Dynamic path planning for mobile robots based on artificial potential field enhanced improved multiobjective snake optimization (APF-IMOSO)

Abstract

With the widespread adoption of mobile robots, effective path planning has become increasingly critical. Although traditional search methods have been extensively utilized, meta-heuristic algorithms have gained popularity owing to their efficiency and problem-specific heuristics. However, challenges remain in terms of premature convergence and lack of solution diversity. To address these issues, this paper proposes a novel artificial potential field enhanced improved multiobjective snake optimization algorithm (APF-IMOSO). This paper presents four key enhancements to the snake optimizer to significantly improve its performance. Additionally, it introduces four fitness functions focused on optimizing path length, safety (evaluated via artificial potential field method), energy consumption, and time efficiency. The results of simulation and experiment in four scenarios including static and dynamic highlight APF-IMOSO's advantages, delivering improvements of 8.02%, 7.61%, 50.71%, and 12.74% in path length, safety, energy efficiency, and time-savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta-heuristics, APF-IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios. The results reveal that APF-IMOSO can generate multiple viable collision-free paths in complex environments under various constraints, showcasing its potential for use in dynamic path planning within the realm of robot navigation.