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Triangular Geometrized Sampling Heuristics for Fast Optimal Motion Planning
Triangular Geometrized Sampling Heuristics for Fast Optimal Motion Planning
Ahmed Hussain Qureshi
(School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), H-12 Campus, Islamabad, Pakistan)
Saba Mumtaz
(School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), H-12 Campus, Islamabad, Pakistan)
Yasar Ayaz
(Robotics and Intelligent Systems Engineering (RISE) Lab, Department of Robotics and Artificial Intelligence, School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), H-12 Campus, Islamabad, Pakistan)
Osman Hasan
(School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), H-12 Campus, Islamabad, Pakistan)
Mannan Saeed Muhammad
(Graduate School of Electronics and Computer Engineering, College of Engineering, Hanyang University, Seoul, Korea)
Muhammad Tariq Mahmood
(School of Computer Science and Engineering, Korea University of Technology and Education, Chungjeolno, Byeogchunmyun, Cheonan, Chungnam, South Korea)
Journal: International Journal of Advanced Robotic Systems
•
• SAGE Publishing
• ISSN: 1729-8814
Abstract
Rapidly-exploring Random Tree (RRT)-based algorithms have become increasingly popular due to their lower computational complexity as compared with other path planning algorithms. The recently presented RRT * motion planning algorithm improves upon the original RRT algorithm by providing optimal path solutions. While RRT determines an initial collision-free path fairly quickly, RRT * guarantees almost certain convergence to an optimal, obstacle-free path from the start to the goal points for any given geometrical environment. However, the main limitations of RRT * include its slow processing rate and high memory consumption, due to the large number of iterations required for calculating the optimal path. In order to overcome these limitations, we present another improvement, i.e, the Triangular Geometerized-RRT * (TG-RRT * ) algorithm, which utilizes triangular geometrical methods to improve the performance of the RRT * algorithm in terms of the processing time and a decreased number of iterations required for an optimal path solution. Simulations comparing the performance results of the improved TG-RRT * with RRT * are presented to demonstrate the overall improvement in performance and optimal path detection.
Ahmed Hussain Qureshi, Saba Mumtaz, Yasar Ayaz, et al. (2015). "Triangular Geometrized Sampling Heuristics for Fast Optimal Motion Planning". International Journal of Advanced Robotic Systems. https://doi.org/10.5772/59763