TY - JOUR
T1 - Optimized RRT-A* path planning method for mobile robots in partially known environment
AU - Ayawli, Ben Beklisi Kwame
AU - Mei, Xue
AU - Shen, Mouquan
AU - Appiah, Albert Yaw
AU - Kyeremeh, Frimpong
N1 - Publisher Copyright:
© 2019, Kauno Technologijos Universitetas. All rights reserved.
PY - 2019
Y1 - 2019
N2 - This paper presents an optimized rapidly exploring random tree A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for 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 partially known environments.
AB - This paper presents an optimized rapidly exploring random tree A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for 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 partially known environments.
KW - Autonomous ground vehicles
KW - Mobile robots
KW - Morphological dilation
KW - Path planning
KW - Rapidly exploring random tree (RRT)
UR - http://www.scopus.com/inward/record.url?scp=85073291931&partnerID=8YFLogxK
U2 - 10.5755/j01.itc.48.2.21390
DO - 10.5755/j01.itc.48.2.21390
M3 - 文章
AN - SCOPUS:85073291931
SN - 1392-124X
VL - 48
SP - 179
EP - 194
JO - Information Technology and Control
JF - Information Technology and Control
IS - 2
ER -