[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

A New Adaptive Differential Evolution Algorithm Fused with Multiple Strategies for Robot Path Planning

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In a complex environment with multiple feasible paths, planning a globally optimal path and performing dynamic path planning for robots is challenging. Therefore, this paper proposed a new adaptive Differential Evolution algorithm combined with K-modes clustering, BP neural network, and other strategies (KBPDE) to overcome this issue. The proposed KBPDE algorithm applies a new population initialization strategy based on K-modes clustering and a two-population strategy to enhance the population diversity and prevent the algorithm from falling into local optima. A BP neural network model is developed to obtain the appropriate mutation scale factor F at each generation G. Based on the positional relationships among individuals in the current generation, a novel mutation strategy is presented. Finally, an adaptive population size strategy is introduced to raise the algorithm’s efficiency. In a complicated environment with several feasible paths, the experimental results demonstrate that KBPDE can obtain the globally optimal path in contrast to GA, DE, and the excellent versions of DE. In a partially known environment, the EKBPDE can plan the path successfully.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author, [author initials], upon reasonable request.

References

  1. Hewawasam, H.; Ibrahim, M.Y.; Appuhamillage, G.K.: Past, present and future of path-planning algorithms for mobile robot navigation in dynamic environments. IEEE Open J. Ind. Electron. Soc. 3, 353–365 (2022)

    Google Scholar 

  2. Zhang, T.-W.; Xu, G.-H.; Zhan, X.-S.; Han, T.: A new hybrid algorithm for path planning of mobile robot. J. Supercomput. 78(3), 4158–4181 (2022)

    Google Scholar 

  3. Li, C.; Huang, X.; Ding, J.; Song, K.; Lu, S.: Global path planning based on a bidirectional alternating search a* algorithm for mobile robots. Comput. Ind. Eng. 168, 108123 (2022)

    Google Scholar 

  4. Xu, X.; Cai, P.; Ahmed, Z.; Yellapu, V.S.; Zhang, W.: Path planning and dynamic collision avoidance algorithm under colregs via deep reinforcement learning. Neurocomputing 468, 181–197 (2022)

    Google Scholar 

  5. Abdulsaheb, J.A.; Kadhim, D.J.: Classical and heuristic approaches for mobile robot path planning: A survey. Robotics 12(4), 93 (2023)

    Google Scholar 

  6. Gao, H.; Liu, D.; Hu, J.: A survey on path planning for mobile robot systems. In: 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS), pp. 1176–1181 (2023). IEEE

  7. Szczepanski, R.; Tarczewski, T.; Erwinski, K.: Energy efficient local path planning algorithm based on predictive artificial potential field. IEEE Access 10, 39729–39742 (2022)

    Google Scholar 

  8. Kanoon, Z.E.; Al-Araji, A.S.; Abdullah, M.N.: Enhancement of cell decomposition path-planning algorithm for autonomous mobile robot based on an intelligent hybrid optimization method. Int. J. Intel. Eng. Syst. 15(3), 161 (2022)

    Google Scholar 

  9. Kumar, S.; Sikander, A.: A modified probabilistic roadmap algorithm for efficient mobile robot path planning. Eng. Optim. 55, 1–19 (2022)

    Google Scholar 

  10. Xiangrui, X.; Jia, G.; Zhuguan, L.; Qianlin, L.; Hongwei, D.: Ddpg-based improved seeker optimization algorithm for robot path planning. In: 2022 2nd International Conference on Computer, Control and Robotics (ICCCR), pp. 27–31 (2022). IEEE

  11. Sharma, V.; Tripathi, A.K.: A systematic review of meta-heuristic algorithms in iot based application. Array 14, 100164 (2022)

    Google Scholar 

  12. Kumar, S.; Sikander, A.: Optimum mobile robot path planning using improved artificial bee colony algorithm and evolutionary programming. Arab. J. Sci. Eng. 47(3), 3519–3539 (2022)

