Oleo strut for use on modular electric automatic guided vehicles

Author(s):  
Alexander Macfarlane ◽  
Udo Becker ◽  
Theo van Niekerk
Author(s):  
Hao Yang ◽  
Yilian Zhang ◽  
Wei Gu ◽  
Fuwen Yang ◽  
Zhiquan Liu

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.


2018 ◽  
Vol 54 (11) ◽  
pp. 1-5 ◽  
Author(s):  
Chaoqiang Jiang ◽  
K. T. Chau ◽  
Chunhua Liu ◽  
Christopher H. T. Lee ◽  
Wei Han ◽  
...  

2019 ◽  
Vol 56 (10) ◽  
pp. 101203
Author(s):  
高雪松 Gao Xuesong ◽  
李宇昊 Li Yuhao ◽  
张立强 Zhang Liqiang ◽  
陈志华 Chen Zhihua

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Qianru Zhao ◽  
Shouwen Ji ◽  
Dong Guo ◽  
Xuemin Du ◽  
Hongxuan Wang

According to previous research studies, automated quayside cranes (AQCs) and automated guided vehicles (AGVs) in automated container terminals have a high potential synergy. In this paper, a collaborative scheduling model for AQCs and AGVs is established and the capacity limitation of the transfer platform on AQCs is considered in the model. The minimum total energy consumption of automated quayside cranes (AQCs) and Automatic Guided Vehicles (AGVs) is taken as the objective function. A two-stage taboo search algorithm is adopted to solve the problem of collaborative scheduling optimization. This algorithm integrates AQC scheduling and AGV scheduling. The optimal solution to the model is obtained by feedback from the two-stage taboo search process. Finally, the Qingdao Port is taken as an example of a data experiment. Ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the two-stage taboo search algorithm since it can find near-optimal solutions, precisely and accurately.


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