摘要
针对移动机器人路径规划质量不高的问题,对环境建模、适应度函数建立、算法选择等方面进行了研究归纳,提出了一种基于模糊推理技术PSO算法的路径规划方法。首先,对障碍物进行了扩展处理,通过坐标系转换建立了简化的环境模型;其次,在分析传统PSO算法采用定值的惯性因子ω和学习因子c_1、c_2取值对算法性能影响的基础上,提出了改进的PSO算法,采用模糊推理技术自适应地动态调整c_1、c_2;最后,将改进PSO算法应用于提高移动机器人路径规划中。研究结果表明:相对于传统PSO与APSO算法路径规划,改进算法在复杂环境下路径长度、平滑度、运行时间方面分别最少提高了17%、14%、7%,验证了算法在路径规划方面的可行性与高效性。
Aiming at the problem of poor quality of path planning for mobile robots, a path planning method of PSO algorithm based on fuzzy reasoning technology was proposed, which focuses on environment modeling, fitness function establishment and algorithm selection.Firstly, the obstacles were extended and the simplified environment model was established by coordinate transformation.Secondly, based on the analysis of the influence of the fixed inertia factor ω and the learning factor c 1 and c 2 on the performance of the traditional PSO algorithm, an improved PSO algorithm was proposed, c 1 and c 2 were adaptively and dynamically adjusted by using the fuzzy reasoning technology.Finally, the improved PSO algorithm was applied to improve the path planning of mobile robot.The results indicate that, compared with the traditional PSO and APSO algorithm, the improved algorithm improves the path length, smoothness and running time at least by 17%, 14% and 7% respectively in complex environment, which verifies the feasibility and efficiency of the algorithm in path planning.
作者
刘彩霞
LIU Cai-xia(Baotou Vocational & Technical College, Computer Numerical ControlTechnology Department, Baotou 014030,China)
出处
《机电工程》
CAS
北大核心
2019年第4期445-450,共6页
Journal of Mechanical & Electrical Engineering
关键词
移动机器人
粒子群算法
模糊推理
路径规划
mobile robot
particle swarm algorithm
fuzzy Inference
path planning
