AGV can be defined as an unmanned, autonomous vehicle that is a subset of mobile robots. The AGV may have on-board computer to store path planning and motion control system. A traditional AGV usually rely on either wired technology, guided tape, laser technology or inertial guidance systems that make use of the gyroscopes and wheel odometry for their path planning system (Kelly et al., 2007). These technologies have made the AGVs dependent on the infrastructure in order to navigate around the facilities where it was deployed. The dependency on the infrastructure demands every single AGV to be in operational state to avoid any disruption to the whole system. This system is, more or less, similar to a conveyor belt system.
(Nasser Houshangi, 2009) present a vision-based autonomous lane following system for a mobile robot. Vision is an essential information source for the autonomous ground vehicle (AGV) navigation applications. The primary task of vision is to provide a description of the world rich enough to facilitate such behavior as road-following, obstacle avoidance, landmark recognition, and cross-country navigation. The current motivation is to improve AGVs navigation techniques by expanding their autonomy, capabilities and their usefulness.
Spampinato, G. et al. (2009) propose a stereo vision based localization and mapping of automated vehicles using natural landmark. In industrial environment or industry the lightening placed on the ceiling act as natural landmarks. The lamp already exists in the working area. There are two phases for localizing and mapping of the robot. First phase the algorithm optimizing for estimating the external parameter of two camera system. The incoming information came from Stephen and Harris feature detection algorithm are combined with the odometry information. Second phase is calibrating the camera parameters. In second phase algorithm, the Extended Kalman Filter (EKF) based probabilistic method has been implemented for simultaneously estimating the camera parameters and the position of robot landmarks in environment. The identification of first landmark related to initial camera view and used by SLAM algorithm as reference frame for robot global localization and landmark positioning is the environment map (Spampinato, G. et al., 2009).
Wood ants that are trained in a laboratory arena over a short route from a start point to a feeder will aim directly at the feeder after being released at a novel start point (Durier et al., 2004) (Fig.·1). In this case, the complexity of the environment with multiple landmarks and room cues makes it difficult to work out how the novel routes are guided. A simpler example to analyse is one with ants trained to a route along which they were guided by just a single vertical edge (Harris et al., 2007). The edge was constructed by papering a vertical wall with a brightness gradient that faded across the wall from black at one end to the same white as the background at the other (Fig.·2). To ensure that this gradient landmark was the major source of visual information, the landmark was rotated about a fixed start position between training trials. The ants learnt to run 80·cm directly towards a drop of sucrose that was placed at the base of the gradient landmark, inset 10·cm from its edge. If the ants were displaced to another starting point relative to the gradient
Segmentation is a vast topic but is one of the most important steps in analysing an image [6, pp. 175-327]. In this research edge- and region-based segmentation is taking priority. Thresholding plays a big role in segmentation determining borders, edges and lines. Gray- level thresholding is one of the simplest segmentation processes. The disadvantage is that the threshold must be set to a predetermined level and lighting plays a big role in altering this threshold value. Optimal- and multi-spectral thresholding are only but a few of the methods used in the segmentation process. Border tracing and region operations of an object also form part of segmentation thus leading to blob analysis, a binary representation of the object. Lu et al. proved in their research on detecting human heads and hands analysing movement gestures that, using colour in addition to thresholding for blob analysis was a successful approach [20, pp. 20c-30c]. Thus using colour in blob analysis could also be applied to other object detection applications. The main goal of segmentation would be to analyse an image by dividing the image into sections that have a strong correlation with objects depicted by these sections of the image.
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.
and development of automatedguidedvehicle (AGV) to replace the classic sprinkler systems. This is based on explanation given in the previous sections about AGV in agriculture and the disadvantages of the SICSMS. The propose system has the capability of taking out the sprinkler from its locations after every sprinkling period and move it to another position for continuous sprinkling, without loss of time. The designed system comprises of two parts, the AGV and the moving path (rail and sensor). Fig. 3 shows shot screens of the proposed AGV.
Such modification is not easy in ordinal fuzzy con- troller with complicated fuzzy rule base , because we have to take care of covering the entire input universe of discourse by the fuzzy sets, i.e., have to know all the de- tails of the controller. The FLC constructed by FRI (Fuzzy Rule Interpolation) method deals with the complex prob- lem by using small number of significant rules. In this technique it is not necessary to cover the entire input uni- verse of discourse [3–5]. Based on this principle, sparse fuzzy rule base can be created. Therefore we can mod- ify the significant FLCs depending on the situations with- out considering the full fuzzy rule base construction. The original forward movement AGV controller using FRI method is comprehensible and is easy to add new func- tion modules.
