Path Planning Algorithm for Unmanned Ground Vehicles (UGVs) in Known Static Environments

Unmanned ground vehicles (UGVs) have been utilized in many civilian fields in addition to the traditional military field. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. Many applications require the UGV to autonomously navigate static environments while taking into consideration obstacle avoidance. Autonomous path planning is one of the key challenges and issues related to UGVs. Generally, robotic path planning is an optimization search problem that comes in different forms. Some of its forms have been solved by different classical algorithms such as A*, but these algorithms are computationally inefficient. In contrast, the emerging nature-inspired algorithms outperform the classical ones since the computational overhead is reduced. Nature-inspired algorithms are among the most common heuristic algorithms. This paper proposes a near-optimal algorithm to find a feasible path for UGV in a static environment. The performance of the proposed algorithm was compared with other well-established algorithms in path planning literature such as A* using a simulator developed for this purpose. The simulator tests three performance measures path length, farness from obstacles, and running time. The simulator results revealed that the length of the path generated by this algorithm is near-optimal, however, the generated path is kept far from obstacles.

• Record URL:
  • https://doi.org/10.1016/j.procs.2020.10.011
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1877050920322766
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2020 Eman Almoaili and Heba Kurdi. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Almoaili, Eman
  • Kurdi, Heba
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 57-63
• Serial:
  • Procedia Computer Science
  • Volume: 177
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Algorithms; Mobile robots; Optimization; Proximity detectors; Training simulators; Trajectory control
• Subject Areas: Data and Information Technology; Transportation (General); Vehicles and Equipment;

Filing Info

• Accession Number: 01761034
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 22 2020 9:36AM