Advertisement

Local Path Planning for Autonomous Mobile Robot Based on APF-BUG Algorithm with Ground Quality Indicator

  • Kamil WyrąbkiewiczEmail author
  • Tomasz Tarczewski
  • Łukasz Niewiara
Conference paper
  • 92 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1196)

Abstract

In this paper, an enhanced Artificial Potential Field (AFP) algorithm applied to autonomous mobile robot is presented. The proposed solution is extended by an additional BUG algorithm and a ground quality indicator. The modification allows to avoid local minima in path planning caused by complex terrain obstacles. The developed algorithm takes into account the substrate quality, classifying a poor ground as an obstacle. It was implemented and tested in Matlab software, utilizing the track planning algorithm as a state machine. The simulation environment enables graphical presentation of the chosen path and the arrangement of moving area. The article presents and discusses a basic issue in local path planning of autonomous mobile platforms, i.e. the ground quality, which is ignored in classical algorithms. This is a non-trivial problem, which impacts the success rate of getting the final destination. The developed algorithm is extended by a BUG rule and ground quality indicator which allows to avoid immobilization of the platform.

Keywords

Artificial Potential Fields Bug algorithm Ground quality indicator Autonomous mobile robots Path planning 

References

  1. 1.
    Kozłowski, K., Kowalczyk, W.: Artificial potential based control for a large scale formation of mobile robots. In: Proceedings of the Fourth International Workshop on Robot Motion and Control (2004)Google Scholar
  2. 2.
    Skrzypczyński, P., Belter, D., Łabęcki, P.: Adaptive motion planning for autonomous rough terrain traversal with a walking robot. J. Field Rob. 33(3), 337–370 (2016)CrossRefGoogle Scholar
  3. 3.
    Weerakoon, T., Ishii, K., Nassiraei, A.A.F.: An artificial potential field based mobile robot navigation method to prevent from deadlock. J. Artif. Intell. Soft Comput. Res. 5(3), 189–203 (2015)CrossRefGoogle Scholar
  4. 4.
    Chołodowicz, E., Figurowski, D.: Mobile robot path planning with obstacle avoidance using particle swarm optimization. Pomiary Automatyka Robotyka 21(3), 59–68 (2017)CrossRefGoogle Scholar
  5. 5.
    Kowalczuk, Z., Duzinkiewicz, K.: Planowanie trajektorii ruchu zespołu robotów mobilnych z zastosowaniem metody warstwicowej. Fuzzy logic system in wheeled mobile robots formation path planning. Pomiary Automatyka Robotyka 54(3), 140–144 (2008)Google Scholar
  6. 6.
    Lacroix, S., Mallet, A., Bonnafous, D., Bauzil, G., Fleury, S., Herrb, M., Chatila, R.: Autonomous rover navigation on unknown terrains: functions and integration. Int. J. Rob. Res. 21(10), 917–942 (2002)CrossRefGoogle Scholar
  7. 7.
    Kala, R., Warwick, K.: Planning autonomous vehicles in the absence of speed lanes using an elastic strip. IEEE Trans. Intell. Transp. Syst. 14(4), 1743–1752 (2013)CrossRefGoogle Scholar
  8. 8.
    Jiang, D.Z., Min, W.Z.: Mobile robot path tracking in unknown dynamic environment. In: Robotics, Automation and Mechatronics, IEEE Conference (2008)Google Scholar
  9. 9.
    Adeli, H., Tabrizi, M.H.N., Mazloomian, A., Hajipour, E., Jahed, M.: Path planning for mobile robots using iterative artificial potential field method. IJCSI Int. J. Comput. Sci. Issues 8(4), 28–32 (2011)Google Scholar
  10. 10.
    Siegwart, R., Nourbakhsh, I.R.: Introductions to autonomous mobile robots. Massachusetts Institute of Technology, pp. 272–274 (2004)Google Scholar
  11. 11.
    Grabowska, E.: Application of terrestrial signal sources in supporting GPS system in tasks of engineering surveying. Prace Naukowe Politechniki Warszawskiej. Geodezja 43, 37–53 (2008)Google Scholar
  12. 12.
    Wyrąbkiewicz, K., Tarczewski, T., Grzesiak, L.: Artificial potential fields algorithm for Mars rover path planning in an unknown environment. Poznan Univ. Technol. Acad. J. Electr. Eng. 80, 183–189 (2014)Google Scholar
  13. 13.
    Wyrąbkiewicz, K., Tarczewski, T., Grzesiak, L.: Artifical potential fields with extended Bug algorithm for Mars rover path planning in an unknown environment. Comput. Appl. Elect. 12, 422–433 (2014)Google Scholar
  14. 14.
    Washington, R., Golden, K., Bresina, J., Smith, D.E., Anderson, C., Smith, T.: Autonomous rovers for mars exploration. In: NASA Ames Research Center. 1999 IEEE Aerospace Conference. Proceedings (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Nicolaus Copernicus UniversityTorunPoland

Personalised recommendations