DocumentCode :
681491
Title :
Obstacle guided RRT path planner with region classification for changing environments
Author :
Hong Liu ; Kai Rao ; Fang Xiao
Author_Institution :
Eng. Lab. on Intell. Perception for Internet of Things (ELIP), Peking Univ., Shenzhen, China
fYear :
2013
fDate :
12-14 Dec. 2013
Firstpage :
164
Lastpage :
171
Abstract :
The Rapidly-exploring Random Tree (RRT) has been widely used to solve path planning problems and well suited to lots of problem domains for its probabilistically complete. However, it is not so rapid in changing environments, troubled with moving obstacles and difficult regions. In this paper, a variant of RRT is proposed which is called obstacle guided RRT (OG-RRT), aiming to plan a path in changing environments efficiently. By preserving a group of invalid configurations blocked by obstacles, an entropy value is introduced to label every state in the tree with region classification information. Then a differentiation strategy is adopted to the framework for extending. Finally, with recording the change between invalid and valid nodes, a fuzzy estimation for obstacles´ movements and an opportunistic strategy for reusing information from previous queries will be used to replan a solution fast. In plentiful experiments, OG-RRT is very effective in changing environment.
Keywords :
collision avoidance; random processes; trees (mathematics); OG-RRT; differentiation strategy; entropy value; fuzzy estimation; obstacle guided RRT path planner; path planning problem; rapidly-exploring random tree; region classification information; Algorithm design and analysis; Classification algorithms; Educational institutions; Entropy; Heuristic algorithms; Planning; Vegetation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on
Conference_Location :
Shenzhen
Type :
conf
DOI :
10.1109/ROBIO.2013.6739453
Filename :
6739453
Link To Document :
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