In order to define the collision avoidance, we have to study the different types of possible collision situations. There are two different collision situations, the frontal and the side collision. It is sufficient to have three ultra- sonic distance sensors on the front of the AGV as shown in Fig. 2. The three distances (R L ,R M ,R R ) are measured by the three obstacle sensors. (R L ,R M ,R R ) give sufficient information in finding a strategy to be able to avoid the frontal collision situations. However the observations to avoid the side collision are not so simple. Having the pre- conditions of motionless and avoidable obstacles, we have a chance to use the obstacle distance measurements of the near past for scanning the boundaries of the obstacles. Collecting the previous measurements of the left and right obstacle sensors and the corresponding positions of the AGV (measured by the motion sensors on the wheels), we can approximate the boundaries of the obstacles by dis- crete points. We call these points unsafe, or risky points. The distance measured by an obstacle sensor means the existence of a potential obstacle outside the circle defined by the position of the sensor and the measured value (see Fig. 3). Having more measurements and more positions, we can approximate the boundaries of the obstacles by the pair point of intersection of these circles (see Fig. 3). The main idea of the side collision avoidance strategy is to avoid unsafe points. In order to have observations eas- ier to be handled, we calculate the actual maximal left and right turning angle without side collision (α ML , α MR ) (see Fig. 4). These values are normalized to [0,1] by being divided by π/2 .
The main scope of this paper is to design a robot that automatically moves pallets around a model warehouse floor. In this paper, the requirement of the conveyors and pallet trolleys etc., to handle material transfer in heavy industries is eliminated. For this purpose, an automatedguidedvehicle controlled by micro controller is designed. Battery, micro controller and motor driving mechanism are the three mechanisms this "AutomatedGuidedVehicle”. These AGV’s are more flexible, highly intelligent and has versatile material-handling systems. It offers an alternative to fixed-path conveyors and overhead materials-handling equipment’s. It also provides a very suitable answer for the problem of integrating a new automated transportation line into an available transportation environment. The vehicle path is controlled automatically by using micro controller. The rechargeable battery is supplying power to the automatedguidedvehicle. It is equipped with an obstacle detection system which helps the system to avoid collisions during operation. Infrared sensors are used for the above purpose.
system as explained in Figure 1-1 would consist of a chassis and drivetrain and will be used as a platform base for testing. The physical design will make use of an electric drivetrain and intelligent motor drive system. The power for the motor drive will be controlled via a high current controller getting its instructions from a data acquisition (DAQ) unit that is connected to a computer where the image processing and control is done. Thus, the AGV would be controlled using computer-based software. The software choice will be LabVIEW from National Instruments. LabVIEW allows for easy direct hardware interface and rapid prototyping of software designs before the final physical system is implemented.
Zone planning is an important method to avoid deadlock. There are two types of zoning systems: static zoning and dynamic zoning. In case of a static zoning, the guide-paths are divided into several zones. When a vehicle arrives at a zone, the controller checks for the presence of another vehicle in this zone. If a vehicle is already traveling in this zone, then the vehicle intended to enter that zone has to wait until the other has passed. In case of a dynamic zoning strategy, zones are not fixed; they can be changed according to the traffic flow in the system. Ho (2000) presents a dynamic-zone strategy for vehicle-collision prevention. His method relies on two procedures: the Zone Adjustment Procedure and the Zone Assistance Procedure. With the Zone Adjustment Procedure, the area of each zone changes according to the current production demand. The Zone Assistance Procedure allows vehicles to help each other so that the workload of every vehicle is balanced over time. Reveliotis (2000) proposes a zone control strategy that determines vehicle routes incrementally, one zone at a time. Routing decisions are the results of a sequence of safety and performance considerations, with the former being primarily based on structural/ logical rather than timing aspects of the system behavior.
67 to the AGV’s controller to manipulate its movements. The IEEE 1394 port is also accessed with the program so that the images from the camera can be received and processed into a panoramic image in real time. Figure 4.18 shows a screen shot of the software written. In the figure there are three additional labels: Sensor values shows where the feedback of the proximity sensors can be seen. Wheel position indicates where the feedback from the wheel sensors can be seen. As the AGV moves a path is drawn on the screen, which indicates how the AGV has moved in space. Processed image indicates where the new processed image is rendered on the screen. The software also allows the user to drive the AGV manually by using the NUM PAD of a standard keyboard. On the NUM PAD button 8 moves the AGV forward, 4 turns it left, 6 turns it right and 2 makes it reverse. If all the buttons are released a command is sent to the AGV to stop. Appendix B shows the code used for the image processing.
An AutomatedGuidedVehicle (AGV) is a mobile robot that follows marker or wires in the floor, or uses vision or lasers. They are frequently utilized as a part of modern applications to move materials around a manufacturing facility or a warehouse. AutomatedGuidedVehicle (AGV) is a very important part of all, for it's a kind of delivery vehicle that can direct itself by following the program and the guide route to do the motions like moving forward, stopping and turning as well as to stop at the programed work stations and load, unload goods.
