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Md. Rafiqul Islam.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Islam, M.R., Protik, P., Das, S. et al. Mobile robot path planning with obstacle avoidance using chemical reaction optimization. Soft Comput 25, 6283–6310 (2021). https://doi.org/10.1007/s00500-021-05615-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05615-6

Keywords

Navigation