scholarly journals Analysis of flexible supported industrial robot on terminal accuracy

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141879302
Author(s):  
Liping Wang ◽  
Lian Chen ◽  
Zhufeng Shao ◽  
Liwen Guan ◽  
Li Du
Keyword(s):  
Vibration Suppression ◽  
Industrial Robot ◽  
Reaction Force ◽  
Industrial Robots ◽  
High Quality ◽  
Flexible Support ◽  
Performance Requirements ◽  
Large Structures ◽  
Robot Systems ◽  
Motion Range

To meet comprehensive performance requirements of large workspace, lightweight, and low energy consumption, and flexible supported industrial robots emerge, which are usually composed of a six-degrees-of-rotational-freedom (6R) industrial robot and a flexible support. Flexible support greatly expands the motion range of the attached industrial robot. Flexible supported industrial robots have been adopted in surface coating of large structures such as aircrafts and rockets. However, the rigid–flexible coupling exists in these robot systems. When the industrial robot moves, the reaction force and torque of the robot disturb the flexible support and introduce vibration, which may result in the deterioration of the system’s terminal accuracy. This study focuses on both the robot body accuracy and system vibration suppression to improve the terminal accuracy of the flexible supported industrial robot. Firstly, based on kinematics analysis, accuracy of the industrial robot is investigated with the local conditioning index. Then, reaction force and torque ellipsoids are proposed with the deduced dynamic model to evaluate disturbances that the industrial robot applies to the flexible support. Considering these two aspects, the high-quality workspace of the flexible supported industrial robot is established. Numerical simulations show that reaction force and torque are effectively limited in the high-quality workspace, which greatly reduce the vibration energy and improve the terminal accuracy of the system.

Supporting Semantic Capture During Kinesthetic Teaching of Collaborative Industrial Robots

2018 ◽  
Vol 12 (01) ◽  
pp. 167-186 ◽  
Author(s):  
Maj Stenmark ◽  
Mathias Haage ◽  
Elin A. Topp ◽  
Jacek Malec
Keyword(s):  
Domain Knowledge ◽  
Semantic Information ◽  
Industrial Robot ◽  
Semantic Context ◽  
Industrial Robots ◽  
Teaching Mode ◽  
Language Support ◽  
Semantic Data ◽  
Robot Systems ◽  
System Knowledge

Industrial robot systems being deployed today do not contain domain knowledge to aid robot operators in setup and operational use. To gather such knowledge in a robotic context requires mechanisms for entering and capturing semantic data. Such mechanisms would allow a system to gradually build a working vocabulary while interacting with the environment and operators, valuable for the bootstrapping system knowledge and ensuring the data collection over time. This paper presents a prototype user interface that assists the kinesthetic teaching mode of a collaborative industrial robot, allowing for the capture of semantic information while working with the robot in day-to-day use. Two modalities, graphical point-and-click and natural language, support capture of semantic context and the building of a working vocabulary of the environment while modifying or creating robot programs. A semantic capture experiment illustrates the approach.

scholarly journals Analysis of Core Technology Problems and Countermeasures in the Robot Industry

2021 ◽  
Vol 5 (6) ◽  
pp. 19-24
Author(s):  
Xiguang Zhang
Keyword(s):  
Industrial Robot ◽  
Strong Dependence ◽  
Industrial Robots ◽  
Robot System ◽  
High Quality ◽  
Core Technology ◽  
Key Technologies ◽  
Core Components ◽  
Basic Technology ◽  
Technology Level

Since 2013, China has been the world’s largest market for industrial robots. Despite the gradual maturity of the industrial robot system, the lagging R&D and backward technology level of industrial robots have led to a strong dependence on the import of core components and key technologies, which to a certain extent has restricted the development and improvement of industrial robots. At present, the “neck problem” in the field of industrial robots in China is not only in the reducer, controller, and servo but also in the basic processing equipment, basic technology, and basic materials. In this paper, we propose measures to improve the “neck problem” of industrial robots to promote the high-quality development of industrial robots in China.

scholarly journals Vibration Suppression for Improving the Estimation of Kinematic Parameters on Industrial Robots

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
David Alejandro Elvira-Ortiz ◽  
Rene de Jesus Romero-Troncoso ◽  
Arturo Yosimar Jaen-Cuellar ◽  
Luis Morales-Velazquez ◽  
Roque Alfredo Osornio-Rios
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Vibration Suppression ◽  
Industrial Robot ◽  
Angular Position ◽  
Industrial Robots ◽  
Industrial System ◽  
Kinematic Parameters ◽  
Fusion Technique ◽  
Motion Profile

