Large Industrial Robots Research Articles

Study on Singularity Passing Method for Large Industrial Robot Based on Proper Element Improvement of Inverse Matrix

Singularity has become a limitation to the expected diversification of industrial robot applications in production. Here, we propose a novel solution for the inverse kinematics of a 6-DOF manipulator that suppresses rapid joint rotation when passing through a singularity. The proposed method is based on the optimization of the inverse Jacobi cofactor matrix utilized in the inverse kinematics calculation. By optimizing only the elements related to the four and six joints of the cofactor matrix in the inverse matrix, we were able to suppress the divergence of the joint velocities and achieve smooth robot motion when the robot entered a singular posture at the wrist. The developed system application range and tuning gain that minimize the position and orientation error were also determined. Hence, the position and orientation errors were improved by approximately 76%, and rapid rotation of the joints was almost completely suppressed. The method did not require consideration of tools attached to the hand tip, and the hand tip position, orientation, and velocity were all maintained.

Positioning Error Calibration of Industrial Robots Based on Random Forest

Because most industrial robots are taught using the direct teaching and playback method, they are unsuitable for variable production systems. Alternatively, the offline teaching method has limited applications because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have been conducted to calibrate the position and posture. Positioning errors of robots can be divided into kinematic and non-kinematic errors. In some studies, kinematic errors are calibrated by kinematic models, and non-kinematic errors are calibrated by neural networks. However, the factor of the positioning errors has not been identified because the neural network is a black box. In another machine learning method, a random forest is constructed from decision trees, and its structure can be visualized. Therefore, we used a random forest method to construct a calibration model for the positioning errors and to identify the positioning error factors. The proposed calibration method is based on a simulation of many candidate points centered on the target point. A large industrial robot was used, and the 3D coordinates of the end-effector were obtained using a laser tracker. The model predicted the positioning error from end-effector coordinates, joint angles, and joint torques using the random forest method. As a result, the positioning error was predicted with a high accuracy. The random forest analysis showed that joint 2 was the primary factor of the X- and Z-axis errors. This suggests that the air cylinder used as an auxiliary to the servo motor of joint 2, which is unique to large industrial robots, is the error factor. With the proposed calibration, the positioning error norm was reduced at all points.

Predicting Positioning Error and Finding Features for Large Industrial Robots Based on Deep Learning

In this study, we evaluated the motion accuracy of a large industrial robot and its compensation method and constructed an off-line teaching operation based on three-dimensional computer aided design data. In this experiment, we used a laser tracker to measure the coordinates of the end effector of the robot. Simultaneously, the end-effector coordinates, each joint angle, the maximum current of the motors attached to each joint, and rotation speed of each joint were measured. This servo information was converted into image data as visible information. For each robot movement path, an image was created; the horizontal axis represented the movement time of the robot and the vertical axis represented the servo information. A convolutional neural network (CNN), a type of deep learning, was used to predict the positioning error with high accuracy. Subsequently, to identify the features of the positioning error, the image was divided into several analysis areas, one of which was filled with various colors and analyzed by the CNN. If the prediction accuracy of the CNN decreased, then the analysis area would be identified as a feature. Thus, the features of the Y-axis positioning error were observed for teaching each joint angle in the opposite direction just after the start of the motion, overshoot of the rotational joint current, and the change in the swivel joint current.

Safe Collaborative Assembly on a Continuously Moving Line with Large Industrial Robots

Robot safety standards defines Collaborative Operation as a state in which purposely designed robots work in direct cooperation with a human within a defined workspace. That is, an operator and an industrial robot complete assembly tasks at the collaborative workspace. A prerequisite to ensuring safety during all phases of operation is an understanding of the nature of hazards pertinent to collaborative systems. An automotive assembly station, where plastic panels are assembled on a continuously moving line, formed the basis for research operations meant to understand safety issues when a large industrial robot aids an operator in assembly tasks. This led to the development of a laboratory demonstrator whose design and functioning will be presented in this article. Additionally, the hazards identified during risk assessment along with measures to mitigate the associated risks will be presented in order to highlight the nature of hazards pertinent to collaborative systems.

