Skip to main content
SpringerLink
Log in

Reduction in Trajectory Error by Generating Smoother Trajectory for the Time-Efficient Navigation of Mobile Robot

  • Original Paper
  • Published:
MAPAN Aims and scope Submit manuscript

Abstract

Robotics is intertwined with metrology, including aircraft component inspection, automotive processes, and part geometry optimization. Optimized trajectory planning is essential for reliable robotic arm operation and maintaining quality in inspections and geometric enhancements, as well as autonomous mobile robot navigation. Technically, a path planning is associated as an optimization problem that relies on various parameters such as length minimization problem, smooth trajectory planning, low time/space complexity, and computational load. While considering all these stated parameters, choosing an optimal path to reach the destination is the primary function of path planning techniques. This research paper is focused on the implementation of adaptive bidirectional A* (ABA*) algorithm along with new strategy of flexible controlling points technique (FCP) to reduce the trajectory error by generating smoother trajectory. With the increased number of sharp turns, the wheel skidding error is generated that reduce the reliability of the path planning techniques by increasing the pose estimation error. By conducting multiple trials, the proposed technique has been implemented, resulting in a 100% reduction in the number of collisions. Furthermore, the application of the new FCP technique eliminates all sharp turns, leading to a 38% decrease in time lag uncertainty compared to conventional approaches. The proposed technique improves autonomous navigation by selecting smoother trajectories.

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

Access this article

Log in via an institution

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

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

Similar content being viewed by others

References

  1. R. Siegwart, I.R. Nourbakhsh and D. Scaramuzza, Introduction to autonomous mobile robots. MIT Press (2011).

    Google Scholar 

  2. B. Siciliano and O. Khatib (eds), Springer handbook of robotics. Springer (2016), pp. 1577–1604.

    Book  Google Scholar 

  3. R. Singh and K.S. Nagla, Improved 2D laser grid mapping by solving mirror reflection uncertainty in SLAM. Int. J. Intell. Unmanned Syst. (2018). https://doi.org/10.1108/IJIUS-01-2018-0003.

    Article  Google Scholar 

  4. R. Singh and K.S. Nagla, Comparative analysis of range sensors for the robust autonomous navigation–a review. Sensor Rev., 40 (2019) 17–41. https://doi.org/10.1108/SR-01-2019-0029.

    Article  Google Scholar 

  5. H.Y. Zhang, W.M. Lin and A.X. Chen, Path planning for the mobile robot: A review. Symmetry, 10 (2018) 450. https://doi.org/10.3390/sym10100450.

    Article  ADS  Google Scholar 

  6. Z. Wu, G. Hu, L. Feng, J. Wu and S. Liu, Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling. Assembly Autom., 36 (2016) 318–332. https://doi.org/10.1108/AA-01-2016-008.

    Article  Google Scholar 

  7. P.E. Hart, N.J. Nilsson and B. Raphael, A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybernet., 4 (1968) 100–107. https://doi.org/10.1109/TSSC.1968.300136.

    Article  Google Scholar 

  8. R.K. Singh and K.S. Nagla, Enhanced A* algorithm for the time efficient navigation of unmanned vehicle by reducing the uncertainty in path length optimization. MAPAN-J. Metrol. Soc India (2023). https://doi.org/10.1007/s12647-022-00618-6.

    Article  Google Scholar 

  9. R.K. Singh and K.S. Nagla, A**: A bidirectional approach based on analytical treatment to conventional A* for the smooth and fast trajectory planning. Int. J. Inf. Tecnol. (2023). https://doi.org/10.1007/s41870-023-01269-9.

    Article  Google Scholar 

  10. R. Singh, Trajectory optimization with hybrid probabilistic roadmap approach to achieve time efficient navigation of unmanned vehicles in unstructured environment. Robot. Intell. Autom. (2024). https://doi.org/10.1108/RIA-08-2023-0107.

    Article  Google Scholar 

  11. R. Singh, Optimized trajectory planning for the time efficient navigation of mobile robot in constrained environment. Int. J. Mach. Learn. & Cyber., 14 (2023) 1079–1103. https://doi.org/10.1007/s13042-022-01684-7.

    Article  Google Scholar 

  12. S. Kumar and A. Sikander, A novel hybrid framework for single and multi-robot path planning in a complex industrial environment. J. Intell. Manuf., 35 (2024) 587–612. https://doi.org/10.1007/s10845-022-02056-2.

    Article  Google Scholar 

  13. B. Song, Z. Wang and L. Sheng, A new genetic algorithm approach to smooth path planning for mobile robots. Assembly Autom., 36 (2016) 138–145. https://doi.org/10.1108/AA-11-2015-094.

    Article  Google Scholar 

  14. B. Fu, L. Chen, Y. Zhou, D. Zheng, Z. Wei, J. Dai and H. Pan, An improved A* algorithm for the industrial robot path planning with high success rate and shortlength. Robot. Autonom. Syst., 106 (2018) 26–37. https://doi.org/10.1016/j.robot.2018.04.007.

    Article  Google Scholar 

  15. M. Davoodi, Bi-objective path planning using deterministic algorithms. Robot. Autonom. Syst., 93 (2017) 105–115. https://doi.org/10.1016/j.robot.2017.03.021.

    Article  Google Scholar 

  16. J. Krishnan, U.P. Rajeev, J. Jayabalan and D.S. Sheela, Optimal motion planning based on path length minimisation. Robot. Autonom. Syst., 94 (2017) 245–263. https://doi.org/10.1016/j.robot.2017.04.014.

    Article  Google Scholar 

  17. L. Zhang and Y. Li, Mobile robot path planning algorithm based on improved a star. J. Phys. Conf. Ser., 2021 (1848) 1–7. https://doi.org/10.1088/1742-6596/1848/1/012013.

    Article  Google Scholar 

  18. N.H. Singh and K. Thongam, Mobile robot navigation using MLPBP approaches in dynamic environments. Arab. J. Sci. Eng. (2018). https://doi.org/10.1007/s13369-018-3267-2.

    Article  Google Scholar 

  19. H.K. Paikray, P.K. Das and S. Panda, Optimal multi-robot path planning using particle swarm optimization algorithm improved bysine and cosine algorithms. Arab. J. Sci. Eng. (2021). https://doi.org/10.1007/s13369-020-05046-9.

    Article  Google Scholar 

  20. B. Song, G. Tian and F. Zhou, A comparison study on path smoothing algorithms for laser robot navigated mobile robot path planning in intelligent space. J. Inf. Comput. Sci., 7 (2010) 2943–2950.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instrumentation and Control Engineering Department, Dr B.R. Ambedkar National Institute of Technology, Jalandhar, India

    Raj Kumar Singh & K. S. Nagla

Authors
  1. Raj Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. S. Nagla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raj Kumar Singh.

Additional information

Publisher's Note

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

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

Singh, R.K., Nagla, K.S. Reduction in Trajectory Error by Generating Smoother Trajectory for the Time-Efficient Navigation of Mobile Robot. MAPAN (2024). https://doi.org/10.1007/s12647-024-00752-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12647-024-00752-3

Keywords

Search

Navigation