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Material Handling Systems
Published in Susmita Bandyopadhyay, Production and Operations Analysis, 2019
Automated Guided Vehicle (AGV) (Figure 14.4) is a computer controlled battery operated mobile vehicle for transporting goods from one place to another place in production floor. AGV generally runs on a guide path although AGVs can also be run without a fixed guide path over the factory floor. Thus, AGVs can run on wired guide path, magnetic or colored tape guide path, laser guide path, inertia driven guide path, RFID (Radio Frequency Identification) technology enhanced guide path, or free-ranging AGV. Some of the major types of AGVs depending on the use are: Towing AGVsUnit load AGVsLight load AGVsFork truck AGVsClamp AGVsPallet truck AGVsVNA AGVsAssembly line AGVsHybrid AGVs
Types of Robols and Their Integration into Computer-Integrated Manufacturing Systems
Published in Ulrich Rembold, Robot Technology and Applications, 2020
Here, proximity, touch, and vision sensors are successfully combined and applied to control the navigation of an AGV. Sonic, optical range (laser, infrared), and video image sensors are the basic components of collision avoidance systems. Collision avoidance is performed in three steps. If an obstacle is detected and identified, the normal control of the vehicle is interrupted. A collision avoidance algorithm is activated, and it modifies the route or selects an alternative path. As soon as the obstacle has been passed, a search algorithm is activated to bring the vehicle back to the original path. A new research topic is the control of a traffic scene in which multiple vehicles are on a possible collision course.
System Definition
Published in Douglas Brauer, John Cesarone, Total Manufacturing Assurance, 2022
AGVs are basically independent vehicles, rolling along from station to station under their own power and control. Some are guided by wires or painted lines on the floor, others by dead reckoning with periodic alignment checks at known landmarks. They are generally powered by on-board batteries, lasting at least an 8-hour shift before needing recharging. Often, they will have a “safety bumper,” a large loop of some flexible material on the front surface which will detect collisions, and stop the vehicle, before the main mass of the AGV does any damage to itself or other objects in its way. Some AGVs use optical or sonar devices (e.g., LIDAR) for collision detection.
AGV dispatching algorithm based on deep Q-network in CNC machines environment
Published in International Journal of Computer Integrated Manufacturing, 2021
Kyuchang Chang, Seung Hwan Park, Jun-Geol Baek
In this section, we describe how the actual manufacturing process functions and the procedure for building a simulator for DQN modeling. First, the working procedure of the process is described. The problem to be solved in this study concerns an actual manufacturing environment composed of multiple CNC machines. Figure 1 displays the facility layout of the target process. Twenty CNC machines for processing metal boards were placed in two rows in parallel. Ten were placed in the upper row and ten in the lower row. The CNC machines were fixed in place with fixed functions. For example, when CNC 1 completes a function, there is a finished product that must be sent elsewhere. CNC 12 is in the process of making smartphone boards. The CNC machines transmit their working status remotely. Therefore, an AGV can obtain the working status of each CNC. An AGV operates between the two rows of CNC machines, e.g. feeding materials or conveying the work in process. An AGV is a portable robot that is used frequently in industrial applications to move materials around a manufacturing facility or warehouse. For a CNC to begin working, the AGV must supply it with the necessary raw materials. After the CNC machine finishes processing, e.g. a completed smartphone board, the AGV takes the finished product and supplies new materials.
A method for reliability detection of automated guided vehicle based on timed automata
Published in Systems Science & Control Engineering, 2021
Xuefeng Deng, Bingqian Zhou, Xinyi Sun, Hua Yang, Lingyu Chen
Path planning and navigation are the core technologies of the AGV. In our work, path planning and navigation are carried out based on the centreline of the road network. In path planning, the extraction of the centre line is particularly important. The navigation part of the AGV is completed by the camera module and the core control module. First, the core control module sends instructions to the camera module, and the camera module starts to work. At this time, the camera module starts to acquire the information of the surrounding environment in real-time and sends the information back to the core control module. Then the core control module uses the centreline method to carry out path planning. The method of centreline path planning is divided into the following five steps: (1) get the road network information. (2) the road network is divided into several edge pairs according to the road edge information. (3) constraint triangulation networks for each pair of edge lines are established respectively. (4) the centreline of each sideline pair unit is extracted according to the constrained triangulation net. (5) connect the centreline of each sideline pair unit according to the connection rules to generate the centreline of the road network. The system extracts the centre line and realizes the road planning by this method. According to the result of path planning, the core control module sends the signal back to the motor drive module, and then the motor drive module controls the rotation of the wheel to realize the navigation function.
Expected distances and alternative design configurations for automated guided vehicle-based order picking systems
Published in International Journal of Production Research, 2022
Francisco J. Aldarondo, Yavuz A. Bozer
The number of AGVs in the system plays a critical role not only because one must ensure that the pods are retrieved and stored at a rate needed to support the PSs but also because the AGVs represent a significant investment. In current applications, the number of AGVs per installation ranges from 50 to several hundred (Bozer and Aldarondo 2018), and each AGV/robot, such as the MiR100 by the RG Group, may cost over $32,000. Citing Amazon's VP in Robotics, Ackerman (2018) reports that over 100,000 AGVs are deployed in Amazon's global fulfilment network.