A Generalized 3-D Path Planning Method for Robots Using Genetic Algorithm with An Adaptive Evolution Process
Refereed conference paper presented and published in conference proceedings


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AbstractTraditionally, path planning for robots is modeled as optimization problems. One of the most commonly used optimization strategies is Genetic Algorithm (GA) since it always guarantees a nearly shortest path even a global path can not be obtained within a reasonable time. In fact, the performance enhancement of GA is still an open topic for researchers by designing different genetic operators. In addition to this, we propose a new criterion for the selection of genetic operators during evolution in order to further facilitate the searching efficiency. In this paper, we propose a generalized 3-D path planning method for robots using GA with an adaptive evolution process. Based on the framework of traditional GA, we first introduce a new genetic operator, called Bind-NN which randomly divides and recombines an elitist chromosome based on nearest neighbor. We also show that by choosing the fitness variance of the shortest path in last generations as a guidance in selecting genetic operators during evolution, the search efficiency can be significantly improved, thus proposing a genetic operators selection scheme. In the latter part of this paper, we present the algorithm evaluation by stimulating a sample path planning problem on a structural frame. With the use of the proposed genetic operator and selection scheme, experimental results show a significant improvement in terms of search effectiveness and efficiency.
All Author(s) ListChung WK, Xu YS
Name of Conference8th World Congress on Intelligent Control and Automation (WCICA)
Start Date of Conference06/07/2010
End Date of Conference09/07/2010
Place of ConferenceJinan
Country/Region of ConferenceChina
Year2010
Month1
Day1
PublisherIEEE
Pages1354 - 1360
eISBN978-1-4244-6712-9
LanguagesEnglish-United Kingdom
Keywords3-D path planning; adaptive evolution; genetic algorithm
Web of Science Subject CategoriesAutomation & Control Systems; Engineering; Engineering, Electrical & Electronic

Last updated on 2021-19-02 at 23:35