Abstract—The hRRT algorithms based on heuristics can solve many path planning problems, but its efficiency can still be improved. We deeply analyze the details of hRRT, and enhance it by using bi-directional tree and adopting kd-tree as secondary data structure to select nearest neighbors. At last, the improved hRRT is used for lift path planning for telescopic crane, and the efficiency of the improved hRRT is verified. The result of the experiment shows that the enhanced hRRT can dramatically shorten the plan time without reducing the quality of path.
Index Terms—Rapidly-exploring random tree, hRRT, path planning, crane lifting.
Yuanshan Lin, Di Wu, and Xiukun Wang are with the School of Computer Science, Dalian University of Technology, Dalian, Liaoning, P.R. China (e-mail: Linyuanshan2008@126.com, Jsjwxk@dlut.edu.cn).
Xin Wang and Shunde Gao are with the School of Mechanical Engineering, Dalian University of Technology, Dalian, Liaoning, P.R. China (e-mail: Wangxbd21@163.com, Gaoshunde@163.com).
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Cite:Yuanshan Lin, Xin Wang, Di Wu, Xiukun Wang, and Shunde Gao, "Lift Path Planning for Telescopic Crane Based-on Improved hRRT," International Journal of Computer Theory and Engineering vol. 5, no. 5, pp. 816-819, 2013.
