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3D path planning for a robot based on improved ant colony algorithm

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Abstract

Path planning is an important issue in the field of robotics research. Compared with traditional two-dimensional (2D) path planning, three-dimensional (3D) path planning is closer to practical applications. In this paper, a new improved ant colony algorithm is proposed to solve the problem of slow convergence speed, low efficiency and the tendency of falling into the local optimal solution of the traditional ant colony algorithm for the 3D path planning. There are three main improved steps in the novel ant colony algorithm in 3D path planning. In the first step, a pseudo-random state transition strategy is adopted to ensure the global search ability of the algorithm. In the second step, the pheromone update and the pheromone increment calculation method are used to accelerate the convergence speed of the algorithm which can ensure the quality of the solution. In the end step, a security value function of the heuristic function is used to ensure the security of path. In addition, the conditional fallback method is used to ensure the global search ability of the algorithm. Simulation results show that the new improved ant colony algorithm can find a feasible three-dimensional path quickly and efficiently.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61876200, in part by the Natural Science Foundation Project of Chongqing Science and Technology Commission under Grant No. cstc2018jcyjAX0112 and in part by the Educational Reform Project of Chongqing University of Posts and Telecommunications (Grant: XJG1635) and in part by the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJ1400432).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Xingcheng Pu

  2. School of Science, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Xingcheng Pu

  3. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Chaowen Xiong

  4. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Lianghao Ji & Longlong Zhao

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  1. Xingcheng Pu
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Correspondence to Xingcheng Pu or Chaowen Xiong.

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Pu, X., Xiong, C., Ji, L. et al. 3D path planning for a robot based on improved ant colony algorithm. Evol. Intel. 17, 55–65 (2024). https://doi.org/10.1007/s12065-020-00397-6

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  • DOI: https://doi.org/10.1007/s12065-020-00397-6

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