Path Planning Analysis of Mobile Robot with Improved Ant Colony Algorithm

Gaofeng Wu; Henry Dyke A. Balmeo

doi:10.62677/IJETAA.2403110

Authors

Gaofeng Wu Graduate School, University of the East, Manila, Philippines Author
Henry Dyke A. Balmeo University of the East, Manila, Philippines Author

DOI:

https://doi.org/10.62677/IJETAA.2403110

Keywords:

Mobile robot, Path planning, Ant colony algorithm, Heuristic function, Pheromone update method

Abstract

Aiming at the problems of poor convergence and local optimization in path planning using the basic ant colony algorithm, this paper studies an improved ant colony algorithm to enhance the effect of mobile robot path planning. Firstly, the state transition probability of the ant colony algorithm is modified, and the influence of the angle on node selection is increased by adding a new angle index heuristic function. Then, the sorting and elite ant colony algorithm strategies are fused to research a path length difference pheromone update method, improving the efficiency of the ant colony algorithm in planning the optimal path. Finally, through a comparison with the basic ant colony algorithm simulation on MATLAB, the feasibility and effectiveness of the improved ant colony algorithm are verified.

Downloads

Download data is not yet available.

References

Y. Luo, G. Zhu, W. Qian, et al., “Algorithm aversion in the era of artificial intelligence: Research framework and future agenda,” Journal of Management World, vol. 39, no. 10, pp. 205–233, 2023.

Z. Yu, Q. Li, Q. Fan, “An overview of the application of intelligent bionic algorithms in the optimization of mobile robot path planning,” Application Research of Computers, vol. 36, no. 11, pp. 3210–3219, 2019.

Z. Wang, X. Hu, X. Li, et al., “Overview of global path planning algorithms for mobile robots,” Computer Science, vol. 48, no. 10, pp.19–29, 2021.

W. Zhao, C. Jiang, “A* global path planning algorithm for two-stage search,” Computer Applications and Software, vol. 37, no. 12, pp. 249253, 2020.

K. Yang, J. Long, X. Ma, et al., “Study on the path planning method for mobile robot to improve artificial potential field,” Modern Electronics Technique, vol. 43, no. 7, pp. 141–145, 2020.

Y. Song, Y. Zhang, Q. Yao, et al., “Path following control method of caterpillar robot based on heuristic dynamic planning,” Transactions of The Chinese Society of Agricultural Machinery, vol. 50, no. 11, pp.24–33, 2019.

C. Wang, J. Mao, “Two-way robot path planning method based on time window model,” Computer Engineering and Applications, pp. 1–11, Nov. 2020, doi: 10.3778/j.issn.1002-8331.2009-0331.

X. L. Dai, S. Long, Z. W. Zhang, et al., “Mobile robot path planning based on ant colony algorithm with A* heuristic method,” Frontiers in Neurorobotics, vol. 13, pp. 15, 2019.

X. Han, X. Li, Y. Fan, et al., “A hybrid optimization algorithm for global path planning,” Ship Science and Technology, vol. 43, no. 7, pp.149–154, 2021.

Q. Shao, W. Shi, “Research on robot path planning based on improved ant colony algorithm,” Modern Manufacturing Engineering, no. 6, pp.46–51, 2023.

M. Jiang, F. Wang, Y. Ge, et al., “Path planning for mobile robot based on improved ant colony algorithm,” Chinese Journal of Scientific Instrument, vol. 40, no. 2, pp. 113–121, 2019.

L. Tu, L. Li, G. Lin, “Orchard mobile robot path planning based on improved ant colony algorithm,” Machine Tool & Hydraulics, vol. 47,no. 23, pp. 69–73, 2019.

Y. Xu, K. Lou, Z. Li, “Path planning for mobile robot based on variablestep ant colony algorithm,” Journal of Intelligent Systems, pp. 1–9, Nov.2020, doi: 10.3969/j.issn.1673-677X.2020.000.

Y. Wan, L. Peng, “Improved A* ant colony algorithm to solve robot path planning problem,” Transducer and Microsystem Technologies, vol. 38, no. 12, pp. 153–160, 2019.

Z. Chen, X. Han, “Application of improved ant colony algorithm in mobile robot path planning,” Computer Engineering and Design, vol. 41, no. 8, pp. 2388–2395, 2020.

J. Liu, Z. Liu, H. Zhang, et al., “Path planning of mobile robot based on fuzzy control ant colony algorithm,” Modular Machine Tool & Automatic Manufacturing Technique, no. 1, pp. 20–24, 2023.