Documenti accessibili
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Informazioni generali |
| Autore |
Polden, Joseph; Pan, Zengxi; Larkin, Nathan; Duin, Stephen Van |
| Pubblicato |
InTech Open Access Publisher, 2013
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| Abstract |
Probabilistic methods have been proven to be
effective for robotic path planning in a geometrically
complex environment. In this paper, we propose a novel
approach, which utilizes a specialized roadmap
expansion phase, to improve lazy probabilistic path
planning. This expansion phase analyses roadmap
connectivity information to bias sampling towards objects
in the workspace that have not yet been navigated by the
robot. A new method to reduce the number of samples
required to navigate narrow passages is also proposed
and tested. Experimental results show that the new
algorithm is more efficient than the traditional path
planning methodologies. It was able to generate solutions
for a variety of path planning problems faster, using
fewer samples to arrive at a valid solution. |
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International Journal of Advanced Robotic Systems
Autore: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Pubblicato: 2004
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