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Zhang Sprenger, C.; Corrales Ramón, J.A.; Baier, N.U. Robot Task Modeling and Notation 2.0. Encyclopedia. Available online: https://encyclopedia.pub/entry/53823 (accessed on 20 May 2024).
Zhang Sprenger C, Corrales Ramón JA, Baier NU. Robot Task Modeling and Notation 2.0. Encyclopedia. Available at: https://encyclopedia.pub/entry/53823. Accessed May 20, 2024.
Zhang Sprenger, Congyu, Juan Antonio Corrales Ramón, Norman Urs Baier. "Robot Task Modeling and Notation 2.0" Encyclopedia, https://encyclopedia.pub/entry/53823 (accessed May 20, 2024).
Zhang Sprenger, C., Corrales Ramón, J.A., & Baier, N.U. (2024, January 15). Robot Task Modeling and Notation 2.0. In Encyclopedia. https://encyclopedia.pub/entry/53823
Zhang Sprenger, Congyu, et al. "Robot Task Modeling and Notation 2.0." Encyclopedia. Web. 15 January, 2024.
Robot Task Modeling and Notation 2.0
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This entry is adapted from the peer-reviewed paper 10.3390/app14010283

RTMN 2.0, an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. Intuitive robot programming allows those without programming expertise to plan and control robots through easily understandable predefined modeling notations. These notations achieve no-code programming and serve as templates for users to create their processes via drag-and-drop functions with graphical representations.

RTMN HRC HRC mode HRC task

1. What Is Human–Robot Collaboration (HRC)?

The ever-growing advancements in the manufacturing industry have made it necessary for companies to optimize their systems by decreasing human workload, fatigue risk, and overall costs. This necessity has led to the introduction of human–robot collaboration in industrial environments [1]. Industry experts have established that the complete removal of humans from manufacturing systems is not viable. A more realistic goal is to have humans and intelligent machines working in harmony [2]. From an anthropological standpoint, “collaboration” refers to multiple parties communicating with each other and coordinating their actions in order to achieve a common goal. To this aim, collaborators observe each other, infer the intent behind an action, and plan their own actions in accordance with this intent. Similarly, in human–robot collaboration, robot systems should be designed with the appropriate tools to coordinate their actions with humans and employ the relevant cognitive and communicative mechanisms such that they can plan actions toward an established goal [3].
For the successful implementation of human–robot collaboration, the machines would require advanced cognitive capabilities to allow the human operators to collaborate comfortably and efficiently and maintain high confidence in these systems. If implemented correctly, industries may achieve a reasonable task reduction for human operators. To this end, robots should be equipped with understanding capabilities that allow them to operate with a human, just as two humans would when working together [4]. HRC systems do not rely on an equal divide of workload between humans and robots. The levels of robot automation are based on the application and are decided such that they lead to an overall improvement in the system’s performance [5]. This improvement in performance can be attributed to the complementary strengths of either party. Robots offer an efficient and guaranteed performance at high speeds, whilst humans offer understanding, reasoning, and problem solving [1].

