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Mobile robot path planning based on an improved ACO algorithm and path optimization

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Abstract

This paper will improve the fundamental ant colony optimization algorithm in the context of mobile robot path planning in response to its flaws, which include easy descent into a local optimum, a large number of inflection points along the path, and an inefficient convergence speed. Based on the two-dimensional grid map modeling approach, map pheromone initialization is unevenly assigned to improve the blindness of the ant colony optimization algorithm in the initial phase of path planning. Furthermore, an adaptive pheromone volatility factor is proposed to improve the global characteristics of early path planning, accelerate the convergence speed as the iteration advances, and avoid a local optimum that could impede the algorithm's progress. The pheromone update mechanism was redesigned, and a reward and punishment mechanism was incorporated into the pheromone update function to accelerate the convergence of the algorithm. Finally, path optimization diminishes the quantity of inflection points along the path, which makes the path smoother and shortens the path length. The outcome of the experiments demonstrate that the improved ant colony optimization algorithm performs better, converges more quickly, and more closely matches the needs of actual mobile robot walking.

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Data availability

The datasets generated during and/or analysed during the current study are not publicly available due [REASON(S) WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the Key Project of Science and Technology Innovation (2030) supported by the Ministry of Science and Technology of China (No. 2018AAA0101301), the Natural Science Foundation of Guangdong Province (No.2024A1515011838), the Key Research Platforms and Projects of High School in Guangdong Province (No. 2023ZDZX1028, 2023ZDZX1050), the Distinctive Innovation Projects of High School in Guangdong Province (No. 2021KTSCX192), Dongguan Social Development Science and Technology Project (No. 20211800904722) and Dongguan Science and Technology Special Commissioner Project (No. 2021180050007)

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  1. School of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523808, China

    Tianfeng Zhou & Wenhong Wei

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  1. Tianfeng Zhou
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Correspondence to Wenhong Wei.

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Zhou, T., Wei, W. Mobile robot path planning based on an improved ACO algorithm and path optimization. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19370-x

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