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Journal of Electrical and Computer Engineering
Volume 2016, Article ID 3620895, 10 pages
Research Article

Disordered and Multiple Destinations Path Planning Methods for Mobile Robot in Dynamic Environment

1School of Computer Science and Engineering, Big Data Computing Key Laboratory of Hebei Province, Hebei University of Technology, No. 5340 Xiping Road, Shuangkou, Beichen District, Tianjin 300401, China
2School of Computer Science and Engineering, Hebei University of Technology, No. 5340 Xiping Road, Shuangkou, Beichen District, Tianjin 300401, China
3School of Information Engineering, Tianjin University of Commerce, Tianjin, China

Received 30 November 2015; Accepted 10 May 2016

Academic Editor: Sook Yoon

Copyright © 2016 Yong-feng Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the smart home environment, aiming at the disordered and multiple destinations path planning, the sequencing rule is proposed to determine the order of destinations. Within each branching process, the initial feasible path set is generated according to the law of attractive destination. A sinusoidal adaptive genetic algorithm is adopted. It can calculate the crossover probability and mutation probability adaptively changing with environment at any time. According to the cultural-genetic algorithm, it introduces the concept of reducing turns by parallelogram and reducing length by triangle in the belief space, which can improve the quality of population. And the fallback strategy can help to jump out of the “U” trap effectively. The algorithm analyses the virtual collision in dynamic environment with obstacles. According to the different collision types, different strategies are executed to avoid obstacles. The experimental results show that cultural-genetic algorithm can overcome the problems of premature and convergence of original algorithm effectively. It can avoid getting into the local optimum. And it is more effective for mobile robot path planning. Even in complex environment with static and dynamic obstacles, it can avoid collision safely and plan an optimal path rapidly at the same time.