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Improved ant colony algorithm in path planning of a single robot and multi-robots with multi-objective

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Abstract

In order to solve the problem of path planning of a single robot and multi-Robots with multi-objective, an improved ant colony algorithm is proposed.In the improved ant colony algorithm, both local and global optimal searching are considered. In local optimal searching, gradient distribution is used to initialize the pheromone concentration to improve the convergence speed of the algorithm. The self-adaptive impact factor is used to enhance better fitness of the algorithm in different environments. In global optimal searching, a reward and punishment mechanism has been established to improve the pheromone update strategy. Furthermore, a node rearrangement strategy is adopted to increase the diversity of solutions. According to the improved colony algorithm, the problem of path planning of a single robot and multi-Robots with multi-objective can be solved. In two different scenarios, compared experiments show that the improved colony algorithm can obtain better path length, convergence speed and running time than traditional ant colony algorithm (ACO) and the improved ant colony algorithm (IACO-SFLA) in path planning of a single robot. Simulation results also show that the improved ant colony algorithm has good ability in multi-objective path planning. Especially, all robots can safely reach the targets by moving simultaneously and obstacles can be effectively avoided too.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61876200, in part by Chongqing Postgraduate Research Innovation Project (CYS21312), in part by Science and technology research Project of Chongqing Education Commission (Grant No.KJZD-M202001901)) in part by General projects of Chongqing Science and Technology Commission (Grant No. cstc2020jcyj-msxmX0895 ) and in part by by key research and development program of Anhui province under Grant No. 202004a05020010.

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Author notes

  1. Xinlin Song, Ling Tan and Yi Zhang contributed equally to this work.

Authors and Affiliations

  1. College of Mathematics and Computer Science, Tongling University, Tongling, 244061, China

    Xingcheng Pu

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

    Xingcheng Pu & Xinlin Song

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

    Ling Tan

  4. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Yi Zhang

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  1. Xingcheng Pu
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Correspondence to Xinlin Song.

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Pu, X., Song, X., Tan, L. et al. Improved ant colony algorithm in path planning of a single robot and multi-robots with multi-objective. Evol. Intel. 17, 1313–1326 (2024). https://doi.org/10.1007/s12065-023-00821-7

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