    Google Scholar 

  13. Chen, Z.; Xiong, G.; Liu, S.; Shen, Z.; Li, Y.: Path planning of mobile robot based on an improved genetic algorithm. In: 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI), pp. 1–6 (2022). IEEE

  14. Singh, J.; Fatima, S.; Chauhan, A.S.: Multi-objective travel route optimization using non-dominated sorting genetic algorithm. Int. J. Intel. Syst. Appl. Eng. 11(3), 785–794 (2023)

    Google Scholar 

  15. Yu, Z.; Si, Z.; Li, X.; Wang, D.; Song, H.: A novel hybrid particle swarm optimization algorithm for path planning of uavs. IEEE Internet Things J. 9(22), 22547–22558 (2022)

    Google Scholar 

  16. Hou, W.; Xiong, Z.; Wang, C.; Chen, H.: Enhanced ant colony algorithm with communication mechanism for mobile robot path planning. Robot. Autonom. Syst. 148, 103949 (2022)

    Google Scholar 

  17. Fu, J.; Lv, T.; Li, B.: Underwater submarine path planning based on artificial potential field ant colony algorithm and velocity obstacle method. Sensors 22(10), 3652 (2022)

    Google Scholar 

  18. Gao, P.; Zhou, L.; Zhao, X.; Shao, B.: Research on ship collision avoidance path planning based on modified potential field ant colony algorithm. Ocean Coast. Manag. 235, 106482 (2023)

    Google Scholar 

  19. Li, F.-F.; Du, Y.; Jia, K.-J.: Path planning and smoothing of mobile robot based on improved artificial fish swarm algorithm. Sci. Rep. 12(1), 659 (2022)

    Google Scholar 

  20. Cui, Y.; Hu, W.; Rahmani, A.: Fractional-order artificial bee colony algorithm with application in robot path planning. Eur. J. Oper. Res. 306(1), 47–64 (2023)

    MathSciNet  Google Scholar 

  21. Ou, Y.; Yin, P.; Mo, L.: An improved grey wolf optimizer and its application in robot path planning. Biomimetics 8(1), 84 (2023)

    Google Scholar 

  22. Slim, M.; Rokbani, N.; Neji, B.; Terres, M.A.; Beyrouthy, T.: Inverse kinematic solver based on bat algorithm for robotic arm path planning. Robotics 12(2), 38 (2023)

    Google Scholar 

  23. Xin, G.; Shi, L.; Long, G.; Pan, W.; Li, Y.; Xu, J.: Mobile robot path planning with reformative bat algorithm. Plos One 17(11), 0276577 (2022)

    Google Scholar 

  24. Kundra, H.; Khan, W.; Malik, M.; Rane, K.P.; Neware, R.; Jain, V.: Quantum-inspired firefly algorithm integrated with cuckoo search for optimal path planning. Int. J. Mod. Phys. C 33(02), 2250018 (2022)

    MathSciNet  Google Scholar 

  25. Chand, V.; Chaudhary, K.C.; Sharma, B.N.; Prasad, A.: Landmark aided robot path planning using stepping-ahead firefly algorithm. In: Pacific Asia Conference on Information Systems, p. 194 (2022). PACIS

  26. Dai, Y.; Yu, J.; Zhang, C.; Zhan, B.; Zheng, X.: A novel whale optimization algorithm of path planning strategy for mobile robots. Appl. Intell. 53(9), 10843–10857 (2023)

    Google Scholar 

  27. Katoch, S.; Chauhan, S.S.; Kumar, V.: A review on genetic algorithm: past, present, and future. Multimed. Tools Appl. 80, 8091–8126 (2021)

    Google Scholar 

  28. Yang, X.-S.; He, X.: Bat algorithm: literature review and applications. Int. J. Bio-inspired Comput. 5(3), 141–149 (2013)

