Adaptive Industrial Robots Using Machine Vision

2018 ◽  
Author(s):  
Vladimir Kuts ◽  
Tauno Otto ◽  
Toivo Tähemaa ◽  
Khuldoon Bukhari ◽  
Tengiz Pataraia
Keyword(s):  
Machine Vision ◽  
Manufacturing Industry ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Engineering Industry ◽  
Test Environment ◽  
Operation System ◽  
Robotic Cells ◽  
Self Learning ◽  
Human Robot Collaboration

The use of industrial robots in modern manufacturing scenarios is a rising trend in the engineering industry. Currently, industrial robots are able to perform pre-programmed tasks very efficiently irrespective of time and complexity. However, often robots encounter unknown scenarios and to solve those, they need to cooperate with humans, leading to unnecessary downtime of the machine and the need for human intervention. The main aim of this study is to propose a method to develop adaptive industrial robots using Machine Learning (ML)/Machine Vision (MV) tools. The proposed method aims to reduce the effort of re-programming and enable self-learning in industrial robots. The elaborated online programming method can lead to fully automated industrial robotic cells in accordance with the human-robot collaboration standard and provide multiple usage options of this approach in the manufacturing industry. Machine Vision (MV) tools used for online programming allow industrial robots to make autonomous decisions during sorting or assembling operations based on the color and/or shape of the test object. The test setup consisted of an industrial robot cell, cameras and LIDAR connected to MATLAB through a Robot Operation System (ROS). The online programming tests and simulations were performed using Virtual/Augmented Reality (VR/AR) toolkits together with a Digital Twin (DT) concept, to test the industrial robot program on a digital object before executing it on the real object, thus creating a safe and secure test environment.

scholarly journals Interactive, Collaborative Robots: Challenges and Opportunities

2018 ◽  
Author(s):  
Danica Kragic ◽  
Joakim Gustafson ◽  
Hakan Karaoguz ◽  
Patric Jensfelt ◽  
Robert Krug
Keyword(s):  
Manufacturing Industry ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Service Robots ◽  
Natural Interaction ◽  
Current State ◽  
Challenges And Opportunities ◽  
Important Open Problem ◽  
In The Beginning ◽  
Office Environments

Robotic technology has transformed manufacturing industry ever since the first industrial robot was put in use in the beginning of the 60s. The challenge of developing flexible solutions where production lines can be quickly re-planned, adapted and structured for new or slightly changed products is still an important open problem. Industrial robots today are still largely preprogrammed for their tasks, not able to detect errors in their own performance or to robustly interact with a complex environment and a human worker. The challenges are even more serious when it comes to various types of service robots. Full robot autonomy, including natural interaction, learning from and with human, safe and flexible performance for challenging tasks in unstructured environments will remain out of reach for the foreseeable future. In the envisioned future factory setups, home and office environments, humans and robots will share the same workspace and perform different object manipulation tasks in a collaborative manner. We discuss some of the major challenges of developing such systems and provide examples of the current state of the art.

A comparative analysis of indicators and factors of robotization in Russia and Poland

2021 ◽  
Vol 17 (10) ◽  
pp. 1875-1902
Author(s):  
Aleksandr E. VARSHAVSKII ◽  
Viktoriya V. DUBININA
Keyword(s):  
Comparative Analysis ◽  
Manufacturing Industry ◽  
Industrial Robots ◽  
Engineering Industry ◽  
High Tech ◽  
Application Development ◽  
Electronic Engineering ◽  
Production Processes ◽  
Robotic Automation ◽  
The Relationship

Subject. This article analyzes the indicators of robot-based application and economic development, and explores the particularities of the robot-based application development in Russia and Poland. Objectives. The article aims to identify trends in the density of robot-based application in both countries, and the relationship between economic indicators and this density in order to use them to develop recommendations for accelerating the development of domestic robotics. Methods. For the study, we used the methods of analysis and modeling. Results. The article finds that in Poland, the density of robot-based application got increased at a significant rate due to the growth of production in the manufacturing industry. As the experience of Poland shows, the development of this industry and the export of high-tech products stimulate the use of industrial robots and the expansion of robotic automation of production. Conclusions. The introduction of robot-based application in production processes increases the competitiveness of the economy. This is especially true for Russia in the context of sanctions and the course for re-industrialization. In Russia, to increase the density of robot-based application, it is necessary to significantly increase the gross accumulation of fixed capital, the volume of industrial production and manufacturing industry, and primarily, the production of machinery and equipment, machine tool construction, and the electronic engineering industry.

