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Szybicki, D.;  Obal, P.;  Kurc, K.;  Gierlak, P. Programming of Industrial Robots with Tracker. Encyclopedia. Available online: https://encyclopedia.pub/entry/27004 (accessed on 13 June 2024).
Szybicki D,  Obal P,  Kurc K,  Gierlak P. Programming of Industrial Robots with Tracker. Encyclopedia. Available at: https://encyclopedia.pub/entry/27004. Accessed June 13, 2024.
Szybicki, Dariusz, Paweł Obal, Krzysztof Kurc, Piotr Gierlak. "Programming of Industrial Robots with Tracker" Encyclopedia, https://encyclopedia.pub/entry/27004 (accessed June 13, 2024).
Szybicki, D.,  Obal, P.,  Kurc, K., & Gierlak, P. (2022, September 08). Programming of Industrial Robots with Tracker. In Encyclopedia. https://encyclopedia.pub/entry/27004
Szybicki, Dariusz, et al. "Programming of Industrial Robots with Tracker." Encyclopedia. Web. 08 September, 2022.
Programming of Industrial Robots with Tracker
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Newly installed robots, as well as those already in use, require the development of software or its modification, which is why robot programming methods are a rapidly developing field. The main purpose of the newly developed methods is to speed up the programming process and make it easier for staff to execute it. Reducing programming time is a source of cost savings and is of enormous importance to businesses.
industrial robots robot programming methods laser trackers

1. Lead-Through Programming, On-Line Programming

The lead-through programming [1][2] method consists of the fact that the operator carries out the movement of the robot’s arm using the manual panel, and the individual points of the robot’s trajectory are saved in the controller’s memory. It is the most popular method of programming industrial robots. In this method, the programmer, using his own senses, especially eyesight, has to drive the robot TCP (in accordance with the ISO 10218-1: 2011 tool center point—a point defined for a given application, taking into account the coordinate system of the mechanical interface) to the selected point using the joystick or the manual panel keys. This method is time consuming, relies on the precision of the programmer’s eyesight, and can cause collisions. Passing through the recorded points, the path of the robot’s tip is determined with the given accuracy and speed. This makes it possible to recreate the path set by the operator during the process execution. This method is used, for example, when CAD models of all essential elements of the robotic station do not exist.

2. Walk-Through Programming

One of the forms of programming robots is for the programmer to run the robot tip along the desired trajectory of movement and save the subsequent trajectory points in the memory of the robot controller [1][2]. Such a path is carried out by manually moving the tip of the robot equipped with a force sensor with a special grip. The force applied to the grip by the programmer is detected by the force measuring system and the robot moves the tip in the direction of the force. In the event of a moment force appearing, the robot rotates the tip in the direction of the moment. This method is used in the following cases: the desired robot motion paths are very complicated, and at the same time there are no environment models to program the robot off-line; it is required to program the robot to map the movements carried out so far by the human operator.
The benefit of this programming method is the maximum facilitation of this process and the possibility of its implementation by unskilled personnel, and easy adaptation of robots to the implementation of processes in the event of a change in the range of products.
An extension to walk-through programming is provided by the method of programming by demonstrating [1], whereby the robot is taught movements performed in various conditions and generalize them in new scenarios that have not been demonstrated before. A robotic station must have the ability to learn.

3. Off-Line Programming

The off-line programming method consists of generating robot paths with the use of a virtual environment [3][4]. CAD models of individual robots, auxiliary devices, such as feeders, tool changers, part warehouses, etc., are imported into the environment. It is also possible to use the CAD models of the designed elements. Then, robot paths are generated tracing the indicated contours of the CAD models or connecting the indicated points of the environment. The advantages of this method are the speed of generating paths, the safety of the programmer and hardware, and the convenience of the work. Apart from the advantages of this method, there are also limitations to programming. The basic and often overlooked problem in off-line design and programming is the absolute accuracy of industrial robots and the difference between accuracy and repeatability. Programming off-line requires high robot accuracy. The subject of examining the accuracy and repeatability of robots is the subject of many publications, and the methods of their determination are specified in, inter alia, ISO 9283: 1998. The accuracy of the robot is a measure of how close the robot can get to a given point (usually by coordinates) in the working space. Repeatability is a measure of how close a robot can get to a previously reached position. Robot manufacturers provide repeatability in their catalog cards and it is very difficult to obtain information about their accuracy. Experience shows that if the repeatability of the robot is, for example, 0.03 mm, the accuracy can be even several mm. One way to quickly and efficiently design and then program robots is to use online corrections. In this case, the coordinate systems, program points, and station logic are created off-line. Then, only those points or coordinate systems that require it are corrected on the real online workstation.

