Multi-strategy ensemble Harris hawks optimization for smooth path planning of mobile robots

Xinlu Zong, Yin Liu (), Zhiwei Ye and Xue Xia
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Xinlu Zong: School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, P. R. China
Yin Liu: School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, P. R. China
Zhiwei Ye: School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, P. R. China
Xue Xia: School of Computer Science, Hubei University of Technology, Wuhan, Hubei 430068, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 07, 1-31

Abstract: Efficient and safe path planning for autonomous navigation is paramount in advancing the motion control capabilities of mobile robots. To obtain the global optimal smooth path for mobile robots, a multi-strategy ensemble Harris hawks optimization algorithm (SDHHO) is proposed in this paper. The spiral search strategy is adopted to improve the early update method of the algorithm, which can improve the global exploration ability. To achieve better balance between global exploration and local exploitation, the Sine chaotic map is introduced to the escape energy, replacing random components. Furthermore, an elite differential mutation strategy combined with Gaussian mutation is designed to prevent the algorithm from falling into local optima. We compared the SDHHO algorithm with other classical and novel algorithms on 23 benchmark functions, and the results demonstrated the superiority of SDHHO. The proposed algorithm is applied to the smooth path planning for mobile robots, which is transformed into an optimization problem of control nodes of high-order Bezier curves. Empirical evaluations across diverse environments underscore the proficiency of the proposed method in generating paths characterized by reduced length and enhanced safety and continuity.

Keywords: Smooth path planning; Harris hawks optimization; Bezier curve; spiral search; elite differential mutation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124500839

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