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.
A horizontally positioned, suitable shaped reflector, reflecting an omnidirectionalimage of the AGV’s immediate environment onto the camera’s sensor, is contemplated. The reflector size, shape and placement – relative to the position of the camera and its lens – will play an important role in the quality and nature of images acquired. These characteristics will have to be optimised mathematically and the satisfactory functioning of the comprehensive optical system verified experimentally [2, pp. 157-160][4, p. 53]. Visual identification of any physical object entails the identification of a substantial correlation between a perceived entity and its physical equivalent. Hence, the first objective of the optical configuration would be to process the acquired image such as to obtain an accurate image of the camera’s immediate surroundings – enabling the substantive identification of known physical phenomena. This would necessitate image correction by means of significant signal processing algorithms. An example of this in ordinary photography is with the use of fish-eye lenses where the distorted image is mentally transformed by the human viewer into its real format. This technique will be modelled in the project using M ATLAB ® before being implemented on a practical model.
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.
An automatedguidedvehicle is a programmable mobile vehicle. The automatedguidedvehicle is used in industrial application to move material around a manufacturing facility. The AGV are capable of transportation task fully automated at low expanses. AGV have to make the system automatic by doing the decision on the path selection. This is done through different method frequency selected mode, path selected mode and vision based mode etc. The central processing system of AGV is issue the steering command and speed command. For the pre-defined manufacturing environment the line follower robot is good option for choice. A line follower robot is a robot which follows a pre-defined path controlled by a feedback mechanism. The path can be visible like a black line on a white surface (or vice versa) or it can be invisible like a magnetic field. Sensing a line and guiding the robot to stay on course, while constantly correcting. Some of the practical applications of a line follower are industrial applications were these robots can be used as automated equipment carriers in industries replacing traditional conveyer belts in automobile. Some recent development of line follower is seen in applications such floor cleaning, guidance in public places, library assistance (thirumurgan 2010), entertainment (coalk 2009), education (Makrodimitris 2011) etc. Most commonly used technology in line following robot are done by using microcontrollers and without using microcontroller.
This method holds many advantages, but also has some disadvantages. Once laid out and installed, the wire is set in place and cannot be moved – meaning changes cannot be made to the factory floor layout. Other options for navigation include guide tape, laser target navigation, natural features navigation and geo-guidance. All use existing features to navigate their environment. Vision-Guided AGVs have the advantage that it can be installed with no modifications to the environment or infrastructure, making them a better scalable option for a company. They operate by using cameras or sensors to record features along the route, allowing the AGV to replay the path by using the recorded features to navigate. The AGV will be able to adapt to a change in environment and continue to work. To help in localisation, AGVs could use Evidence Grid technology, an application of probabilistic volumetric sensing, which was invented and initially developed by Dr Moravec at Carnegie Mellon University .
Autonomous robots are independent of any controller and can act on their own. The robot is programmed to respond in a particular way to an outside stimulus. The bump- and -go robot is a good example. This robot uses bumper sensors to detect obstacle. When the robot is turned on, it moves in a straight direction and when it hits an obstacle, the crash triggers its bumper sensor. The robot gives a programming instruction that asks the robot to back up, turn to the right direction and move forward. This is its response to every bump. In this way, the robot can change direction every time, it encounters an obstacle. A more elaborate version of the same idea is used by more advanced robots .Robotics create new sensor systems and algorithms to make robots more perceptive and smarter. Today, robots are able to effectively navigate a variety of environments. Obstacle avoidance can be implemented as a reactive control law whereas path planning involves the pre-computation of an obstacle- free path which a controller will then guide a robot=along. Some mobile robots also use various ultrasound sensors to see obstacles or infrared. These sensors work in a similar fashion to animal echolocation. The robot sends out a beam of infrared light or a sound signal. It then detects the reflection of the signal. The robot locates this distance to the obstacles depending on how long it takes the signal to bounce back. Some advanced robots also use stereo vision. Two cameras provide robots with depth perception. Image recognition software then gives them the ability to locate, classify various objects. Robots also use smell and sound sensors to gain knowledge about its surroundings
This project is focusing on the integration of Programmable Logic Controller (PLC) as the control system for the AGV, sensors for sensing and giving feedback data, servo motor for steering system and the Direct Current (DC) motor as the drive system. Sensing is an important part in AGV on how the robot will detect the route, the destination, any obstacle during the path and its own state such as the battery level and operating status. Control of AGV is about the functionality of the AGV during the run on how it will react to the surroundings, forward movement, backward movement, driving and steering. The motor selection is based on the weight of the load, torque and the speed of the AGV .
