ZHANG Songsong, WU Xiaojun, ZHAO He, WANG Peng, WANG Huan
（School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China）
Abstract： As an important handling tool in the modern intelligent warehousing and logistics industry, omnidirectional automated guided vehicles (AGVs) have greatly improved the efficiency of warehousing and handling operations. However, the existing omnidirectional AGVs have problems such as insufficient bearing capacity of drive structure, poor path tracking accuracy, and poor correction effect. A new type of omnidirectional AGV with two-wheel differential full steering drive mechanism is designed, and a path tracking control strategy using fuzzy neural network PID (FNN-PID) control is proposed to improve the driving performance and path tracking effect of the omnidirectional AGV. Firstly, the new differential full steering drive structure is designed, and its kinematics model is established, the new drive structure has better load-bearing performance. In addition, according to the established kinematics model, the relationship between speed and rotation angle in four different motion modes is analyzed. Finally, the FNN-PID control strategy is used to track and correct the omnidirectional AGV path, and the conjugate gradient (i.e., FR) method is used to learn and train neural network weights, which can improve the response performance of the control system. The proposed control strategy is compared with the traditional PID control strategy through simulation and semicircular path tracking experiments. The simulation and experimental results show that the proposed control strategy can quickly eliminate the deviation in 2 s. The distance deviation is within 1 cm, and the heading deviation is about 1° in the stable tracking stage. The new full steering drive structure has better drive performance and the FNN-PID control strategy using the FR method to learn and train can more effectively track and control the path. The new structure and control method have a certain significance for the precise handling of omnidirectional AGVs.
Key words： omnidirectional automated guided vehicles (AGVs); drive structure; full steering differential mechanism; fuzzy neural network; multi-variable input; path tracking
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全向AGV驱动结构和路径跟踪策略
张松松， 吴晓君， 赵鹤， 王鹏， 王欢
（西安建筑科技大学 机电工程学院， 陕西 西安 710055）
摘要：全向自动导引车（AGVs）作为现代智能仓储物流行业的重要搬运作业工具， 大大提高了仓储搬运作业效率。然而， 现有的全向AGV存在驱动结构承载力不足以及路径跟踪精度和纠偏效果不好等问题。本文设计了一款新型双轮差速全转向驱动结构的全向AGV， 并提出使用模糊神经网络PID（FNN-PID）控制策略来提高全向AGV的驱动性能和路径跟踪效果。首先， 对新型差速全转向驱动结构进行设计， 建立其运动学模型， 新型驱动结构有更好的承载性能。然后， 根据建立的运动学模型， 分析得到 4种不同运动模式下的速度和转角关系。最后， 使用FNN-PID控制策略对全向AGV进行路径跟踪和纠偏， 并使用共轭梯度（FR）法对神经网络权值学习训练来提高控制系统的响应性能。将所提出的控制策略与传统PID控制策略进行了仿真和半圆形路径跟踪实验对比。仿真和实验结果表明， 所提出的控制策略能够在2 s内快速消除偏差， 并且稳定跟踪阶段距离偏差在1 cm以内， 航向偏差在1°左右。新型全转向驱动结构具有更好的驱动性能， 且使用FR法学习训练的FNN-PID控制策略能够更有效地对路径进行跟踪控制， 该新型结构和控制方法对全向AGV精准搬运作业具有一定意义。
关键词：全向自动导引车（全向AGV）； 驱动结构； 全转向差速机构； 模糊神经网络； 路径跟踪
引用格式：ZHANG Songsong, WU Xiaojun, ZHAO He, et al． Drive structure and path tracking strategy of omnidirectional AGV． Journal of Measurement Science and Instrumentation， 2023， 14（4）： 431-441. DOI： 10．3969／j．issn．1674-8042．2023．04．006
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