Triangular Geometrized Sampling Heuristics for Fast Optimal Motion Planning

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ID: 52085
2015
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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.
Reference Key
qureshi2015triangularinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Qureshi, Ahmed Hussain;Mumtaz, Saba;Ayaz, Yasar;Hasan, Osman;Muhammad, Mannan Saeed;Mahmood, Muhammad Tariq;
Journal international journal of advanced robotic systems
Year 2015
DOI
DOI not found
URL
https://doi.org/10.5772/59763
Keywords
physics psychology gynecology and obstetrics commerce electronics political science electronic computers. computer science international relations marketing. distribution of products

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