Vision based AGV utilises vision sensor and computer image processing for navigation. AGVs may share a common generic architecture where the vehicle is governed by it sensors to gather information from the surrounding and response to its surrounding by several actuators. Information processed from the vehicle’s sensors is called perception. The perception can be in the form of image processing to get usable information for path planning and control when the vehicle is using a camera as its sensor.
[Displaying confidence images, Nagy et al.]  Using motion Cameras we can capture the image of the license plates of cars that fail to obey speed limits or other traffic regulations and are thus they are employed to enforce law. The images acquired are usually contaminated with motion blur, so we can examine the uncertainties involved in the processing of such images. Supposing a moving object say a bus, were it is moving directly away from the camera, then the motion blur encountered would be in the vertical direction of the captured image. Such a blur can be practically simulated as spreading each pixel to the adjoining 19 pixels, and then adding a noise image, cropping such an image and resizing it to size say 36 x 36, which gives each column of pixels an independent reconstruction matrix problem of rank 36. The original and blurred images are shown in the top row of figure below. However the second row shows the reconstructed images, using the assumptions of nonnegativity of pixel values and using the bounds of [0, 1] respectively.
Omnidirectional camera is a camera that have a vision of 360° with surounding vision that can view a large of information in image processing. Meanwhile, the normal camera has a field of view from a few degrees to almost 180° view only. An ideal omnidirectional camera can capture light from all direction to its focal point like covering full sphere of lens. In practice, most of the omnidirectional cameras just cover almost approximately a semisphere with 360° of light captured.
𝑇, 𝑐, 𝑍 > 0 (3.16) Equations (3.1) to (3.5) are based on a typical Job Shop Scheduling (JSP) model (Pinedo, 2009), while an additional parameter 𝑡 𝑖 is used to consider necessary transportation time of a job from one machine to another for a pair of consecutive operations. When jobs finish their last operation, they are immediately removed from the machine, and AGVs do not handle them back to L/U, hence the makespan is defined as the finish time of the last operation on the shop floor in Equation (3.2). Binary variable x represents the routes of AGVs, which indicates the sequential relationship of each operation. Equations (3.6) and (3.7) regulate the strict one-by-one following relationship between each pair of operations. Equation (3.8) defines that the number of AGV routes is limited by AGV fleet size. Equation (3.9) ensures that for each AGV, there must be a starting trip as well as an ending trip. Equation (3.10) means the operation must begin after the job arrival to the machine. Note that Equation (3.10) is not an equation because it is possible that in an optimal schedule, an early-arriving job waits at the machine until another job whose operation arrives later to start first. The operation sequence of one job is ensured in Equation (3.11). Equations (3.12) and (3.13) are linearized conditional constraints to replace the nonlinear constraints by Bilge and Ulusoy (1995), which indicate the impact of previous trips on the next trip of each AGV. Equation (3.14) is used to start timing when a vehicle leaves the L/U with the first job it conveys, and such a constraint means a default initial condition that AGVs are at the L/U until they leave for the first job handling task. Sometimes the trip of vehicles between L/U and machines is not considered (Khayat, Langevin, & Riopel, 2006); however, we decide to include these trips in the optimization thus reflecting the production reality (Y. J. Xiao, Zheng, & Jia, 2014).