2. The Importance of Human–Robot Collaboration (HRC)

The aim of industrial robotics is to enable efficient performance repeatedly and accurately [6]. Currently, assembly lines have important requirements related to adaptability, mainly due to the rapid rate at which new products are introduced to the market, as well as changing technologies. The current trends in industry reflect a shift from “mass-production” to “mass-customization”. Products now come with numerous variants or upgrades and a much shorter product lifetime. This imposes a challenge regarding flexibility and adaptability on the manufacturing process, a challenge for which human–robot collaboration is an attractive arrangement [7]. Companies have traditionally relied on robots with in-built capabilities and limited flexibility. However, this level of flexibility is not enough to match the current market’s demands [8]. While traditional robot systems provide high payload capabilities and repeatability, they then suffer from limited flexibility and dexterity [9]. Thus, human–robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in industrial scenarios. The recent trend of automation and data exchange, known as Industry 4.0, also supports the use of collaborative systems in industry. The aim of Industry 4.0 is to achieve efficiency, cost reduction, and increased productivity by means of integrated automation. This aim highlights the need for flexible and interoperable systems, including intelligent decision-making software and robots that can be quickly, safely, and intuitively operated by humans [10].
Industries are increasingly relying on HRC arrangements, both from an engineering perspective and from a socio-economic standpoint. While the manufacturing industry is a significant source of employment, it has been reported that most jobs offered by this sector may remain unfilled. This is attributed to a shortage of workers with the relevant technological and technical skills [11]. HRC is, therefore, a promising alternative, which makes up for the skill gap, still requires human operators, and may be more attractive to the younger generation. Additionally, robotic systems result in higher competitiveness with countries with cheap labor systems and increase trust in the company’s technological aptitude. Collaborative systems also alleviate the ergonomic burden on human workers, resulting in an improved work environment and a reduction in occupational injuries. This makes environments that include both robots and human laborers more attractive to interested partners, customers, and the public [12]. Modern technologies of intuitive systems such as augmented reality, walkthrough programming, and programming by demonstration are all simple methods to operate collaborative robots, unlike the advanced technical expertise necessary to operate traditional robotic systems [10].
Currently, collaborative robotic solutions are attractive even to small- and medium-sized companies since such systems are more affordable, compact, and easy to use compared with traditional robotic systems. Traditionally, factory floors have had strict divisions of labor, with robots confined to strict safety cages far from humans. Collaborative robots overcome this division of labor, allowing humans and robots to work closely together. In doing so, the advantages of strength and automation of the robot are combined with the flexibility and intuitive nature of the human [9][10]. Evidently, there are numerous advantages to collaborative robotic systems, including economic, social, and ergonomic improvements to traditional systems. However, to harness the full benefits of such systems, companies should adhere to the appropriate safety standards to ensure optimal operation.

3. Safety Standards and HRC Modes

The International Federation of Robotics has reported an all-time high of 517,385 new industrial robots installed in 2021 in factories around the world, with a growth rate of 31%, increased by 22% compared to 2018 (pre-pandemic record). Until now, the stock of operational robots around the globe has reached 3.5 million units [13]. With the increasing use of robots in industry, standardization and guidelines to ensure the safety of human operators are required [7]. Many standards have been proposed to give guidelines for the safe use of collaborative robots. The machinery safety is regulated under the Machinery Directive, which covers the scope of collaborative applications [7]. The following reference standards are reported (see Table 1):
Table 1. Safety standards.
Type Description Standard
