Control of an industrial robot by vision system (original) (raw)
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Industrial Robot Vision System
IFAC Proceedings Volumes, 1983
The paper describes the results of developing and testing of a simple, cheap and fas~ reacti~ system of t~chno logical vision, necessary for solv~ng appl~ed tasks of v~s ual ~data processing, cop~ected with the utilizing of industrial /.-robots for manufacturing processes.
Integration of a 2D Vision System into a Control of an Industrial Robot
KMUTNB International Journal of Applied Science and Technology, 2014
Industrial robots play an important role in today's production. They are mostly tied to the production process by teached orders. To make industrial robots more flexible and more interesting for the industry, they are often additionally equipped with a vision system. Thus, they are not bound by their fixed program and able to adapt their path to each object individually. This paper explains the cooperation of a six-axis industrial robot and a 2D-Vision System. Here, the precision of the robot and the accuracy of the vision system are combined. The vision system In Sight Micro 1100 acquires quality and localization tasks. It locates the object, inspects it and judged its quality. These results are finally send to the robot controller of KUKA KRC 2 Edition 2005 in the form of coordinates and are ultimately put into action by the industrial robot KUKA KR 16-2. With the software In Sight Explorer 4.8.0 a visual order can be created and adapted to the existing conditions. It offers a variety of preset localizing and qualitative tools. Alternatively, there is the possibility to created special tools in a spreadsheet program. The paper describes the interface between vision system and robot. Finally, an inspection station for work piece quality control is created from the derived results.
Industrial Vision System for Object Detection
TECHNICAL SCIENCES AND TECHNOLOGIES, 2021
The article aimed to experimentally verify and detect the coordinates of a given reference object, which will be manipulated by an industrial robotic arm, type SCARA. It was necessary to identify and locate individual objects at the automated workplace using the OMRON F150-3 visual inspection system during the process. Therefore, the ultimate goal of the assigned task is to reliably grasp the detected reference object and move on to the next technological operation. In the future, it would be appropriate to ensure reliable lighting conditions to guarantee the continuity of the automated process. The article is a publication of scientific and methodical character.
Computer Vision in Industrial Automation and Mobile Robots
Materials forming, machining and tribology, 2018
Computer vision is presently a very relevant and important tool in both industrial manufacturing and mobile robots. As human vision is the most relevant sense to feed the brain with environmental information for decision making, computer vision is nowadays becoming the main artificial sensor in the domains of industrial quality assurance and trajectory control of mobile robots. Keywords Computer vision Á Industrial automation Á Mobile robots 1 Computer Vision in Industrial Automation 1.1 Artificial Vision in Automation Distributed systems began to be used in the telecommunication sector, pushed by the generalized spread of computers and their need to be interconnected. This context originated the rise of several network topologies. Distributed strategies were rapidly extended to other domains, as they bring several advantages. The distributed strategy has very interesting characteristics as it shares several resources, allowing a more rational distribution among the users. This philosophy presents enormous economical benefits in comparison to traditional centralized systems, where the same resources have to be multiplied. Decentralized management of systems is nowadays an important development tool. This strategy reaches different fields, from agriculture to industry, building automation, etc. [1, 2]. Artificial vision and image processing have already an important role in several technical domains as: (i) pattern recognition, with multiple applications in industrial
IRJET- Six axes robotic arm aiding machine vision
IRJET, 2020
In the future, operations in the factories will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles and to complete the information provided by other sensors to improve their positioning accuracy. This paper discusses the development of a robotic arm integrated with machine vision. This work analyzes accuracy, range and performance of robotic arm in the working environment of a factory which is considered as the constraint. The developed model included a conveyor system for delivering the products, laser sensor for detecting the products, barcode scanner module and an LCD screen displaying the details of the tray. The robotic arm, controlled by Arduino Mega 2560, was able to place the products at the various destinations accurately. The instructions to place the product was preprogrammed in the controller. The processing of the image scanned by the barcode scanner was done by Raspberry Pi 2.
There are myriad occasions where human inclusions in a situation can be replaced by a robot which does the same task with ease and in a shorter duration of time. Earlier efforts in the field of vision based robotics have been primarily manual and static in nature: process being monitored at a single fixed point. Recent evolutions have led us to gesture controlled robotics which may be fascinating but manual in nature. Systems are being developed which have on-board vision: Autonomous systems that can act on the same situations at different locations. To facilitate one possible feasible solution, we have implemented a system through which an area is scanned for a particular object by a surveillance robot. Through this method autonomous monitoring of hazardous elements is made possible with a wireless camera on-board and robot monitoring various locations in a given region. The command signals are generated from the scanning through Image Processing. These signals are then passed to the robot to navigate it to the required position pick the object and place it to the required position using an electro-pneumatic gripping mechanism.
Application of the computer vision technology to control of robot manipulators
The paper displays main results of the Ukrainian-French research project which studies interaction of a robot with its environment. Following applications of computer vision technology for various issues of robotics are shown: robot follows movements of a person, detects speed of the moving object, studies its environment, learns particular manipulations, gets trained to focus attention on the objects of interest and synchronizes its motion with an interacting agent. The article proposes approaches to development of the software interface for the robot control based on the computer vision technology.
Intelligent control of DaNI robot based on robot vision and object recognition
In order to use the mobile robot platform for tracking particular moving objects it is necessary to develop intelligent top level control algorithm. In this paper a computational intelligence based object recognition algorithm in robot vision system is presented. The main goal of this research was to enable DaNI robot to recognize the particular Lego NXT robot among other differently shaped Lego NXT robots and objects, and to localize them with accuracy high enough to allow following the chosen one. The necessary robustness of the 2D object recognition is achieved by a novel robust robot vision systems that introduces the closed-loop control of image segmentation without the use of extensive previous knowledge and computational intelligence as an important part of the vision system. Reliable feature extraction is necessary to fully exploit intelligent classifiers, which are the core of the proposed 2D object recognition method. Two different types of classifiers were developed and then compared, the ANFIS neuro-fuzzy classifier and neural network classifier. The supervisory control algorithm was tested through experiments and the results are showing that this kind of approach in robot vision for object recognition provides good results. Besides that, future use of computational intelligence techniques in robotic vision object recognition system is also briefly discussed.
Control of an industrial desktop robot using computer vision and fuzzy rules
IEEE International Symposium on Industrial Electronics, 2005
Desktop robots are suitable for various production line systems in industrial applications like dispensing, soldering, screw tightening, pick'n place, welding or marking. Despite their capabilities to meet diverse requirements, they have to be programmed off-line using waypoints and path information. Misalignments in the workspace location during loading cause injuries in the workpiece and tool. Further, in modern flexible industrial production, machinery must adapt to changing product demands, both to the simultaneous production of different types of workpieces and to product styles with short life cycles.
Image Processing and Artificial Neural Network for Robot Application
Applied Mechanics and Materials, 2014
This paper presents a robot vision application, implemented in MATLAB working environment, developed for feature-based object recognition, object sorting and manipulation, based on shape classification and its pose calculus for proper positioning. The application described in this article, designed to detect, identify, classify and manipulate objects is based on previous robot vision applications that are presented in more detail in [1]. The idea underlying the mentioned applications is to determine the type, position and orientation of the work pieces (in those cases different types of bearings). Taking it further, in the presented application, objects that show shape with a gradual level of complexity are used. For this reason pattern recognition are discriminated by training a two layers neural network. The network is presented and also the input and output vectors.