Robotic Deep Rolling With Iterative Learning Motion and Force Control

Large industrial robots offer an attractive option for deep rolling in terms of cost and flexibility. These robots are typically designed for fast and precise motion, but may be commanded to perform force control by adjusting the position setpoint based on the measurements from a wrist-mounted force/torque sensor. Contact force during deep rolling may be as high as 2000 N. The force control performance is affected by robot dynamics, robot joint servo controllers, and motion-induced inertial force. In this letter, we compare three deep rolling force control strategies: position-based rolling with open-loop force control, impedance control, and gradient-based iterative learning control (ILC). Open loop force control is easy to implement but does not correct for any force deviation. Impedance control uses force feedback, but does not track well non-constant force profiles. The ILC augments the impedance control by updating the commanded motion and force profiles based on the motion and force error trajectories in the previous iteration. The update is based on the gradient of the motion and force trajectories with respect to the commanded motion and force. We show that this gradient may be generated experimentally without the need of an explicit model. This is possible because the mapping from the commanded joint motion to the actual joint motion is nearly identical for all joints in industrial robots. We have evaluated the approach on the physical testbed using an ABB robot and demonstrated the convergence of the ILC scheme. The final ILC tracking performance of a trapezoidal force profile improves by over 70% in terms of the RMS error compared with the impedance controller.

Recent Developments Regarding Painting Robots for Research in Automatic Painting, Artificial Creativity, and Machine Learning

E-David (Electronic Drawing Apparatus for Vivid Image Display) is a system for controlling a variety of painting machines in order to create robotic paintings. This article summarizes the hardware set-up used for painting, along with recent developments, lessons learned from past painting machines, as well as plans for new approaches. We want to apply e-David as a platform for research towards improving automatic painting and to explore machine creativity. We present different painting machines, from small low-cost plotters to large industrial robots, and discuss the benefits and limitations of each type of platform and present their applicability to different tasks within the domain of robotic painting and artificial creativity research. A unified control interface with a scripting language allows users a simplified usage of different e-David-like machines. Furthermore, we present our system for automated stroke experimentation and recording, which is an advance towards allowing the machine to autonomously learn about brush dynamics. Finally, we also show how e-David can be used by artists “in the field” for different exhibitions.

Improvement of Large Industrial Robot Positioning Accuracy by using Random Forest and Bayesian Optimization

Improvement of Large Industrial Robot Positioning Accuracy by using Random Forest and Bayesian Optimization

Automatic Calibration of an Industrial RGB-D Camera Network Using Retroreflective Fiducial Markers.

This paper describes a non-invasive, automatic, and robust method for calibrating a scalable RGB-D sensor network based on retroreflective ArUco markers and the iterative closest point (ICP) scheme. We demonstrate the system by calibrating a sensor network comprised of six sensor nodes positioned in a relatively large industrial robot cell with an approximate size of . Here, the automatic calibration achieved an average Euclidean error of 3 at distances up to . To achieve robustness, we apply several innovative techniques: Firstly, we mitigate the ambiguity problem that occurs when detecting a marker at long range or low resolution by comparing the camera projection with depth data. Secondly, we use retroreflective fiducial markers in the RGB-D calibration for improved accuracy and detectability. Finally, the repeating ICP refinement uses an exact region of interest such that we employ the precise depth measurements of the retroreflective surfaces only. The complete calibration software and a recorded dataset are publically available and open source.

Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments.

This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m. The main advantage of processing point cloud data locally on the nodes is scalability. The proposed solution could, with a dedicated Gigabit Ethernet local network, be scaled up to approximately 440 sensor nodes, only limited by the processing power of the central node that is receiving the compressed data from the local nodes. A compression ratio of 40.5 was obtained when compressing a point cloud stream from a single Microsoft Kinect V2 sensor using an octree resolution of 4 cm.