Type A Standard Basic safety standards for general requirements ISO 12100: 2010 “Machine safety, general design principles, risk assessment, and risk reduction” IEC 61508: terminology and methodology [14]
Type B Standard Generic safety standards
–
B1 standards (ISO 13849-1, IEC 62061): specific safety aspects [15]
–
B2 standards (ISO 13850, ISO 13851): safeguard [16][17]
Type C Standard Safety countermeasures for specific machineryPrioritized over Type A and Type B standards
–
ISO 10218: safety of industrial robots
–
ISO 10218-1:2011 “Robots and equipment for robots, Safety requirements for industrial robots, Part 1: Robots”, safety requirements for robot manufacturers (robot and controller) [18]
–
ISO 10218-2: 2011 “Robots and equipment for robots, Safety requirements for industrial robots, Part 2: Systems and integration of robots”, safety requirements for system integrators (robot and ancillary devices), [19]
–
ISO TS 15066: 2016 “Robots and robotic devices, Collaborative Robots”, guidance on collaborative robot Operations [20][21]
Four categories of safety requirements are defined for collaborative robots in the type C international standards (ISO 10218-1, ISO 10218-2, and ISO TS 15066) [7][9][10]:
  • Safety-rated monitored stop (SMS)
SMS [7][10] is a collaboration arrangement in which robot motion is stopped before a human operator enters the collaborative workspace to interact and carry out a task with the robotic system. This is the most basic form of collaboration and takes place within a collaborative area, that is, an area of operation shared by the robot and the human. Both parties can work in this area, but not simultaneously, since the robot cannot move if the operator is in the shared space. Therefore, it is ideal for tasks in which the robot primarily works alone and is occasionally interrupted by a human operator. Examples of such tasks include visual inspection or the positioning of heavy components by the robot for the human.
  • Hand-guiding (HG)
Another mode of collaboration is known as hand-guiding [7][10], or “direct teach”. In this mode, the operator simply moves the robot to teach it significant positions, without the use of intermediate interfaces such as teach pendants. These positions are communicated as commands to the robot system. Throughout this process, the robot arm’s weight is compensated such that its position is held. A guiding, hand-operated device is used by the operator to guide the robot’s motion. For this advanced form of collaboration, the robot must be equipped with safety-rated monitored stop and speed functionalities. Once the robot has learned the motion and the human operator has left the collaborative area, the robot may execute the program in automatic mode. However, if the operator enters the area, the program is interrupted. When the operator is using the hand-guiding device, the robot operates in a state of safety-rated monitored speed functionality until the operator releases the arm and leaves the collaborative area, allowing the robot to resume automatic operation once again.
  • Speed and separation monitoring (SSM)
In SSM [7][10], the robot operates even when a human is present by means of safety-rated monitoring sensors. Thus, human and robot operations take place simultaneously. To reduce risks, a stipulated protective distance must always be kept between the two parties. If this distance is not kept, the robot operation stops and only resumes once the operator has moved away from the system. If the robot system operates at a reduced speed, the protective distance is reduced accordingly. The workspace may be divided into “zones”, whereby if the human is in the green zone, the robot may operate at full speed, if in the yellow zone, the robot operates with reduced speed, and if the human enters the red zone, the robot’s operation is stopped. Vision systems are used to monitor these zones.
  • Power and force limiting (PFL)
PFL [7][10] is a collaborative approach in which limits are set for motor power and force such that the human operator and robot may work side-by-side. These limits are set as a risk reduction method, defined by a risk assessment. To implement this approach, specific equipment and control modes are required in order to handle collisions between the robot and human and prevent any injuries to the human.
These four collaborative modes can be applied to both traditional industrial robots and collaborative robots. For traditional industrial robots, additional safety devices such as laser sensors or light curtains are required. On the other hand, for collaborative additional features such as force and torque sensors, force limits, vision systems, laser systems, and anti-collision systems are required [9].