Vibration is a phenomenon that is present on every industrial system such as CNC machines and industrial robots. Moreover, sensors used to estimate angular position of a joint in an industrial robot are severely affected by vibrations and lead to wrong estimations. This paper proposes a methodology for improving the estimation of kinematic parameters on industrial robots through a proper suppression of the vibration components present on signals acquired from two primary sensors: accelerometer and gyroscope. A Kalman filter is responsible for the filtering of spurious vibration. Additionally, a sensor fusion technique is used to merge information from both sensors and improve the results obtained using each sensor separately. The methodology is implemented in a proprietary hardware signal processor and tested in an ABB IRB 140 industrial robot, first by analyzing the motion profile of only one joint and then by estimating the path tracking of two welding tasks: one rectangular and another one circular. Results from this work prove that the sensor fusion technique accompanied by proper suppression of vibrations delivers better estimation than other proposed techniques.

SAFETY AND RISK ASSESSMENT AT AUTOMATED WORKPLACE

2018 ◽  
pp. 78-84
Author(s):  
Marek Vagas
Keyword(s):  
Risk Assessment ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Added Value ◽  
Growing Up ◽  
Target Setting ◽  
Clear View ◽  
General Safety

Urgency of the research. Automated workplaces are growing up in present, especially with implementation of industrial robots with feasibility of various dispositions, where safety and risk assessment is considered as most important issues. Target setting. The protection of workers must be at the first place, therefore safety and risk assessment at automated workplaces is most important problematic, which had presented in this article Actual scientific researches and issues analysis. Actual research is much more focused at standard workplaces without industrial robots. So, missing of information from the field of automated workplaces in connection with various dispositions can be considered as added value of article. Uninvestigated parts of general matters defining. Despite to lot of general safety instructions in this area, still is missed clear view only at automated workplace with industrial robots. The research objective. The aim of article is to provide general instructions directly from the field of automated workplaces The statement of basic materials. For success realization of automated workplace is good to have a helping hand and orientation requirements needed for risk assessment at the workplace. Conclusions. The results published in this article increase the awareness and information of such automated workplaces, together with industrial robots. In addition, presented general steps and requirements helps persons for better realization of these types of workplaces, where major role takes an industrial robot. Our proposed solution can be considered as relevant base for risk assessment such workplaces with safety fences or light barriers.

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Chen Zhang ◽  
Zhuo Tang ◽  
Kenli Li ◽  
Jianzhong Yang ◽  
Li Yang
Keyword(s):  
Time Series ◽  
Control System ◽  
Force Control ◽  
Time Series Data ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Force Control System

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

scholarly journals Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 226
Author(s):  
Xuyang Zhao ◽  
Cisheng Wu ◽  
Duanyong Liu
Keyword(s):  
Life Cycle ◽  
Systems Engineering ◽  
Life Cycle Cost ◽  
Manufacturing Systems ◽  
Large Scale ◽  
Cost Allocation ◽  
Industrial Robot ◽  
Case Analysis ◽  
Industrial Robots ◽  
Analysis Model

Within the context of the large-scale application of industrial robots, methods of analyzing the life-cycle cost (LCC) of industrial robot production have shown considerable developments, but there remains a lack of methods that allow for the examination of robot substitution. Taking inspiration from the symmetry philosophy in manufacturing systems engineering, this article further establishes a comparative LCC analysis model to compare the LCC of the industrial robot production with traditional production at the same time. This model introduces intangible costs (covering idle loss, efficiency loss and defect loss) to supplement the actual costs and comprehensively uses various methods for cost allocation and variable estimation to conduct total cost and the cost efficiency analysis, together with hierarchical decomposition and dynamic comparison. To demonstrate the model, an investigation of a Chinese automobile manufacturer is provided to compare the LCC of welding robot production with that of manual welding production; methods of case analysis and simulation are combined, and a thorough comparison is done with related existing works to show the validity of this framework. In accordance with this study, a simple template is developed to support the decision-making analysis of the application and cost management of industrial robots. In addition, the case analysis and simulations can provide references for enterprises in emerging markets in relation to robot substitution.

scholarly journals Research of Calibration Method for Industrial Robot Based on Error Model of Position

2021 ◽  
Vol 11 (3) ◽  
pp. 1287
Author(s):  
Tianyan Chen ◽  
Jinsong Lin ◽  
Deyu Wu ◽  
Haibin Wu
Keyword(s):  
Coordinate System ◽  
Industrial Robot ◽  
Calibration Method ◽  
Error Model ◽  
Position Error ◽  
Industrial Robots ◽  
Positioning Error ◽  
Robot Position ◽  
General Measurement ◽  
Parameter Error

Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the "hidden" characteristics of the RBCS (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCS and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCS, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

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