Understanding situational and mode awareness for safe human-robot collaboration: case studies on assembly applications

In order for humans and robots to collaborate on an assembly line, safety of operations is a prerequisite. In this article, two assembly stations where a large industrial robots collaborate with humans will be analysed with the aim to 1. determine the characteristics of hazards associated with human-robot interaction and 2. design solutions that can mitigate risks associated with these hazards. To support the aim of this article, a literature review will attempt to characterize automation and detail the problems associated with human-automation interaction. The analysis points at situational awareness and mode-awareness as contributing factors to operator and process safety. These underlying mechanisms, if recognised by the risk assessment team as hazards, can mitigate risks of operator injury or production delays. This article details the function of visual and physical interfaces that allow operators to comprehend system-state in order to avoid undesirable situations.

APPLICATION OF SIMULATION MODELS FOR PROGRAMMING OF ROBOTS

In recent years, there has been an increasing robotization of many areas of life. This requires knowledge of issues related to robots and their programming. Devices of this type, especially large industrial or medical robots, are very expensive, or they may be hardly affordable for educational purposes. Simulation models are helpful in such situation. The aim of the article is to present the possibilities of using simulation models of robots and robotic stations in the educational and research process. Specialized software packages make it easier not only to understand how robots work, but they also allow to test different operating conditions of the designed computer model of the real robotized process.

Collaborative Assembly on a Continuously Moving Line – An Automotive Case Study

Collaborative operation is a state in which a purposely designed robot system and an operator work within a collaborative workspace. To ensure a safe working environment, safety standards suggest conducting a task-based risk assessment followed by risk reduction to reduce the risks to an acceptable level. In this article, an automotive case study will be used to understand safety issues associated with collaborative operations with large industrial robots. Based on this case study, a layout of the collaborative workstation, in terms of workspaces and tasks, which is the outcome of a risk assessment program will be presented. The first critical step in risk assessment is hazard identification, which can allow the risk assessing team to evaluate the hazards and suggest measures to mitigate the risks. Therefore, the hazards identified during the risk assessment program will also be detailed along with measures to mitigate the risks and is expected to complement our understanding of the nature of hazards associated with collaborative operations with large industrial robots.

Safety-Focussed Design of Collaborative Assembly Station with Large Industrial Robots

The perceived benefits of large industrial robots for collaborative operations are characteristics such as long reach with heavy load carrying capability. Collaborative operations refers to situations where operators and robots share a workspace to complete tasks in close proximity. This mode of operation coupled with the physical characteristics of large robots represents high risks to injury and for these reasons, the safeguarding of the workspaces needs to be achieved in conjunction with the tasks to be performed within the workstation. This article will detail two workstations that was developed in a laboratory environment and are partial results of a research project titled ToMM2, whose aim was to understand safety issues associated with collaborative operations with large robots.

Safe Assembly Cell Layout through Risk Assessment – An Application with Hand Guided Industrial Robot

Risk assessment is a systematic and iterative process which involves risk analysis where the probable hazards are identified and corresponding risks are evaluated along with solutions to mitigate the effect of these risks. In this article the outcome of a risk assessment process will be detailed where a large industrial robot is being used as a intelligent and flexible lifting tool that can aid operators in assembly tasks. The realization of a collaborative assembly station has several benefits such as increased productivity and improved ergonomic work environment. The article will detail the design of the layout of a collaborative assembly cell which takes into account the safety and productivity concerns of automotive assembly plants.

A review of the state-of-the-art of 'smart' systems in surgery

In recent years, there has been a considerable transition in smart systems for surgery. Originally systems were devised primarily from large industrial robots. There has been a gradual transition towards sensor rich devices which are much simpler, lower-cost and devised for specific surgical tasks. The history of this development is considered together with examples, and the reasons for this gradual transition are discussed. Specific details are given of the Acrobot 'hands-on' robot for orthopaedic surgery, which is an example of the gradual evolution of a medical robot from a large industrial robot carrying a second robot as an end effector, to a small, low cost smart system for specific tasks.