Material handling does not add value to the product but only cost. Thus the objective of material handling is the efficient movement of goods for the on-time delivery of correct parts in exact quantities to desired locations in order to minimize associated handling costs. It is not uncommon to have parts/subassemblies moving around a plant several kilometers prior to their shipment. Manufacturing plants must therefore eliminate all unnecessary part movements, as well as in process inventories, for just-in-time (JIT) production. Material handling equipment can be classified according to the movement mode: above-floor transportation (e.g., belt conveyors, trucks, etc.), on-floor transportation (e.g., chain conveyors), and overhead transportation (e.g., cranes). In the following sections, we will review industrial trucks (including automatedguided vehicles), conveyors, and industrial robots as the primary mechanized/automated material handling equipment. We will also briefly review the automated storage and retrieval of goods in high density warehouses, as well as the important issue of automatic part identification (including bar codes). The chapter will be concluded with a discussion on automobile assembly.
From the industrial training experienced, author has been involved in a kit delivery project for chassis A and B assembly line at Isuzu HICOM Malaysia Sdn. Bhd. The project is mainly involved in constructing a guide tape made for two automatedguidedvehicle (AGV) which the company bought from Japan. These two AGVs were modified at Engineering Workshop so that it becomes a towing AGV. The main task of the AGV is to deliver a kit dolly rack that is filled with assembly components for HICOM Perkasa truck and ISUZU D-MAX pickup. However, there are a few problems and shortage encounter by both AGV such as being hitting by a transverser, can’t move backward, do not move automatically after an operator from the assembly line pick the rack, non path planning programming, non obstacle avoidance programming, non sequence programming and missing guide tape on the floor. All of these problems sometimes delay the delivery routine. Operators have to replace the delivery task if any of the problems occurred to the AGV. Since the company reduces the workman power, the used of AGV is really necessary and reliable for delivery task. AGV is a costing mobile robot where a basic AGV can cost for almost RM5000 for industrial applications. Thus to reduce the cost, the company is planning to build its own AGV from scratch (refer Appendix A for project cost).
24 Tsukiyama explored a simple navigation mechanism based on vision for free space detection and RFID tags as labels within a topological map of an indoor environment (Tsukiyama, 2003). Kulyukin et al (2006) have also studied navigation based on passive RFID tags. Their robotic guide was able to assist visually impaired people in way finding in indoor environments. Kleiner (2006) studied the use of RFID labels for the coordination of robot teams in exploration, during which the labels were autonomously deployed. Recently, Zhou proposed a vision-based indoor localization method in which they used modified active RFID tags as landmarks (Zhou et al., 2007). The tags were equipped with bright LEDs to be recognized. An additional laser diode allowed for the selective activation via a laser beam emitted by the robot. A prototype system, which lacked the ability of autonomously point a laser at visually recognized RFID labels, promised accurate localization. Note that the laser activation step requires line-of-sight, which is generally not the case for other RFID-based localization approaches.
Some characteristic numbers clarify the different requirements for operators in Europe, Japan and the USA. Comparing to Japanese and American AutomatedGuidedVehicle Systems, European systems have the largest average number of vehicles per system while Japanese systems have the smallest number. Most AGVS are operating in Japan, but the majority of these systems are to be considered as rather “simple“ from the technological point of view. This is also reflected in the installed navigation systems. In Europe laser navigation was realized in about 47 % of the systems in the year 2006 whereas in Japan the magnetic procedures predominate.
Camera system was installed on our FANUC M-1iA robot, which will serve the robot guidance and creation of programs supported by iR Vision system. This configuration enables the implemen- tation of handling tasks with variable components location on designed workplace. Hardware part of iR Vision system is made up of camera Sony XC- 56 (in Fig. 2, left - the black cylinder mounted in the middle of robot frame), the lens and connecting cable. The software tool is based on web server, which is running in personal computer connected to the robot control system with Ethernet cable.
The contribution of this study is that it analyses three different guide path configurations for an integrated automatedguidedvehicle system which is embedded in a flexible manufacturing system. These guide path configurations have been developed in such a way that in the first case, an AGV has been allowed to serve only same machine resources thus forming a dedicated relationship between machines and the AGVs. The second configuration explores a limited flexibility as AGVs can serve every machine and the assembly station relevant to a specific manufacturing cell. In the third case, the flexibility of the AGVS is increased and every AGV can visit any machine or assembly station throughout the manufacturing system. Thus the aim of the development of the AGVS configurations is to gradually enhance the flexibility in AGVS and examine the performance for each configuration so that the best configuration can be proposed. The details of these three AGV guide-path configurations are discussed in section 3. Two of the three configurations have mixed uni/bidirectional guide-path layouts. Moreover, this study also presents the application of advanced tools like CPN Tools and shows how these powerful tools can be used to analyze a manufacturing system.
The sensor circuit is a device which responds to some type of input from physical environment. In the line follower robot sensor circuit is responsible for detect the line segment or the path defined in the work floor. Robotics sensors are used in many implementations as object detection, path detection (line detection using color variation.), etc. also it can be easily integrated to microcontroller based controlling systems which are also widely used.
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).
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 .
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).
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.
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).