摘要
为进一步提升复杂场景中空间中的路径规划效果,基于改进的PSO-ACO算法,同时引入多因素地形适应度,提出一种路径规划算法。其中,将改进的PSO-ACO种群融合算法作为路径规划算法,引入多因素地形适应度对算法进行二次优化,以整体提升路径规划效果。实验结果表明,与单独的改进PSO和ACO算法相比,本研究的融合改进种群算法和多因素地形适应度的路径规划算法具有更佳的路径规划效果,在3 km、6 km、9 km、12 km的路径规模下的转折点个数仅为33,105,239,543,路径长度仅为3.25 km, 6.33 km, 10.26 km, 13.44 km,明显低于改进PSO和ACO算法;与其他路径规划算法相比,本研究算法在复杂场景下能够得到最佳的路径,同时还能够保持路径的平滑性。以上结果表明,本研究设计的路径规划算法具有优秀的路径规划效果,能够应用于实际场景中的路径规划,具有较高的可行性和使用价值。
In order to further improve the effect of path planning in the space of complex scenes,a path planning algorithm is proposed based on the improved PSO-ACO algorithm and the introduction of multi factor terrain fitness.Among them,the improved PSO-ACO population fusion algorithm is used as the path planning algorithm,and the multi factor terrain fitness is introduced to optimize the algorithm twice to improve the overall path planning effect.The experimental results show that,compared with the improved PSO and ACO algorithms alone,the path planning algorithm in this study,which combines the improved population algorithm and the multi factor terrain fitness,has a more effective path planning.Under the path scale of 3 km,6 km,9 km,and 12 km,the number of turning points is only 33,105,239,543,and the path length is only 3.25 km,6.33 km,10.26 km,and 13.44 km,which is significantly lower than the improved PSO and ACO algorithms;Compared with other path planning algorithms,this research algorithm can obtain the best path in complex scenarios while maintaining the smoothness of the path.The above results indicate that the path planning algorithm designed in this study has excellent path planning effects and can be applied to path planning in practical scenarios,with high feasibility and practical value.
作者
吴茜
WU Xi(Xi’an vocational and technical college,Xi’an 710077,China)
出处
《自动化与仪器仪表》
2024年第1期35-38,43,共5页
Automation & Instrumentation
基金
“社科助力县域经济高质量发展”(高等职业院校)专项研究课题项目(合作项目)《以区县为载体的新型城镇化驱动乡村振兴创新发展路径研究-以西安市长安区为例》(2023HZ0850)。
