摘要
为了提高移动机器人的路径规划能力,寻找到更优的移动路径,提出了改进的人工大猩猩算法。在全局探索和局部开发阶段,通过引入惯性加权因子不同的自适应权重,提高了全局搜索能力和局部开发能力。在局部开发阶段,通过融合互利共生策略加强了个体与最优个体间的联系,提高了算法的收敛速度,通过引入黄金正弦算法进一步平衡了全局探索和局部开发能力。通过构造障碍物和基于三次样条插值的路径适应度函数,利用改进的人工大猩猩优化算法寻优求解,获得移动机器人的最优路线。通过简单障碍环境和复杂障碍环境的仿真实验证明了本文提出的改进算法的有效性和实用性。
In order to improve the path planning ability of mobile robots and find better mobile paths,an improved artificial gorilla algorithm is proposed.In the global exploration and local development stages,the introduction of adaptive weights with different inertia weighting factors improves the global search and local development capabilities.In the local exploration stage,the integration of mutually beneficial symbiotic strategies strengthens the connection between individuals and the optimal individual,improves the convergence speed of the algorithm,and further balances the global exploration and local development capabilities by introducing the golden sine algorithm.By constructing obstacles and using a path fitness function based on cubic spline interpolation,an improved artificial gorilla optimization algorithm is used to find the optimal path for the mobile robot.The effectiveness and practicality of the improved algorithm proposed in this paper are demonstrated through simulation experiments in both simple and complex obstacle environments.
作者
贾鹤鸣
游进华
李永超
李政邦
JIA Heming;YOU Jinhua;LI Yongchao;LI Zhengbang(School of Information Engineering,Sanming University,Sanming 365004,China;School of Computer Science and Mathematics,Fujian University of Technology,Fuzhou 350118,China)
出处
《新乡学院学报》
2023年第12期16-21,共6页
Journal of Xinxiang University
基金
福建省自然科学基金面上项目(2021J011128)。