With the technological advancement in the field of machineries, there have been various attempts to improve the material handling techniques. AGV (Automated Geared Vehicle) is one of the remarkable machine which helps in various tasks such as fork lifting objects, towing, product transportation etc., without the continuous monitoring of human. An AGV works with the simultaneous processing of various parts. The control device which is common to both the driving device and transfer device operates the vehicle and maintains the ultimate process of automatedguidedvehicle. Proximity sensors are set up to detect the vehicle movement which directly controls the start and stop process of AGV. Photo sensors are incorporated to detect the material or object in the station. A material transfer system includes loading and unloading of material through set of specific device, in which the electrical connections are interconnected. The first AGV developed by A.M.Barnet (1953) who used overhead wire to navigate the vehicle in grocery shop. The use of AGV has grown enormously since their introduction, the number of area of application and variation type has increased significantly. Recently AGV extended their popularity to other application. Depak punithe (IJRAS august 2013) developed an AGV to betterment public health care system. we can use AGV as serving robot in hotel, material handling robot in warehouse and improve the health care system. At manufacturing area AGV are capable to transport all type of material related to manufacturing process. According to Gotte (2000) the usage of AGV will pay off for manufacturing environment (like distribution, transportation, and transshipment) with repeating transpiration pattern. He described different available technology for automation in container terminal. The control device receives signal from the transfer device once transferring gets completed and transmits signal to the driving system to move the vehicle to the next destination point. In accordance with the flow path, the colored tape method is best suited to this vehicle for best outcome. The best flow path is designed considering all aspects. It is a battery powered vehicle in which it charges automatically. Inductive power transfer methods were implemented in the vehicle to enhance better performance. Although, most of the AGVs use some mark or defined path to move on, works are going on to develop such an AGV having artificial intelligence which can be dynamic in the sense of navigation and whose locomotion is not limited to just retrofit workspace.
Abstract - The Automatic GuidedVehicle (AGV) refers a type of system that can be used in production as well as in other industries. This system includes a battery operated remote sensing locomotive (carrier) on which a small lift is provided, specific path over which it moves, sensors for sensing the obstructions on the path of the carrier. The main focus of this study is to make AGVs with the convenient materials, simple and applicable routing system and more importantly reducing the cost and increase the flexibility. In this paper is to build a prototype of an AutomatedGuidedVehicle (AGVs) model that can move on a flat surface with its four driving wheels. The prototype is able to follow line on floor with the Arduino mega microcontroller as it main brain that control all the navigation and responses to the environment. The ability to follow line on floor is an advantage of this prototype as it can be further developed to do more complicated task in real life. To follow the line, the microcontroller is attached to a sensor that continuously reflects to the surface condition. It has also been attached with an ultrasonic sensor for the detection of object. In this paper implicates of designing and fabrication of the hardware and circuitry. AGV is therefore suitable for automating material handling in batch production and mixed model production.
V. O PERATING C OSTS A S S CALE O F E CONOMY Contrary to man-operated industrial trucks the operating costs of AGVS are only marginally affected by the development of the labor costs. From this it results that relating to the labor costs a high calculative planning reliability can be achieved in the long-term. This is a general advantage of all automated material flow systems. On the assumption that the labor costs will rise even more strongly in the future than in the past, AGVS will increase above average in comparison to personnel intensive material flow systems.
According to Vivaldini, Rocha, Becker, and Moreira (2015), the major design challenge of an AGV system is to assure that vehicles efficiently arrive to the desired destinations at the desired time within highly dynamic environments so that traffic conflicts, machine overloads, starvations, and other unpredicted events will be avoided. The most common approaches to manage the coordination among AGVs are dispatching and scheduling. Original AGV dispatching was defined as a function that assigns transportation tasks to vehicles, where scheduling determines the time at which vehicles should enter and leave the guide-path segments to avoid conflicts (Langevin, Lauzon, & Riopel, 1996). However, in recent years, scheduling becomes a task allocation process for AGVs considering the time and cost of operations (Corréa, Langevin, & Rousseau, 2007). A scheduling system can decide when, where, and how a vehicle performs tasks including the route it should take (Le-Anh & De Koster, 2006). With an on-line scheduling system, these decisions are specified and updated after a time horizon (Yang, Jaillet, & Mahmassani, 2004).
Extending these advantages of industrial trucks by means of automation technology results in increased reliability and reduced operating costs. The outcome is the so called AutomatedGuidedVehicle System, abbreviated as AGVS. AGVS are capable of performing transportation tasks fully automated at low expenses. Applications can be found throughout all industrial branches, from the automotive, printing and pharmaceutical sectors over metal and food processing to aerospace and port facilities. The increasing interest in AGVS is reflected in the sales figures which reached a new peak in 2006.
In the general terms the mobile robot can be defined as mobile systems equipped with sensors to interact with the external environment and navigate with it when trying to achieve several objectives. There are several mobile robots that can change their location through movements such as Automatic Guided Vehicles (AGV). Figure 2.2 shows an AVG that are used to transport a motor blocks from the assembly station to another (Roland Siegwart and Illah R. Nourbakhsh, 2004).
