摘要
摘要
随着科学技术的迅猛发展，拥有自动化优势，安全可靠且灵活的AGV （Automatic Guided Vehicle）在智能巡检、仓储物流等方面发挥的作用日益重要，而AGV系统应用中的关键问题便是路径规划与动态避障，因此研究路径规划与动态避障算法方面的技术有着极为重要的实际意义。
本文对传统AGV的路径规划和自主避障传统算法进行深入研究，优化蚁群算法以及引入模糊神经网络改进动态窗口法，进而提高机器人的运行效率以及其可靠性，同时通过ROS系统操控机器人行走并自动避障实验，验证算法的实际效果。
首先，对ROS（Robot Operating System）系统进行学习研究，该系统具有开源分布式操作系统的特点，能够解决开发机器人时代码重复化和兼容差的难题，便于后续的研究开发。因此，本论文选用ROS作为移动机器人的软件系统，操控移动机器人进行实验。
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其次，针对ACO（蚁群优化）算法存在收敛速度慢以及易陷入局部最优的问题，将改进的人工势场算法和蚁群算法相结合，减少了ACO算法初始规划的盲目性。利用A*算法的评估函数以及路径转折角度，引入启发信息递增函数，改进信息素更新机制和路径评价函数等措施来改进蚁群算法，搭建仿真环境进行实验。结果表明，改进提升了蚁群算法的收敛速度和最优解。
然后，在对移动机器人自主避障算法研究的基础上，本文对传统的经典避障算法动态窗口法进行优化。改进动态窗口法轨迹评价函数，发挥全局路径规划出的路径的最优性，同时结合模糊神经网络，根据环境信息对动态窗口法的评价函数权重进行动态调整，改进动态窗口法的规划效率。最后进行仿真实验验证，结果证明改进的规划算法在移动机器人自主避障方面的有效性以及实时性。
最后在搭建的室内实验环境中，随机设置障碍物位置，使用ROS系统导入改进算法运行Turtlebot3机器人，使其能够穿行并自主避障，以此测试本文算法的实际效果。实验数据表明在路径长度、路径平滑度与跟踪时间等性能上，本文改进的算法比起原算法都具有更好的性能和表现。该研究成果有效解决了机器人路径规划和自主避障的相关问题，有益于机器人自主导航领域的发展。
关键词：AGV路径规划自主避障蚁群算法ROS
Abstract
Abstract
With the rapid development of science and technology,AGV(automatic guided vehicle),which has the advantages of automation,safety,reliability and flexibility, plays an increasingly important role in intelligent inspection,warehousing and logistics.The key problem in the application of AGV system is
path planning and dynamic obstacle avoidance,so it is of great practical significance to study the technology of path planning and dynamic obstacle avoidance algorithm.
In this paper,the traditional AGV path planning and the traditional algorithm of autonomous obstacle avoidance are deeply studied,the ant colony algorithm is optimized and the dynamic window method is improved by introducing fuzzy neural network,so as to improve the operation efficiency and reliability of the robot.At the same time,the actual effect of the algorithm is verified by the robot walking and automatic obstacle avoidance experiment controlled by ROS system.
First of all,the ROS(robot operating system)system is studied.The system has the characteristics of open source distributed operating system,which can solve the problem of code duplication and poor compatibility when developing robots,and replace the follow-up rearch and development.Therefore,in this paper,ROS is lected as the software system of mobile robot to carry out the experiment of mobile robot.
Secondly,to solve the problems of slow convergence and easy to fall into local optimum in ACO algorithm,the improved Artificial Potential Field algorithm is combined with ACO to reduce the blindness of initial planning.Using the evaluation function and path turning angle of A*algorithm,the p
aper introduces heuristic information increasing function,improves pheromone updating mechanism and path evaluation function to improve ant colony algorithm.The simulation results show that the improved algorithm promotes the convergence speed and the optimal solution.
Then,bad on the rearch of autonomous obstacle avoidance algorithm of mobile robot,the traditional dynamic window algorithm is optimized.The track evaluation function of dynamic window method is improved to give full play to the optimality of global path.At the same time,combining with fuzzy neural network,the weight of evaluation function of dynamic window method is adjusted dynamically according to the environmental information to improve the planning efficiency of dynamic window method.Lastly,the simulation results show that the improved
Abstract
planning algorithm is effective and real-time in autonomous obstacle avoidance of mobile robot.
Finally,in the built indoor experimental environment,the obstacle position is t randomly,and the improved algorithm is introduced into the ROS system to run the turnlebot3robot,so that it can walk through and avoid obstacles independently,so as to test the actual effect of the algorithm in this paper.Experimental data show that the improved algorithm has better performance than the original 谨慎行事
algorithm in path length, path smoothness and tracking time.The rearch results effectively solve the problems related to robot path planning and autonomous obstacle avoidance,which is beneficial to the development of robot autonomous navigation field.
Key words:AGV Path planning Autonomous obstacle avoidance Ant colony algorithm ROS
目录
如何申请哈佛大学摘要............................................................................................................................II 第一章绪论.. (1)
第二章ROS操作系统与导航避障模块设计 (8)2014六级真题
2.1ROS操作系统 (8)
2.1.1ROS基本概念 (8)
well是什么意思
2.1.2ROS的功能及优点 (10)
2.2ROS实验平台turtlebot3及其运动方程建模 (10)
2.2.1turtlebot3硬件结构 (10)
2.2.2Turtlebot3运动方程建模 (14)
橙子的英文
2.3ROS软件系统模块 (15)
2.3.1ROS Navigation Stack (15)
2.3.2人机交互 (17)
2.4本章小结 (19)
第三章改进蚁群算法的全局路径规划技术 (20)
3.1引言 (20)
3.2最大最小蚁群系统 (20)
3.3改进的人工势场算法 (21)
3.4蚁群算法改进 (22)
3.4.1改进启发函数 (22)
3.4.2改进状态转移规则 (22)
3.4.3改进信息素更新规则 (23)
3.5改进算法仿真结果及分析 (24)
3.6本章小结 (28)
第四章局部路径规划算法研究 (29)
行尸走肉终点站
4.1引言 (29)
4.2动态窗口法 (29)
4.2.1移动机器人运动学模型 (29)
4.2.2速度矢量采样空间 (30)
4.2.3轨迹评价函数 (31)
4.3改进DWA算法 (31)
4.3.1改进评价函数 (31)
4.3.2基于进化二型量子模糊神经网络的动态窗口法 (32)
4.4仿真结果实验对比 (37)
4.5本章小结 (39)
第五章实验验证 (40)
5.1引言 (40)
5.2测试方案 (40)
affecting
5.3实验结果与分析 (41)
5.4本章小结 (44)美国恐怖故事电视剧
第六章总结与展望 (45)
致谢 (47)
参考文献 (48)
作者简介 (52)
攻读硕士期间研究成果 (53)感叹句
