【作者】 王健；

【导师】 朱欣华；

【作者基本信息】 南京理工大学 ， 仪器仪表工程（专业学位）， 2018， 硕士

【摘要】 随着现代企业仓储规模的不断扩大,越来越多的企业在仓储系统中引入了智能无人车进行搬运、分拣等工作。而好的仓储用智能无人车路径规划算法,可以有效提高仓储系统的工作效率和安全性。本文对仓储用智能无人车的路径规划算法进行了研究。首先在概述了仓储用智能无人车技术的发展和常用路径规划算法的基础上,分析了几种常用路径规划算法的原理,从仓储用智能无人车的全局路径规划和局部路径规划两个部分进行研究。考虑到仓储系统的环境以及无人车的工作效率,重点研究以距离短和计算量小为优化目标的路径规划算法。针对全局路径规划部分,以经典A*算法为基础,提出了基于拓扑化环境模型的改进A*算法,在成功搜索到全局最优路径的同时能够有效降低搜索过程中的计算量。针对局部路径规划部分,以经典人工势场法为基础,提出了改进人工势场法,通过改进斥力势场函数,解决了目标不可达问题。在此基础上,通过设置子目标点的方法,解决了局部极小值问题。然后将全局路径规划算法和局部路径规划算法进行融合,提出了仓储无人车路径规划混合算法,通过决策模块以适应不同的仓库环境,仿真结果证明了该方法的可行性。最后设计了两轮差速驱动无人车的运动控制算法和上位机软件,并搭建了无人车硬件实验平台,通过实验验证了本文提出算法的有效性和实用性。更多还原

【Abstract】 With the continuous expansion of the storage scale of modern enterprises,more and more enterprises have introduced intelligent unmanned vehicles for handling and sorting in the storage system.And a good intelligent vehicle path planning algorithm for intelligent unmanned vehicles can effectively improve the efficiency and security of the storage system.The path planning algorithm of intelligent unmanned vehicle for storage is studied in this paper.Based on the overview of the development of the technology and the common path planning algorithm of the intelligent self driving vehicle,the principles of several commonly used path planning algorithms are analyzed,and the two parts of the intelligent vehicle’s global path planning and local path planning are studied.Considering the environment of the storage system and the efficiency of the unmanned vehicle,we focus on the path planning algorithm based on the short distance and the small amount of calculation.Aiming at the global path planning part,based on the classical A*algorithm,an improved A*algorithm based on the topology environment model is proposed.It can effectively reduce the amount of computation in search process when searching the global optimal path successfully.Aiming at the partial path planning part,based on the classical artificial potential field method,an improved artificial potential field method is proposed,and the target unreachable problem is solved by improving the repulsive potential field function.On this basis,the local minimum problem is solved by setting the method of subtarget point.Then,we integrate the global path planning algorithm and the local path planning algorithm,and propose a hybrid algorithm of path planning for warehousing autonomous vehicle,which is adapted to different warehouse environments through decision modules.Simulation results prove the feasibility of the method.Finally,the motion control algorithm and software of two wheel differential driving self driving vehicle are designed,and the hardware experimental platform of the driverless vehicle is built.The effectiveness and practicability of the proposed algorithm is verified by experiments.更多还原