Safety Strategy in the Smart Manufacturing System: A Human Robot Collaboration Case Study

2020 ◽  
Author(s):  
Xi Vincent Wang ◽  
Lihui Wang
Keyword(s):  
Manufacturing Industry ◽  
Industrial Robot ◽  
Working Environment ◽  
Smart Manufacturing ◽  
Future Research ◽  
Human Beings ◽  
Biometric Data ◽  
Action Strategy ◽  
Human Robot Collaboration ◽  
Safety Strategy

Abstract The next generation of the manufacturing industry calls for new approaches with smarter functionalities and better/safer working environment for human beings. The Human-Robot Collaboration (HRC) approach provides a feasible solution combing the flexibility and intelligence of a human, together with the accuracy and strength of an industrial robot. However, in the past years, despite the significant development of different HRC approaches, there is still a lack of clear safety strategy for an HRC system. Thus in this paper, the extensive taxonomy of the human-robot relations are first defined to provide a clear classification in different robotic scenarios. Then a comprehensive action strategy is developed toward different scenarios and human stakeholder’s roles. A dynamic HRC layout approach is also introduced based on the actual speed of human and robot and the distance between them. The feasibility of the proposed approaches in this paper is then evaluated via the implemenntation in an HRC-based assembly cell. The operator’s biometric data is also included in the HRC control loop. It is proven achievable to conduct personalised HRC safety strategy based on the human stakeholder’s role, physical conditions, speed and so forth. The future research outlooks and essential considerations are addressed at the end of the paper.

scholarly journals The Design and Evaluation of an Ergonomic Contactless Gesture Control System for Industrial Robots

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Gilbert Tang ◽  
Phil Webb
Keyword(s):  
Factor Analysis ◽  
Control System ◽  
Human Factor ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Effective Control ◽  
Analysis Tool ◽  
Leap Motion ◽  
Gesture Control ◽  
Human Robot Collaboration

In industrial human-robot collaboration, variability commonly exists in the operation environment and the components, which induces uncertainty and error that require frequent manual intervention for rectification. Conventional teach pendants can be physically demanding to use and require user training prior to operation. Thus, a more effective control interface is required. In this paper, the design and evaluation of a contactless gesture control system using Leap Motion is described. The design process involves the use of RULA human factor analysis tool. Separately, an exploratory usability test was conducted to compare three usability aspects between the developed gesture control system and an off-the-shelf conventional touchscreen teach pendant. This paper focuses on the user-centred design methodology of the gesture control system. The novelties of this research are the use of human factor analysis tools in the human-centred development process, as well as the gesture control design that enable users to control industrial robot’s motion by its joints and tool centre point position. The system has potential to use as an input device for industrial robot control in a human-robot collaboration scene. The developed gesture control system was targeting applications in system recovery and error correction in flexible manufacturing environment shared between humans and robots. The system allows operators to control an industrial robot without the requirement of significant training.

scholarly journals Development Status and Trend of Industrial Robot in China

2021 ◽  
Vol 5 (2) ◽  
pp. p49
Author(s):  
Gao Hang
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Industrial Robot ◽  
Development Trend ◽  
Industrial Robots ◽  
Continuous Development ◽  
Industrial Robotics ◽  
Development Status ◽  
The World ◽  
The Times

With the continuous development of the times, technology is flourishing and the world is entering the era of artificial intelligence, industrial robots will replace humans as the backbone of the manufacturing industry. The continuous development and application of industrial robotics is conducive to building a new advantage for China’s manufacturing, promoting the transformation and upgrading of China’s industry, accelerating the construction of China’s manufacturing power, and making an important contribution to China’s economic development. Based on this, the article describes the current situation of industrial robots in China, the challenges faced, and the future development trend.

Research on Kinematics Simulation and Analysis of 6 Revolute Joint Serial Industrial Robot

2011 ◽  
Vol 308-310 ◽  
pp. 2074-2077
Author(s):  
Ke Li ◽  
Duan Neng Li ◽  
Ling Lin Kong
Keyword(s):  
Manufacturing Industry ◽  
Industrial Robot ◽  
Strong Support ◽  
Industrial Robots ◽  
Mathematical Software ◽  
Kinematics Analysis ◽  
Revolute Joint ◽  
Automated Production ◽  
Kinematics Simulation

Industrial robots are getting more and more applications in modern automated production, it’s a strong support to the development of manufacturing industry. Nowadays many domestic enterprises begin to do the robot R&D. We use mathematical software Matlab to make kinematics simulation of IRB1410 robot and succeed in verifying the justifiability of kinematics analysis.