Nowadays, several types of industries have practiced the technology of automatedguidedvehicle (AGV) as the medium or transportation for material or product from one location to another location. The technology of AGV has been in existence since 1953, and this system was first introduced in the 1960s for industrial applications (Yaghoubi et al. 2012).
This project is about to create and develop the AutomatedGuidedVehicle (AGV) which is by considering three major parts that is design and build a prototype of an AGV, develop the control system for AGV and improve the motion of the AGV by using a suitable controller and improving the design that may occurring problem to the motion of AGV. This project involves of parts from sketching, drawing, measuring each dimension to the control system part which involves computing wiring system and software application to ensure the AGV can run perfectly. This project is proposed to design an AutomatedGuidedVehicle point-to- point motion control. An AGV is fabricated by using DC motor and a basic controller is designed to control the motion. The controller is implemented for a line following robot to analyze the controller robustness.
Material handling system involves short distance movement within the confines of a building or between a building and a transportation vehicle. It utilizes a wide range of manual, semi-automated, and automated equipment and included consideration of the protection, storage, and control of material throughout their manufacturing, warehousing, distribution, consumption, and disposal (Aized, 2010). In a simple way to explanation, material handling system is a system concerned about loading, moving and unloading of materials. Some example of the material handling system is AutomatedGuidedVehicle (AGV) system and conveyor system.
AGV, the AGV will triangulate its current position by comparing the map of reflectors layout store in memory with environment information . In contrast, inertial guided AGV uses various sensors e.g. accelerometer, gyroscope, and magnetometer to determine its position during navigation . Even though the free range AGV is more flexible and does not require a huge initial installation cost in terms of installing the required fixed path, the navigation system of a free range AGV navigation system is much complex and expensive compared to the fixed path AGV. This is because the navigation system involves various expensive sensors and complex system to aggregate and analyse the signals from various sensors in order to navigate the AGV as required. Thus, an alternative of a free range AGV is worthy to be designed and investigated so that a compromise can be reached between complexity and flexibility.
Paint strips- When paint strips are used to define pathway, the vehicle uses an optical sensor system capable of tracking the paint. The strips can be taped, sprayed or painted. An on-board sensor detects the reflected light in the strip and controls the steering mechanism to follow it. It is used when surrounding electrical noise renders the wire guided method unreliable. Over long distances the propriety of the path followed by the AGV is ensured by beacons installed at strategic locations throughout the plant.
8 This paper provides a holistic approach to align automatedvehicle fleet (AGVs) in an industrial environment. Objective Digani, Sabattini, Secchi, & Fantuzzi are aiming to solve coordination problems in a holistic way. They propose an ensemble method where the layers manipulate the architecture and a set of automated guidelines for the definition of the road plan is combined. The structure consists of layers. Lower level represents the road itself. Excessive titles illustrate topology relationships between specific areas around. The objective of the proposed approach in managing the overall design setup and managing techniques automatically, reducing time for setup and installation. Strength also improved by considering the alignment technique for the AGV fleet no longer based on manual ad-hoc guidelines. Simulations are achieved by comparing the proposed methods for the contemporary industry. In the future, industrial factors, such as warehouses that manage the system, may be included as a way of achieving actual and overall industry capabilities. (Digani, Sabattini, Secchi, & Fantuzzi, 2015)
AGV refers to the intelligent equipment that can deliver goods to the designated location through the guidance device. With the rapid development of modern logistics industry, AGV has been called an important intelligent device of logistics system, and its application scope will be more and more extensive . Navigation is one of the core technologies of AGV, so it is also a symbol of the development of AGV technology. At present, the common AGV navigation methods have magnetic navigation , electromagnetic navigation, laser navigation, photoelectric guided, and so on. Magnetic navigation (Figure 1 (a)) and electromagnetic navigation (Figure 1 (b)) have lower flexibility because they need to lay the paths in the workplace beforehand. Laser navigation (Figure 1 (c)) needs to lay the baffle-board in advance, and the maintenance cost and workload are higher when change the workplace. Although photoelectric navigation can change the path, it requires a larger pre-set workload. In order to avoid the shortcomings of the above navigation methods, SLAM navigation based on laser sensors or vision sensors applied to AGV. Using the SLAM technology and the information obtained from the environment by sensors, we can construct a map of the unknown environment and locate the AGV, thus laying a foundation for the next step of AGV path planning . Because of the high flexibility of laser SLAM navigation technology, it is more mature in technology and theory than visual SLAM navigation. It is the development trend of AGV navigation technology. With the development of society, the intelligent logistics industry has a higher requirement. It is an inevitable trend to develop an AGV with high path flexibility and autonomous navigation in unknown environment.
Modern AGV system differs from the classic one as described for instance in the book of Junemann and Schmidt (2000) and Tompkins et al. (2003) in several respect. Rather than using fixed paths, many modern AGV are free ranging, which means the path of the vehicle are software programmed and can be change relatively easy when new stations or even flows are added. Modern technology also allows the vehicle to make decisions on its own compare to the past when control was perform by central controllers. This leads to adaptive, self-learning system of the AGV (Tuan Le-Anh and De Koster). In this section, AGVs classification according to the journal by Peter et al will describe.