4. Programming with the Use of Augmented Reality

Augmented Reality technology belongs to the spectrum of virtual reality methods. Its basic feature is to integrate 3D computer-generated graphic objects into the real-world scene. This approach allows the programming of robots and robotic stations without the need to model all elements of the real environment [5]. It is enough, for example, to use virtual robot arms, whose CAD models are easily accessible, and immerse them in a real robotic station, in which there is a real object with which the robots interact [2]. In this way, robots can be programmed; e.g., for tracing the contours of a workpiece.

5. Programming of Robots Using Virtual Reality and Digital Twins

The essence of this method is the interaction of the operator with elements of the virtual environment. Its aim is to replace humans with robots in tasks whose formal description is complex [6][7][8]. It is usually used in cases where the sequence of movements needed to perform a given task has been experimentally selected by the operator using the method of multiple trials and on the basis of many years of experience. Such examples include grinding turbine blades, cleaning casting molds, painting processes, or complicated assembly of elements. This method is particularly advantageous when it is necessary to maneuver objects that in reality have, e.g., a large mass, as in a virtual environment, this is not a problem.
The most important methods of programming robots presented above have their advantages and disadvantages. It was possible to develop a programming method showing some advantages over the methods presented here and using a laser tracker to indicate the robot’s TCP points.
A laser tracker is a device that allows measurements in three-dimensional space, used for probing, scanning, automated control, and reflector measurements. It is equipped with an absolute rangefinder and a laser interferometer mounted on a biaxial gimbal. Due to their precision, trackers are often used in studies of the accuracy and repeatability of robots [9][10][11]. An example of such a tracker is the Leica AT 960 head (Hexagon, Stockholm, Sweden).
This tracker allows for accurate measurement of the position of a selected point in three directions simultaneously. In the case of measurements on a robot, it is necessary to attach a mirror reflecting the laser beam. Measurement with a laser tracker is classified as coordinate-measuring systems. The principle of operation of trackers is based on the combination of two techniques, namely, laser interferometry, which allows measuring the distance of the target from the measuring head, and measuring the angles of setting two rotational axes: azimuth and height.
In the case of laser trackers, mirrors are used as the measuring target to reflect the laser beam generated by the device. Most often these are Spherically Mounted Retroreflectors (SMR). The mirrors in the retroreflector are mounted precisely at the right angle to each other in such a way that their point of contact (apex) is exactly in the center of the SMR. This makes it possible to precisely determine the coordinates of the position of the retroreflector in three-dimensional space.
Laser trackers in robotic measurements are used for a variety of applications. Theissen, Laspas, and Archenti [12] present an innovative methodology for measuring the susceptibility of articulated serial robots, and a laser tracker is used to measure the response of the system.
Cvitanic, Nguyen, and Melkote [13] used a laser tracker to measure the deflection of the robot end effector during comparative tests and optimization of the robot position using static and dynamic stiffness models for various milling scenarios.
Nguyen and Melkote [14] investigated the modal properties of industrial robots, which change depending on the configuration of the arm in the milling process. A laser tracker was used as a measurement system for the position and orientation of the robot tool in three-dimensional space, to track the robot arm during milling.
Al Khawli et al. [15] presented a method of maintaining a high accuracy of robot manipulation through continuous compensation of the position errors in production processes in the aviation industry. Continuous tracking of the position and orientation of the mounted tool on the robot arm minimize the errors between the tool and the workpiece. For this purpose, among others, the Leica Absolute Tracker laser tracker and the Leica Tracker-Machine Control (T-Mac) (Hexagon, Stockholm, Sweden) reflector were used to record measurements relative to the base of the robot.
