DocumentCode :
305696
Title :
Self-adaptively pruning and renovating the hypothesis tree of dynamic world
Author :
Qiao, Lin ; Yang, Jingan ; Zhang, Diancheng
Author_Institution :
Inst. of Artificial Intelligence, Hefei Univ. of Technol., China
Volume :
1
fYear :
1996
fDate :
14-17 Oct 1996
Firstpage :
631
Abstract :
This paper presents a new self-adaptive pruning and renovation strategy for modeling dynamic world, which is a crucial problem for mobile robots to build and maintain a map of its environment by means of a Bayesian multiple hypothesis approach. This strategy mainly consists of two parts: pruning and renovation. Pruning eliminates those branches of the hypothesis tree with lower probabilities. Once the hypothesis branch with the greatest probability is not identical with the actual measurements, other hypotheses will be compared with the actual measurements in turn due to their probabilities. If criteria are satisfied, the strategy will retain it and prune all other branches
Keywords :
Bayes methods; adaptive systems; mobile robots; path planning; probability; trees (mathematics); Bayesian multiple hypothesis; dynamic world modelling; hypothesis tree; mobile robots; motion planning; probability; renovation strategy; self-adaptive pruning; Artificial intelligence; Bayesian methods; Mobile robots; Motion planning; Probability; Remotely operated vehicles; Robot sensing systems; Solid modeling; Technology management; Vehicle dynamics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1996., IEEE International Conference on
Conference_Location :
Beijing
ISSN :
1062-922X
Print_ISBN :
0-7803-3280-6
Type :
conf
DOI :
10.1109/ICSMC.1996.569867
Filename :
569867
Link To Document :
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