    Google Scholar 

  29. Mirjalili, S.; Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Google Scholar 

  30. Storn, R.; Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341 (1997)

    MathSciNet  Google Scholar 

  31. Ahmad, M.F.; Isa, N.A.M.; Lim, W.H.; Ang, K.M.: Differential evolution: a recent review based on state-of-the-art works. Alex. Eng. J. 61(5), 3831–3872 (2022)

    Google Scholar 

  32. Şenel, B.; Şenel, F.A.: Bandpass filter design using deep neural network and differential evolution algorithm. Arab. J. Sci. Eng. 47(11), 14343–14354 (2022)

    Google Scholar 

  33. Fan, C.; Wu, Y.; Hu, H.; Xiao, L.; Yi, L.; Ning, X.: A two-stage cooperative multi-objective evolutionary differential algorithm for combined heat and power economic emission dispatch. Arab. J. Sci. Eng. 48(5), 5889–5906 (2023)

    Google Scholar 

  34. Chakraborty, S.; Saha, A.K.; Ezugwu, A.E.; Agushaka, J.O.; Zitar, R.A.; Abualigah, L.: Differential evolution and its applications in image processing problems: a comprehensive review. Arch. Comput. Methods Eng. 30(2), 985–1040 (2023)

    Google Scholar 

  35. Deng, W.; Ni, H.; Liu, Y.; Chen, H.; Zhao, H.: An adaptive differential evolution algorithm based on belief space and generalized opposition-based learning for resource allocation. Appl. Soft Comput. 127, 109419 (2022)

    Google Scholar 

  36. Song, Y.; Zhao, G.; Zhang, B.; Chen, H.; Deng, W.; Deng, W.: An enhanced distributed differential evolution algorithm for portfolio optimization problems. Eng. Appl. Artif. Intel. 121, 106004 (2023)

    Google Scholar 

  37. Zhang, H.; Liu, T.; Ye, X.; Heidari, A.A.; Liang, G.; Chen, H.; Pan, Z.: Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems. Eng. Comput. 39(3), 1735–1769 (2023)

    Google Scholar 

  38. Piotrowski, A.P.; Napiorkowski, J.J.; Piotrowska, A.E.: Particle swarm optimization or differential evolution-a comparison. Eng. Appl. Artif. Intel. 121, 106008 (2023)

    Google Scholar 

  39. Qin, A.K.; Huang, V.L.; Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evolut. Comput. 13(2), 398–417 (2008)

    Google Scholar 

  40. Brest, J.; Greiner, S.; Boskovic, B.; Mernik, M.; Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evolut. Comput. 10(6), 646–657 (2006)

    Google Scholar 

  41. Wang, Y.; Cai, Z.; Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evolut. Comput. 15(1), 55–66 (2011)

    Google Scholar 

  42. Zhang, J.; Sanderson, A.C.: Jade: adaptive differential evolution with optional external archive. IEEE Trans. Evolut. Comput. 13(5), 945–958 (2009)

    Google Scholar 

  43. Tanabe, R.; Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation, pp. 71–78 (2013). IEEE

  44. Tanabe, R.; Fukunaga, A.S.: Improving the search performance of shade using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665 (2014). IEEE

  45. Mohamed, A.W.; Mohamed, A.K.: Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int. J. Mach. Learn. Cybernet. 10, 253–277 (2019)

    Google Scholar 

  46. Nikolos, I.K.; Brintaki, A.N.: Coordinated uav path planning using differential evolution. In: Proceedings of the 2005 IEEE International Symposium On, Mediterrean Conference on Control and Automation Intelligent Control, 2005., pp. 549–556 (2005). IEEE

  47. Zhang, X.; Chen, J.; Xin, B.; Fang, H.: Online path planning for uav using an improved differential evolution algorithm. IFAC Proceedings Volumes 44(1), 6349–6354 (2011)

    Google Scholar 

  48. Yang-guang, F.; Cheng-ping, Z.; Han-ping, H.: Research on differential evolution algorithm for path planning for unmanned aerial vehicle inocean environment. Acta armamentarii 33(3), 295 (2012)