Novák et al. and Mei et al. [16][17] considered how to increase the accuracy of industrial robots with the help of the Leica Absolute Tracker AT960 (Hexagon, Stockholm, Sweden). They propose new methods of calibrating robots with tools in their workplace. These methods improve the positioning accuracy by compensating for the identified parameters. The accuracy of the robots, along with the reduction in calibration time, are key factors in the success of robotic production systems.
Fernandez, Olabi, and Gibaru [18] discussed assembly operations in the aviation industry, which are time-consuming and require high accuracy. They emphasized that robotic assembly is a good solution that increases productivity, but pointed out that the poor accuracy of industrial robots limits their use. They proposed an improvement by adding an accurate on-line 3D positioning system, which consists of the KEYENCE LJ-V7200 vision system (Keyence, Osaka, Japan) and the Leica AT-960 + T-Mac TMC-30B (Hexagon, Stockholm, Sweden) tracking system.
Slater et al. [19] presented a new Portable Laser Guided Robotic Metrology (PLGRM) system at the National Aeronautics and Space Agency (NASA) for robot positioning and displacement. This system consists of a cooperating robot arm mounted on a lift and a laser tracker located on a movable base. Together, they allow for scanning an area larger than the range of the robot.
Gonzalez et al. [20] focused on measuring the quasi-static path accuracy and repeatability of industrial manipulators to evaluate their performance in industrial contact applications such as trimming, grinding, or deburring. The validation was performed on an ABB industrial robot using a Leica AT960 laser tracker.
Sanden, Pawlus, and Hovland [21] investigated the ability of a laser tracker to measure the relative position and orientation between two mobile Stewart platforms simulating the movement of ships at sea. These ships are exposed to disturbance from waves and have cranes equipped with active compensation systems on board, which keep the cargo at a certain height from the seabed.
Martin et al. [22] used a laser tracker to improve the accuracy of cable-driven parallel robots. Inaccuracies are caused by deviations in cable lengths caused by elongation, elasticity, or creep.
Theissen, Mohammed, and Archenti [23] focused on modeling, measuring, and identifying the change in the kinematic chain of serial articulated industrial robots based on thermomechanical deformations caused by the self-heating caused by drives. The assessment of the change in the positioning accuracy of the ABB IRB 1600 (ABB Ltd., Zürich, Switzerland) robot was carried out using a Leica AT960 laser tracker and a FLIR SC640 (FLIR, Wilsonville, OR, USA) thermal imaging camera.
Laser distance measuring systems are widely used, not only in industry. Yu, Li, Guan, and Wang [24] used the RIEGL VMX-450 (Riegl, Horn, Austria) system to test a new algorithm to quickly extract objects from urban road spaces, such as lighting poles, road signs, and bus stops. The system uses an array of laser scanners and cameras to map the space around the device. It is equipped with a satellite navigation system and a device for measuring the displacement of the wheels of the vehicle on which it is mounted.
Laser scanners are also often used in autonomous vehicles. Li et al. [25] proposed the use of the LIDAR 3D system in the VFH (Vector Field Histogram) algorithm of a local path planner (generator) for the navigation system of autonomous vehicles.
Another approach to tracking objects in space are vision systems, which use special tags that allow the positions and orientations of an object to be determined. An interesting example of such an approach is presented by Ferreira, Costa, Rocha, and Moreira [26], who built a marker in the shape of an icosahedron. LEDs in five different colors are placed on the walls in such a way as to obtain a unique pattern for each of the walls. The moving marker is tracked by a stereoscopic vision system. This system was used to teach robots painting movements.
As indicated, laser trackers have a wide range of applications in robotics; however, after researching the market and browsing research and websites, no applications of the tracker for generating (programming) paths for industrial robots were found.