Statistical Learning Algorithms to Compensate Slow Visual Feedback for Industrial Robots

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Cong Wang ◽  
Chung-Yen Lin ◽  
Masayoshi Tomizuka
Keyword(s):  
Real Time ◽  
Statistical Learning ◽  
Visual Feedback ◽  
Manufacturing Industry ◽  
Industrial Robot ◽  
Sampling Rate ◽  
Robot Vision ◽  
Industrial Applications ◽  
Industrial Robots ◽  
Imaging Geometry

Vision guided robots have become an important element in the manufacturing industry. In most current industrial applications, vision guided robots are controlled by a look-then-move method. This method cannot support many new emerging demands which require real-time vision guidance. Challenge comes from the speed of visual feedback. Due to cost limit, industrial robot vision systems are subject to considerable latency and limited sampling rate. This paper proposes new algorithms to address this challenge by compensating the latency and slow sampling of visual feedback so that real-time vision guided robot control can be realized with satisfactory performance. Statistical learning methods are developed to model the pattern of target's motion adaptively. The learned model is used to recover visual measurement from latency and slow sampling. The imaging geometry of the camera and all-dimensional motion of the target are fully considered. Tests are conducted to provide evaluation from different aspects.

scholarly journals Do Speed and Proximity Affect Human-Robot Collaboration with an Industrial Robot Arm?

2022 ◽  
Author(s):  
Matthew Story ◽  
Phil Webb ◽  
Sarah R. Fletcher ◽  
Gilbert Tang ◽  
Cyril Jaksic ◽  
...  
Keyword(s):  
Significant Interaction ◽  
Industrial Robot ◽  
Collaborative Work ◽  
Industrial Robots ◽  
Robot Arm ◽  
High Productivity ◽  
The Impact ◽  
Further Development ◽  
Current Guidelines ◽  
Human Robot Collaboration

AbstractCurrent guidelines for Human-Robot Collaboration (HRC) allow a person to be within the working area of an industrial robot arm whilst maintaining their physical safety. However, research into increasing automation and social robotics have shown that attributes in the robot, such as speed and proximity setting, can influence a person’s workload and trust. Despite this, studies into how an industrial robot arm’s attributes affect a person during HRC are limited and require further development. Therefore, a study was proposed to assess the impact of robot’s speed and proximity setting on a person’s workload and trust during an HRC task. Eighty-three participants from Cranfield University and the ASK Centre, BAE Systems Samlesbury, completed a task in collaboration with a UR5 industrial robot arm running at different speeds and proximity settings, workload and trust were measured after each run. Workload was found to be positively related to speed but not significantly related to proximity setting. Significant interaction was not found for trust with speed or proximity setting. This study showed that even when operating within current safety guidelines, an industrial robot can affect a person’s workload. The lack of significant interaction with trust was attributed to the robot’s relatively small size and high success rate, and therefore may have an influence in larger industrial robots. As workload and trust can have a significant impact on a person’s performance and satisfaction, it is key to understand this relationship early in the development and design of collaborative work cells to ensure safe and high productivity.

scholarly journals Industrial Robot Technology and Its Typical Application Analysis

2018 ◽  
Vol 2 (3) ◽  
Author(s):  
Jiangong Chen
Keyword(s):  
Manufacturing Industry ◽  
Manufacturing System ◽  
Manufacturing Sector ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Typical Application ◽  
New Development ◽  
Application Analysis ◽  
Robot Technology ◽  
Near Future

With the rising of the manufacturing industry in our neighboring countries, China no longer has advantages in manufacturing sector. New development situation and tasks make it extremely urgent to establish a new manufacturing system featuring standardization, modularization, network and intelligence. Therefore, research on industrial robot technology is of great practical significance. It is believed that in the near future, industrial robots will become an important driving force for the transformation and upgrade of China’s manufacturing industry.

Geometric Positioning and Color Recognition of Greenhouse Electric Work Robot Based on Visual Processing

2020 ◽  
pp. 2159005
Author(s):  
Zhifu Xu ◽  
Xiaoyan Shi ◽  
Hongbao Ye ◽  
Shan Hua
Keyword(s):  
Machine Vision ◽  
Industrial Production ◽  
Visual Processing ◽  
Production Efficiency ◽  
Vision System ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Robot Kinematics ◽  
Color Recognition ◽  
Time Motion

With the continuous development of science and technology, industrial production technology is also constantly developing, and production efficiency is also constantly improving. Greenhouse electric working robots are industrial production tools with automatic control technology as the core, which affects the quality of industrial products and thus affects the profitability of the factory. According to the set programming work, the greenhouse electric working robot can realize the reproduction production and reduce the workload of the workers. In today’s era, the industrial production steps are more complicated, the production process is more flexible, and the robot’s unchanging posture and movement cannot meet the needs of modern industry, which restricts the development of the factory. In order to better complete the work of industrial robots, it is necessary to study the geometric positioning and color recognition of industrial robots based on machine vision to improve the working efficiency of industrial robots. This paper established an active positioning machine vision system for precise positioning of robot parts greenhouse electric working stations. The matching method using image processing and feature recognition area based on the shape of the binding phase combines the threshold shape criterion to identify object features. The experiments prove that the method can quickly and accurately obtain the object boundaries and centroid calculations and identification data, the robot kinematics combined with real-time motion control of the robot in order to eliminate this error, meet the requirements of the industrial robot self-aligned.

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