AutomatedGuided Vehicles (AGV) with magnetic navigation and electromagnetic navigation have lower path flexibility due to the need to lay the path ahead of time. The Simultaneous Localization and Mapping (SLAM) is mature in technology theory, so that this technique is applied to the AGV navigation. The laser sensor is used to acquire the information in the location environment for immediate location and mapping, so that carry out the path planning and complete the AGV autonomous navigation. Extended Kalman Filter (EKF) is the most common solution for laser SLAM. Firstly, this paper analyzes the research results of EKF- SLAM at this stage and its limitations. Secondly, it discusses the application of EKF-SLAM in AGV and the development direction in the next stage. By comparative analysis shows that laser EKF-SLAM can meet the requirements of AGV navigation. The development of EKF-SLAM algorithm in positioning, mapping accuracy and the ability to solve interferences in dynamic environment has an important reference for the reliability of AGV navigation. 1
In addition to choosing the right vehicle for the right job, there are also choices to be made when it comes to AGV guidance, also referred to as navigation systems. Navigation systems can be closed path or open path. Bellow it a list part of AGV guidance. Figure 2.3 shows the picture example of navigation system.
The research presented in this paper approaches the issue of navigation using an automatedguidedvehicle (AGV) in industrial environments. The work describes the navigation system of a flexible AGV intended for operation in partially structured warehouses and with frequent changes in the floor plant layout. This is achieved by incorporating a high degree of on-board autonomy and by decreasing the amount of manual work required by the operator when establishing the a priori knowledge of the environment. The AGV's autonomy consists of the set of automatic tasks, such as planner, perception, path planning and path tracking, that the industrial vehicle must perform to accomplish the task required by the operator. The integration of these techniques has been tested in a real AGV working on an industrial warehouse environment (Martínez-Barberá & Herrero-Pérez, 2010).
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.
The automatedguidedvehicle is highlighted as a flexible transport vehicle for existing lines in variety industrial fields. An automatedguidedvehicle (AGV) is a vehicle that is equipped with automatic guidance system either electromagnetic or optically. This vehicle is capable of transportation of material, sorting and material handling work also handling dangerous materials. An AGV consist of one or more computer controller wheel based load carriers that run on the plant floor without the need for an onboard operator or driver. As it names was automated, this vehicle is programmed to handle operation on its own. (Junemann and Schmidt, 2000)
The scope of this project is to create a vehicle model that capability to move freely by user and create a 2D environment mapping. This outcome is to be done using an RPlidar laser scanner to allow visualize a map to become aware of its position in a room. The Robot Operating System (ROS) will be acting just like the brain for the model that controls all operations of the system from a laptop or PC.
Abstract: The emerging concept intelligent space (IS) offers the use of mobile autonomous devices like vehicles or robots in a very broad area without necessity for these devices to own all necessary sensors. From this reason also new navigation methods are developing, which aim to offer maybe not accurate but first of all cheap and reliable solutions for a wide variety of devices. This concept deals with the examination of possibility to interconnect RFID tags with sensors. The signals produced by these two technologies are often affected by uncertainty and incompleteness we use fuzzy logic for their processing as well as control of the entire navigation process. For this purpose a special type of a fuzzy cognitive map was proposed. The concept describes real navigation experiments with a simple vehicle and evaluates them by selected criteria. From our results and their explanations and conclusions for potential future research are sketched.
Vehicles are the most important elements of an AGVs as they perform the actual transportation tasks. All vehicles are design individually according to their specific working tasks, working condition and working environment (Schulze et at., 2008). Vehicle can be classified as toward AGV, unit load AGV, pallet truck AGV and fork truck AGV (Wu et at., 2012), (Vosniakos and Mamalis, 1990).
The chip disposal AGV (AutomatedGuidedVehicle) consists of various components to ensure functional propriety and to fulfill the objective of operation. The mechanical structure includes Reeler, Conveyor, Chip breakers, Cabin, Vacuum extraction of chips and a battery to supply power to various elements. The design is task-oriented with provision for optional aesthetic features. Various standard parts are used to the extent possible to fabricate the peculiar anatomy of the AGV.