Triangular Geometrized Sampling Heuristics for Fast Optimal Motion Planning

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...

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Bibliographic Details
Published inInternational journal of advanced robotic systems Vol. 12; no. 2
Main Authors Qureshi, Ahmed Hussain, Mumtaz, Saba, Ayaz, Yasar, Hasan, Osman, Muhammad, Mannan Saeed, Mahmood, Muhammad Tariq
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 12.02.2015
Sage Publications Ltd
SAGE Publishing
Subjects
Algorithms
Collision avoidance
Computer simulation
Convergence
Exploitation
Geometry
Heuristic
Iterative methods
Mathematical analysis
Mathematical models
Methods
Motion planning
Optimization
Path planning
Performance enhancement
Planning
Robotics
Motion planning
Triangular geometry
Optimal path planning
Sampling-based algorithm
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Summary: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.
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ISSN:1729-8806
1729-8814
DOI:10.5772/59763