摘要
针对人类学习优化算法搜索效率低、易陷入局部最优、无法实现动态避障等问题,提出一种融合改进人类学习优化算法和动态窗口算法的路径规划算法。首先,利用非线性递增和递减改进概率参数提高人类学习优化算法的收敛速率,并引入粒子群算法更新个体知识数据库与社会知识数据库,并且自适应调整惯性权重系数,避免陷入局部最优;其次,在动态窗口算法的评价函数中加入角评价函数避免与障碍物的夹角过小、动态改变速度评价函数和角评价函数权重,以调节速度及角度;最后,将改进的算法应用于自动导引车的路径规划,仿真实验表明融合算法规划路径长度比蚁群算法路径减少4%,比混合人类学习优化与粒子群算法减少15%,其他两种算法与障碍物接触次数是改进算法的5倍,减少路径长度和转折次数,提升路径的平滑性。并且成功避免在T型以及复杂地图环境的障碍物,验证所提算法的可行性。
Aiming at the problems of low search efficiency of human learning optimization algorithm,easy to fall into local optimum,and unable to achieve dynamic obstacle avoidance,a path planning algorithm integrating improved human learning optimization algorithm and dynamic window algorithm was proposed.Firstly,the nonlinear increasing and decreasing probability parameters are used to improve the convergence rate of HLO.Introduction of particle swarm algorithms to update personal and social knowledge databases.The inertia weight coefficients are adjusted adaptively to avoid falling into the local optimum.weights to adjust the speed and angle;finally,the improved algorithm was applied to the path planning of the automated guided vehicle,and simulation experiments show that the fusion algorithm plans path lengths that by 4%less than the ant colony algorithm paths,and by 15%less than the hybrid human learning optimization and particle swarm algorithms,and that the other two algorithms make contact with the obstacles by five times as many times as the improved algorithm,which reduces the length of the path and number of transitions and improves Smoothness of the path.It avoids obstacles in T-shaped and complex map environments to verify the feasibility of the proposed algorithm.
作者
王勤
魏利胜
Wang Qin;Wei Lisheng(School of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000,China)
出处
《电子测量与仪器学报》
北大核心
2025年第2期213-221,共9页
Journal of Electronic Measurement and Instrumentation
基金
安徽省教育厅重大项目(KJ2020ZD39)
安徽省高等学校省级质量工程项目(2023cxtd057)资助。
