Open AccessOptimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment
Author(s) -
Ben Beklisi Kwame Ayawli,
Xue Mei,
Mouquan Shen,
Albert Yaw Appiah,
Frimpong Kyeremeh
Publication year - 2019
Publication title -
information technology and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.286
H-Index - 19
eISSN - 2335-884X
pISSN - 1392-124X
DOI - 10.5755/j01.itc.48.2.21390
Subject(s) - motion planning , random tree , computer science , mobile robot , spline interpolation , robot , path (computing) , shortest path problem , mathematical optimization , spline (mechanical) , dilation (metric space) , algorithm , artificial intelligence , computer vision , mathematics , engineering , theoretical computer science , graph , structural engineering , bilinear interpolation , programming language , combinatorics
This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments
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