  49. Zamuda, A.; Sosa, J.D.H.: Differential evolution and underwater glider path planning applied to the short-term opportunistic sampling of dynamic mesoscale ocean structures. Appl. Soft Comput. 24, 95–108 (2014)

  50. Li, Z.; Jia, J.; Cheng, M.; Cui, Z.: Solving path planning of uav based on modified multi-population differential evolution algorithm. In: Advances in Neural Networks–ISNN 2014: 11th International Symposium on Neural Networks, ISNN 2014, Hong Kong and Macao, China, November 28-December 1, 2014. Proceedings, pp. 602–610 (2014). Springer

  51. Kok, K.Y.; Rajendran, P.: Differential-evolution control parameter optimization for unmanned aerial vehicle path planning. PloS one 11(3), 0150558 (2016)

    Google Scholar 

  52. Adhikari, D.; Kim, E.; Reza, H.: A fuzzy adaptive differential evolution for multi-objective 3d uav path optimization. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 2258–2265 (2017). IEEE

  53. Zamuda, A.; Sosa, J.D.H.: Success history applied to expert system for underwater glider path planning using differential evolution. Expert Syst. Appl. 119, 155–170 (2019)

    Google Scholar 

  54. Yu, X.; Li, C.; Yen, G.G.: A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management. Appl. Soft Comput. 98, 106857 (2021)

    Google Scholar 

  55. Pant, M.; Zaheer, H.; Garcia-Hernandez, L.; Abraham, A.; et al.: Differential evolution: a review of more than two decades of research. Eng. Appl. Artif. Intel. 90, 103479 (2020)

    Google Scholar 

  56. Das, S.; Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evolut. Comput. 15(1), 4–31 (2010)

    Google Scholar 

  57. Khan, S.S.; Ahmad, A.: Cluster center initialization algorithm for k-modes clustering. Expert Syst. Appl. 40(18), 7444–7456 (2013)

    Google Scholar 

  58. Sapegin, A.; Meinel, C.: K-metamodes: Frequency-and ensemble-based distributed k-modes clustering for security analytics. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 344–351 (2020). IEEE

  59. Wang, W.; Zhu, Q.; Wang, Z.; Zhao, X.; Yang, Y.: Research on indoor positioning algorithm based on saga-bp neural network. IEEE Sensors J. 22(4), 3736–3744 (2021)

  60. Liu, J.; Meng, L.: Integrating artificial bee colony algorithm and bp neural network for software aging prediction in iot environment. IEEE Access 7, 32941–32948 (2019)

    Google Scholar 

  61. Tu, J.; Yang, S.X.: Genetic algorithm based path planning for a mobile robot. In: 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), vol. 1, pp. 1221–1226 (2003). IEEE

Download references

Funding

This research was funded by the Guangxi Science and Technology Program(AB21120039).

Author information

Author notes

  1. Likun Hu and Zhihuan Ma have contributed equally to this work.

Authors and Affiliations

  1. College of Electrical Engineering, Guangxi University, Xixiangtang, Nanning, 530004, Guangxi, China

    Yueyang Liu, Likun Hu & Zhihuan Ma

  2. College of Electrical Engineering, Guangxi University, Xixiangtang, Nanning, 610101, Guangxi, China

    Likun Hu

Authors
  1. Yueyang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Likun Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihuan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yueyang Liu performed the study conception and design, the data analysis and the manuscript writing; Likun Hu performed the review and revision of manuscripts; Zhihuan Ma given some advice for this research.All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yueyang Liu.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

No publicly available data or datasets were used in this study.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Hu, L. & Ma, Z. A New Adaptive Differential Evolution Algorithm Fused with Multiple Strategies for Robot Path Planning. Arab J Sci Eng 49, 11907–11924 (2024). https://doi.org/10.1007/s13369-023-08380-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-023-08380-w

Keywords