A hybrid artificial bee colony algorithm with genetic augmented exploration mechanism toward safe and smooth path planning for mobile robot

Abstract

Path planning is important for mobile robot to ensure safe and efficient navigation. This paper proposes a hybrid artificial bee colony with genetic augmented exploration mechanism (HABC-GA) that enables mobile robot to achieve safe and smooth path planning. Considering the characteristics of path planning problem, a mathematical model is constructed to balance three objectives: path length, path safety, and path smoothness. In the employed bee phase, a genetic augmented exploration mechanism is designed, which encompasses redesigned path crossover, adaptive obstacle-aware mutation, and dynamic elite selection operators. In the onlooker bee phase, an objective-guided optimization strategy is investigated to improve local search ability. In the scout bee phase, a dual exploration restart strategy is developed to increase the activity of individuals in the population, in which stagnant individuals in the evolution are replaced by more promising ones. Finally, the proposed HABC-GA is compared with five efficient algorithms in 24 instances of six representative environments. Simulation results demonstrate the effectiveness and high performance of HABC-GA in obtaining safe and smooth paths.

Keywords

Mobile robot / Path planning / Hybrid artificial bee colony with genetic augmented exploration mechanism / Objective-guided optimization strategy / Dual exploration restart strategy

CRediT authorship contribution statement

Fan Ye: Writing - original draft, Validation, Software, Methodology, Conceptualization. Peng Duan: Writing - review & editing, Validation, Funding acquisition. Leilei Meng: Funding acquisition. Lingyan Xue: Validation, Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the Opening Fund of Shandong Key Laboratory of Ubiquitous Intelligent Computing, China, the National Natural Science Foundation of China (52205529), the Natural Science Foundation of Shandong Province, China (ZR2021QE195 and ZR2021MD090), and the Discipline with Strong Characteristics of Liaocheng University-Intelligent Science and Technology, China (319462208).

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