DocumentCode :
1798255
Title :
A computationally efficient complete area coverage algorithm for intelligent mobile robot navigation
Author :
Jan, Gene Eu ; Chaomin Luo ; Lun-Ping Hung ; Shao-Ting Shih
Author_Institution :
Dept. of Electr. Eng., Nat. Taipei Univ., Taipei, Taiwan
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
961
Lastpage :
966
Abstract :
Complete area coverage navigation (CAC) requires a special type of robot path planning, where the robots should visit every point of the state workspace. CAC is an essential issue for cleaning robots and many other robotic applications. Real-time complete area coverage path planning is desirable for efficient performance in many applications. In this paper, a novel vertical cell-decomposition (VCD) with convex hull (VCD-CH) approach is proposed for real-time CAC navigation of autonomous mobile robots. In this model, a vertical cell-decomposition (VCD) methodology and a spanning-tree based approach with convex hull are effectively integrated to plan a complete area coverage motion for autonomous mobile robot navigation. The computational complexity of this method with minimum trajectory length planned by a cleaning robot in the complete area coverage navigation with rectangle obstacles in the Euclidean space is O(n log n). The performance analysis, computational validation and comparison studies demonstrate that the proposal model is computational efficient, complete and robust.
Keywords :
computational complexity; intelligent robots; mobile robots; path planning; trees (mathematics); CAC algorithm; Euclidean space; O(n log n) complexity; VCD-CH approach; autonomous mobile robots; cleaning robots; complete area coverage algorithm; computational complexity; intelligent mobile robot navigation; robot path planning; robotic applications; spanning-tree based approach; vertical cell-decomposition with convex hull; Cleaning; Mobile robots; Navigation; Planning; Robot sensing systems; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889862
Filename :
6889862
Link To Document :
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