Design and implementation of a five axis robotic micromanipulator for inserting parts into precision collets

The design, construction, and testing of a five-axis micromanipulator for attachment to the wrist of a large industrial robot is described. The micromanipulator is capable of inserting a part into a collet which is only 0.1–0.2 mm (0.004–0.008 in) larger in diameter than the part, thus compensating for robot positioning errors. The micromanipulator weighs 2.3 kg (5 lbs) and is 64 mm (2.5 in) thick and 127 mm (5 in) square with two linear axes having ±3 mm (0.13 in) of travel, two angular axes having ±4 angular degrees of motion, and one linear degree-of-freedom having ±25 mm (1.0 in) of travel. Hydraulic actuators, operated at 6.9 MPa (1000 lbf/in 2), provide 2700 N (600 1bf) of force along the linear axes, and 68 N-m (600 in-lbf) of torque about angular axes. Repeatability of the device is 0.025 mm (0.001 in).

Application of robotics in blood banking.

To demonstrate the feasibility of using robots in blood banking applications several prototype systems were developed. Activities associated with sample testing and component preparation were examined. In one project, a general-purpose laboratory robot (Zymate Laboratory Automation System, Zymark, Inc., Hopkinton, Mass.) was configured to prepare samples for microplate-based ABO/Rh testing. In a second project, this same robot was configured to carry out specific steps in evaluating bar-coded labels, as part of a quality control procedure. A fluid-handling robot (Sampler 505, Tecan AG, Hombrechtikon, Switzerland) was used to prepare the dilution of serum samples for the evaluation of an anti-HTLV-III test kit. It was then configured to aspirate, dilute and dispense samples for anti-HTLV-III and HBsAg testing. This robotic system is now in field trial. The use of large industrial robots for automating component production was also considered. The key element in this design was the development of a fixture that would hold the blood bag set during the balancing, centrifugation and expressing steps. However, a fixture which was capable of performing these operations and that was equivalent in size and adaptable to a standard centrifuge bucket could not be fabricated.

Active Control of a Compliant Wrist in Manufacturing Tasks

A robotic wrist unit has been constructed and is functioning on a large industrial robot used to load, assemble, and finish parts in an automated metal working cell. The wrist uses force feedback and remote-center-compliance techniques similar to those that have been developed for assembly robots. The wrist employs four spherical hydraulic actuators that behave as adjustable springs with a stiffness that varies between 5.8 × 103 and 7.9 × 103 N/m. This allows the center of compliance to be projected over a range from 25 to 157 mm out from the upper platform of the wrist. The wrist has 5 1/2 degrees-of-freedom, being compliant in each direction except axial extension. Deflections of ± 4.6 mm in the radial plane and 5.1 mm in compression are possible. The accuracy of the sensors over this working range is within 0.025 mm for translations and within 0.001 rad for rotations.

Application of Robotics in Blood Banking

To demonstrate the feasibility of using robots in blood banking applications several prototype systems were developed. Activities associated with sample testing and component preparation were examined. In one project, a general-purpose laboratory robot (Zymate Laboratory Automation System, Zymark, Inc., Hopkinton, Mass.) was configured to prepare samples for microplate-based ABO/Rh testing. In a second project, this same robot was configured to carry out specific steps in evaluating bar-coded labels, as part of a quality control procedure. A fluid-handling robot (Sampler 505, Tecan AG, Hombrechtikon, Switzerland) was used to prepare the dilution of serum samples for the evaluation of an anti-HTLV-III test kit. It was then configured to aspirate, dilute and dispense samples for anti-HTLV-III and HBsAg testing. This robotic system is now in field trial. The use of large industrial robots for automating component production was also considered. The key element in this design was the development of a fixture that would hold the blood bag set during the balancing, centrifugation and expressing steps. However, a fixture which was capable of performing these operations and that was equivalent in size and adaptable to a standard centrifuge bucket could not be fabricated.