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An improved ant colony algorithm for robot path planning

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Abstract

To solve the problems of convergence speed in the ant colony algorithm, an improved ant colony optimization algorithm is proposed for path planning of mobile robots in the environment that is expressed using the grid method. The pheromone diffusion and geometric local optimization are combined in the process of searching for the globally optimal path. The current path pheromone diffuses in the direction of the potential field force during the ant searching process, so ants tend to search for a higher fitness subspace, and the search space of the test pattern becomes smaller. The path that is first optimized using the ant colony algorithm is optimized using the geometric algorithm. The pheromones of the first optimal path and the second optimal path are simultaneously updated. The simulation results show that the improved ant colony optimization algorithm is notably effective.

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Acknowledgments

This study was funded by Chinese High-tech R&D (863) Program (Grant Number 2007AA04Z232), the Natural Science Foundation of China (Grant Numbers 61075027, 91120011), and the Natural Science Foundation of Hebei province (Grant Numbers F2010001106, F2013210094).

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Authors and Affiliations

  1. College of Mechanical Engineering, Donghua University, Shanghai, 201620, China

    Jianhua Liu & Jianguo Yang

  2. College of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Jianhua Liu, Xingjun Tian & Meng Gao

  3. Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing, 100084, China

    Huaping Liu

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  1. Jianhua Liu
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  2. Jianguo Yang
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  3. Huaping Liu
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  4. Xingjun Tian
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  5. Meng Gao
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Correspondence to Jianguo Yang.

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The authors declare that there is no conflict of interests regarding the publication of this paper.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by V. Loia.

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Liu, J., Yang, J., Liu, H. et al. An improved ant colony algorithm for robot path planning. Soft Comput 21, 5829–5839 (2017). https://doi.org/10.1007/s00500-016-2161-7

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  • DOI: https://doi.org/10.1007/s00500-016-2161-7

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