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Fuzzy Greedy RRT Path Planning Algorithm in a Complex Configuration Space

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  • Intelligent Control and Applications
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Abstract

A randomized sampling-based path planning algorithm for holonomic mobile robots in complex configuration spaces is proposed in this article. A complex configuration space for path planning algorithms may cause different environmental constraints including the convex/concave obstacles, narrow passages, maze-like spaces and cluttered obstacles. The number of vertices and edges of a search tree for path planning in these configuration spaces would increase through the conventional randomized sampling-based algorithm leading to exacerbation of computational complexity and required runtime. The proposed path planning algorithm is named fuzzy greedy rapidly-exploring random tree (FG-RRT). The FG-RRT is equipped with a fuzzy inference system (FIS) consisting of two inputs, one output and nine rules. The first input is a Euclidean function applied in evaluating the quantity of selected parent vertex. The second input is a metaheuristic function applied in evaluating the quality of selected parent vertex. The output indicates the competency of the selected parent vertex for generating a random offspring vertex. This algorithm controls the tree edges growth direction and density in different places of the configuration space concurrently. The proposed method is implemented on a Single Board Computer (SBC) through the xPC Target to evaluate this algorithm. For this purpose four test-cases are designed with different complexity. The results of the Processor-in-the-Loop (PIL) tests indicate that FG-RRT algorithm reduces the required runtime and computational complexity in comparison with the conventional and greedy RRT through fewer number of vertices in planning an initial path in significant manner.

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Author information

Authors and Affiliations

  1. Control Group, Electrical Engineering Department, Malek-Ashtar University of Technology, Tehran, 15875-1774, Iran

    Ehsan Taheri & Mohammad Hossein Ferdowsi

  2. Department of Mechanical Engineering, Isfahan University of Technology, 84156-83111, Isfahan, Iran

    Mohammad Danesh

Authors
  1. Ehsan Taheri
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  2. Mohammad Hossein Ferdowsi
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  3. Mohammad Danesh
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Corresponding author

Correspondence to Ehsan Taheri.

Additional information

Recommended by Associate Editor DaeEun Kim under the direction of Editor Euntai Kim.

Ehsan Taheri was born in Iran in 1984. He received his B.Sc. degree in electrical engineering and his M.S. degree in control engineering, in 2006 and 2008, respectively from the Islamic Azad University, Najafabad Branch and Malek Ashtar University of Technology. Currently, he is a Ph.D. candidate in the Malek Ashtar University of Technology. His research interests include Autonomy, Underwater Robots, Path planning, heuristic optimization, and Motion Control.

Mohammad Hossein Ferdowsi received his BSc and MSc degrees in electrical engineering from Sharif University of Technology and his Ph.D. degree in electrical engineering from University of Tehran, in 1977, 1980, and 2004, respectively. From 1985 to 1987, he was a lecturer at Sharif University of Technology, and since 1987, has been at Malek Ashtar University of Technology as a faculty member. His research interest is in multivariable and adaptive control systems, intelligent systems, and target tracking.

Mohammad Danesh received his B.Sc., M.Sc., and Ph.D. degrees in control engineering from the Isfahan University of Technology (IUT), Isfahan, Iran, in 1997, 1999, and 2007, respectively. He has been with the department of Mechanical Engineering, IUT, since 2007. His current research interests include robotics, intelligent systems, mechatronics, control of dynamical systems, and stability analysis.

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Taheri, E., Ferdowsi, M.H. & Danesh, M. Fuzzy Greedy RRT Path Planning Algorithm in a Complex Configuration Space. Int. J. Control Autom. Syst. 16, 3026–3035 (2018). https://doi.org/10.1007/s12555-018-0037-6

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  • DOI: https://doi.org/10.1007/s12555-018-0037-6

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