References

  1. Villani, V.; Pini, F.; Leali, F.; Secchi, C.; Fantuzzi, C. Survey on human-robot interaction for robot programming in industrial applications. IFAC-PapersOnLine 2018, 51, 66–71.
  2. Pan, Z.; Polden, J.; Larkin, N.; Van Duin, S.; Norrish, J. Recent progress on programming methods for industrial robots. Robot. Comput. Integr. Manuf. 2012, 28, 87–94.
  3. Neto, P.; Mendes, N. Direct off-line robot programming via a common CAD package. Robot. Auton. Syst. 2013, 61, 896–910.
  4. Obal, P.; Gierlak, P. EGM Toolbox-Interface for Controlling ABB Robots in Simulink. Sensors 2021, 21, 7463.
  5. Pettersen, T.; Pretlove, J.; Skourup, C.; Engedal, T.; Lokstad, T. Augmented reality for programming industrial robots. In Proceedings of the Second IEEE and ACM International Symposium on Mixed and Augmented Reality, Tokyo, Japan, 7–10 October 2003.
  6. Burghardt, A.; Szybicki, D.; Gierlak, P.; Kurc, K.; Pietruś, P.; Cygan, R. Programming of Industrial Robots Using Virtual Reality and Digital Twins. Appl. Sci. 2020, 10, 486.
  7. Szybicki, D.; Kurc, K.; Gierlak, P.; Burghardt, A.; Muszyńska, M.; Uliasz, M. Application of Virtual Reality in Designing and Programming of Robotic Stations. In Collaborative Networks and Digital Transformation; Camarinha-Matos, L., Afsarmanesh, H., Antonelli, D., Eds.; PRO-VE 2019; IFIP Advances in Information and Communication Technology; Springer: Cham, Switzerland, 2019; Volume 568.
  8. Muszyńska, M.; Szybicki, D.; Gierlak, P.; Kurc, K.; Burghardt, A.; Uliasz, M. Application of Virtual Reality in the Training of Operators and Servicing of Robotic Stations. In Collaborative Networks and Digital Transformation; Camarinha-Matos, L., Afsarmanesh, H., Antonelli, D., Eds.; PRO-VE 2019; IFIP Advances in Information and Communication Technology; Springer: Cham, Switzerland, 2019; Volume 568.
  9. Schröer, K.; Bernhardt, R.; Albright, S.; Wörn, H.; Kyle, S.; van Albada, D.; Smyth, J.; Meyer, R. Calibration applied to quality control in robot production. Control. Eng. Pract. 1995, 3, 575–580.
  10. Ginani, L.S.; Motta, J.M.S. Theoretical and practical aspects of robot calibration with experimental verification. J. Braz. Soc. Mech. Sci. Eng. 2011, 33, 15–21.
  11. Nubiola, A.; Bonev, I.A. Absolute calibration of an ABB IRB 1600 robot using a laser tracker. Robot. Comput. Integr. Manuf. 2013, 29, 236–245.
  12. Theissen, N.A.; Laspas, T.; Archenti, A. Closed-force-loop elastostatic calibration of serial articulated robots. Robot. Comput. Integr. Manuf. 2019, 57, 86–91.
  13. Cvitanic, T.; Nguyen, V.; Melkote, S.N. Pose optimization in robotic machining using static and dynamic stiffness models. Robot. Comput. Integr. Manuf. 2020, 66, 101992.
  14. Nguyen, V.; Melkote, S.N. Identification of industrial robot frequency response function for robotic milling using operational modal analysis. Procedia Manuf. 2020, 48, 154–158.
  15. Al Khawli, T.; Anwar, M.; Sunda-Meya, A.; Islam, S. A calibration method for laser guided robotic manipulation for industrial automation. In Proceedings of the IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 21–23 October 2018.
  16. Novák, P.; Stoszek, Š.; Vyskočil, J. Calibrating industrial robots with absolute position tracking system. In Proceedings of the 2020-25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 8–11 September 2020.
  17. Mei, B.; Xie, F.; Liu, X.J.; Li, H. Calibration of a 6-DOF industrial robot considering the actual mechanical structures and CNC system. In Proceedings of the 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), Shanghai, China, 29–31 December 2017.
  18. Fernandez, S.R.; Olabi, A.; Gibaru, O. On-line Accurate 3D Positioning Solution for Robotic Large-Scale Assembly Using a Vision System and a 6Dof Tracking Unit. In Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 12–14 October 2018.
  19. Slater, P.A.; Downey, J.M.; Piasecki, M.T.; Schoenholz, B.L. Portable laser guided robotic metrology system. In Proceedings of the 2019 Antenna Measurement Techniques Association Symposium (AMTA), IEEE, San Diego, CA, USA, 6–11 October 2019.
  20. Gonzalez, M.; Hosseini, A.; Theissen, N.; Archenti, A. Quasi-static loaded circular testing of serial articulated industrial manipulators. In Proceedings of the ISR 2020-52th International Symposium on Robotics, VDE, Online, 9–10 December 2020.
  21. Sanden, S.; Pawlus, W.; Hovland, G. Real-time 6-DOF Vessel-to-Vessel Motion Compensation using laser tracker. In Proceedings of the OCEANS 2017-Aberdeen, Aberdeen, UK, 19–22 June 2017.
  22. Martin, C.; Fabritius, M.; Stoll, J.T.; Pott, A. A laser-based direct cable length measurement sensor for CDPRs. Robotics 2021, 10, 60.
  23. Theissen, N.A.; Mohammed, A.; Archenti, A. Articulated industrial robots: An approach to thermal compensation based on joint power consumption. Laser Metrol. Mach. Perform. 2019, 13, 81–90.
  24. Yu, Y.; Li, J.; Guan, H.; Wang, C. Automated extraction of urban road facilities using mobile laser scanning data. IEEE Trans. Intell. Transp. Syst. 2015, 16, 2167–2181.
  25. Li, J.; Bao, H.; Han, X.; Pan, F.; Pan, W.; Zhang, F.; Wang, D. Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS. Multimed. Tools Appl. 2017, 76, 23017–23039.
  26. Ferreira, M.; Costa, P.; Rocha, L.; Moreira, A.P. Stereo-based real-time 6-DoF work tool tracking for robot programing by demonstration. Int. J. Adv. Manuf. Technol. 2016, 85, 57–69.
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