DocumentCode
2558311
Title
Flexible inspection path planning based on Adaptive Genetic Algorithm
Author
Yang Zeqing ; Libing, Liu ; Weidong, Yang
Author_Institution
Sch. of Mech. Eng., Hebei Univ. of Technol., Tianjin
fYear
2008
fDate
2-4 July 2008
Firstpage
1558
Lastpage
1563
Abstract
In view of the characteristics of flexible inspection and the specific requirements of flexible inspection path planning, a new adaptive multi-object genetic algorithm (AMOGA) was proposed to provide quick and near-optimal solutions to large flexible inspection path planning problems. The encoding mechanism, crossover operator and mutation operator was designed in this algorithm according to the problem of the shortest path planning, meanwhile, the on-line adaptive adjustment strategy of crossover probability and mutation probability was used to compensate for the traditional algorithmpsilas limitations, which improved the search speed and search quality in genetic algorithm. Finally, we had experimented on the SSCK - U6035 5-axis CNC machine tools in Shenyang Machine Tool Co., LTD., and the inspection of Roller Bits Palm adopted AMOGA can get higher measurement accuracy. Moreover, the on-line inspection system was successfully developed based on OpenGL platform according to user needs, the effective inspection track was generated, the number of inspection points was obviously dropped compared with unplanning inspection. The experimental results demonstrated that this algorithm applied in the flexible inspection path planning was feasibility and reasonable, which can improve the measurement accuracy and inspection efficiency and provide a new thought for the largescale flexible inspection path planning.
Keywords
genetic algorithms; inspection; path planning; adaptive genetic algorithm; crossover operator; encoding mechanism; flexible inspection; mutation operator; path planning; Genetic algorithms; Inspection; Path planning; Adaptive Genetic Algorithm; Dynamic Simulation; Flexible Inspection; Path planning; Visualization;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location
Yantai, Shandong
Print_ISBN
978-1-4244-1733-9
Electronic_ISBN
978-1-4244-1734-6
Type
conf
DOI
10.1109/CCDC.2008.4597579
Filename
4597579
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