COMPARATIVE ANALYSIS OF RRT-BASED METHODS FOR PATHFINDING IN UNDERGROUND ENVIRONMENT

Article Sidebar

PDF
DOI: https://doi.org/10.20998/2522-9052.2020.3.15
Keywords:
robot, pathfinding, autonomy, RRT

Main Article Content

Andrii Protsenko
Kharkiv National University of Radio Electronics, Kharkiv
https://orcid.org/0000-0001-8754-7444
Valerii Ivanov
Kharkiv National University of Radio Electronics, Kharkiv
https://orcid.org/0000-0002-6419-3759

Abstract

The importance of finding a path for autonomous moving robots is indispensable, because the successful achievement of the target location depends on the solution of this problem.There are a large number of different methods of finding the way, which differ in the accuracy of work, speed, the need for additional equipment.Underground environments, such as mines and tunnels, differ from other structures and open space, and therefore, require different approach when performing pathfinding, as narrow, curved passages and heterogeneous structure could render some of the pathfinding methods ineffective. However, methods based on rapidly exploring random trees (RRT) maintain their effectiveness because they are unaffected by the complexity of the environment. In this article presented a comparisonof the three RRT-based methods: RRT, RRT-connect and RRT*.

Article Details

How to Cite
Protsenko, A., & Ivanov, V. (2020). COMPARATIVE ANALYSIS OF RRT-BASED METHODS FOR PATHFINDING IN UNDERGROUND ENVIRONMENT. Advanced Information Systems, 4(3), 109–112. https://doi.org/10.20998/2522-9052.2020.3.15
Issue
Vol. 4 No. 3 (2020): Advanced Information Systems
Section
Information systems research
Author Biographies

Andrii Protsenko, Kharkiv National University of Radio Electronics, Kharkiv

PhD student of Department of Computer-Integrated Technologies, Automation and Mechatronics

Valerii Ivanov, Kharkiv National University of Radio Electronics, Kharkiv

PhD (C) of Technical Sciences, Professor of Department of Systems Engineering (SysEng)

References

Protsenko, A. & Ivanov V. (2019), “Classical methods of path planning for mobile robots”, Control, navigation and communication systems, Vol. 3 (55), pp. 143-151, DOI: https://doi.org/10.26906/SUNZ.2019.3.143.

Barfoot, T. D., Stenning, B., Furgale, P., & McManus, C. (2012), “Exploiting Reusable Paths in Mobile Robotics: Benefits and Challenges for Long-term Autonomy”, Ninth Conf. on Comp. and Robot Vision, DOI: https://doi.org/10.1109/crv.2012.58.

Dang, T., Khattak, S., Mascarich, F., & Alexis, K. (2019), “Explore Locally, Plan Globally: A Path Planning Framework for Autonomous Robotic Exploration in Subterranean Environments”, 19th International Conference on Advanced Robotics (IC-AR). DOI: https://doi.org/10.1109/icar46387.2019.8981594.

LaValle, S.M. (1998), “Rapidly-exploring random trees: A new tool for path planning”, DOI: https://doi.org/10.1.1.35.1853.

Kuffner, J.J., & LaValle, S.M. (2000), “RRT-connect: An efficient approach to single-query path planning”, IEEE Int. Conf. on Rob. and Aut. Symp. Proc. (Cat. No. 00CH37065), Vol. 2, pp. 995-1001, DOI: https://doi.org/10.1109/ROBOT.2000.844730.

Karaman, S. & Frazzoli, E. (2011), “Sampling-based algorithms for optimal motion planning”, The Int. Journal of Robotics Research, 30(7), pp. 846–894, DOI: https://doi.org/10.1177/0278364911406761.