Path planning method for robots in complex ground environment based on cultural algorithm

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Path planning method for robots in complex ground environment based on cultural algorithm

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ACM/SIGEVO Summit on Genetic and Evolutionary Computation archive
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation table of contents

Shanghai, China

SESSION: Full papers table of contents

Pages 185-192

Year of Publication: 2009

ISBN:978-1-60558-326-6

Authors

Yi-nan Guo	School of Information and Electronic Engineering,China University of Mining and Technology, Xuzhou, China
Mei Yang	Electronic Engineering,China University of Mining and Technology, Xuzhou, China
Jian Cheng	Electronic Engineering,China University of Mining and Technology, Xuzhou, China

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

In complex ground environment, different regions have different road conditions. Path planning for robots in such environment is an open problem, which lacks effective methods. A novel global path planning method based on common sense and evolution knowledge is proposed by adopting dual evolution structure in culture algorithms. Common sense describes ground information and feasibility of environment, which is used to evaluate and select the paths. Evolution knowledge describes the angle relationship between the path and the obstacles, or the common segments of paths, which is used to judge and repair infeasible individuals. Taken two types of environments with different obstacles and road conditions as examples, simulation results indicate that the algorithm can effectively solve path planning problem in complex ground environment and decrease the computation complexity for judgment and repair of infeasible individuals. It also can improve the convergence speed and have better computation stability.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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