4. HRC Task Types

It is important to analyze the different types of collaboration tasks [7][9][10]. Matheson et al. used the classification that Müller et al. [22] proposed for human–robot collaboration in their paper [9], which distinguishes HRC task types into four groups: coexistence (same environment, no interaction), synchronized (same workspace, different times), cooperation (same workspace, at the same time, separate tasks), and collaboration (same workspace, same task, same time). Wang et al. [23] presented the following types in their paper: coexistence (not sharing workspace, no direct contact), interaction (sharing workspace, communicating with each other, performing tasks sequentially), cooperation (sharing workspace, having individual goals, sharing resources, working simultaneously), and collaboration (joint activity, sharing workspace, having the same goal, physical contact allowed). Thiemermann [24] differentiated four operating modes: manual mode (human), automation (robot), parallelization (same product, direct contact, suitable for pre-assembly), and collaboration (same product, work together). There are other classifications in the literature [25][26][27]. To summarize, there are four basic task types for HRC based on the literature: coexistence, sequential cooperation, parallel cooperation, and collaboration.

References

  1. Li, Y.; Ge, S.S. Human-Robot Collaboration Based on Motion Intention Estimation. IEEE/ASME Trans. Mechatron. 2014, 19, 1007–1014.
  2. Weiss, A.; Wortmeier, A.K.; Kubicek, B. Cobots in Industry 4.0: A Roadmap for Future Practice Studies on Human-Robot Collaboration. IEEE Trans. Hum. Mach. Syst. 2021, 51, 335–345.
  3. Lubold, N.; Walker, E.; Pon-Barry, H. Effects of voice-adaptation and social dialogue on perceptions of a robotic learning companion. In Proceedings of the HRI’16: The 11th ACM/IEEE International Conference on Human Robot Interation, Christchurch, NZ, USA, 7–10 March 2016.
  4. Institute of Electrical and Electronics Engineers. A Special Project of the IEEE Region 3 Strategic Planning Committee; IEEE: Piscataway, NJ, USA, 2015.
  5. Freedy, A.; DeVisser, E.; Weltman, G.; Coeyman, N. Measurement of Trust in Human-Robot Collaboration. In Proceedings of the International Symposium on Collaborative Technologies and Systems, Orlando, FL, USA, 25 May 2007.
  6. IEEE Staff. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems; IEEE: Piscataway, NJ, USA, 2010.
  7. Kumar, S.; Savur, C.; Sahin, F. Survey of Human-Robot Collaboration in Industrial Settings: Awareness, Intelligence, and Compliance. IEEE Trans. Syst. Man. Cybern. Syst. 2021, 51, 280–297.
  8. Kock, S.; Vittor, T.; Matthias, B.; Jerregard, H.; Källman, M.; Lundberg, I.; Hedelind, M. Robot concept for scalable, flexible assembly automation: A technology study on a harmless dual-armed robot. In Proceedings of the IEEE International Symposium on Assembly and Manufacturing, Tampere, Finland, 25–27 May 2011.
  9. Matheson, E.; Minto, R.; Zampieri, E.G.G.; Faccio, M.; Rosati, G. Human–Robot Collaboration in Manufacturing Applications: A Review. Robotics 2019, 8, 100.
  10. Villani, V.; Pini, F.; Leali, F.; Secchi, C. Survey on Human-Robot Collaboration in Industrial Settings: Safety, Intuitive Interfaces and Applications. Mechatronics 2018, 55, 248–266.
  11. Kim, A. A Shortage of Skilled Workers Threatens Manufacturing’s Rebound. Available online: https://www.ge.com/news/reports/a-shortage-of-skilled-workers-threatens-manufacturings-r (accessed on 23 October 2023).
  12. Vysocky, A.; Novak, P. Human—Robot Collaboration in Industry. MM Sci. J. 2016, 903–906.
  13. International Federation of Robotics. Available online: https://ifr.org/ (accessed on 18 July 2023).
  14. ISO 12100:2010; Safety of Machinery—General Principles for Design—Risk Assessment and Risk Reduction. International Organization for Standardization: Geneva, Switzerland, 2010.
  15. ISO 13849-1:2023; Safety-Related Parts of Control Systems—Part 1: General Principles for Design. International Organization for Standardization: Geneva, Switzerland, 2023.
  16. ISO 13850:2015; Safety of Machinery—Emergency Stop Function—Principles for Design. International Organization for Standardization: Geneva, Switzerland, 2015.
  17. ISO 13851:2019; Safety of Machinery—Two-Hand Control Devices—Principles for Design and Selection. International Organization for Standardization: Geneva, Switzerland, 2019.
  18. ISO 10218-1:2011; Robots and Robotic Devices—Safety Requirements for Industrial Robots—Part 1: Robots. International Organization for Standardization: Geneva, Switzerland, 2011.
  19. ISO 10218-2:2011; Robots and Robotic Devices—Safety Requirements for Industrial Robots—Part 2: Robot Systems and Integration. International Organization for Standardization: Geneva, Switzerland, 2011.
  20. EC 62061; Safety of Machinery Functional Safety of Safety-Related Electrical, Electronic and Programmable Electronic Control System. International Electrotechnical Commission: London, UK, 2005.
  21. ISO/TS 15066:2016; Robots and Robotic Devices—Collaborative Robots. International Organization for Standardization: Geneva, Switzerland, 2016.
  22. Müller, R.; Vette, M.; Geenen, A. Skill-Based Dynamic Task Allocation in Human-Robot-Cooperation with the Example of Welding Application. Procedia Manuf. 2017, 11, 13–21.
  23. Wang, L.; Gao, R.; Váncza, J.; Krüger, J.; Wang, X.V.; Makris, S.; Chryssolouris, G. Symbiotic Human-Robot Collaborative Assembly. CIRP Ann. 2019, 68, 701–726.
  24. Thiemermann, S. Direkte Mensch-Roboter-Kooperation in Der Kleinteilemontage Mit Einem SCARA-Roboter. Ph.D. Thesis, University of Stuttgart, Stuttgart, Germany, 2004.
  25. Müller, R.; Vette, M.; Mailahn, O. Process-Oriented Task Assignment for Assembly Processes with Human-Robot Interaction. Procedia CIRP 2016, 44, 210–215.
  26. Vincent Wang, X.; Kemény, Z.; Váncza, J.; Wang, L. Human-Robot Collaborative Assembly in Cyber-Physical Production: Classification Framework and Implementation. CIRP Ann. 2017, 66, 5–8.
  27. Krü, J.; Lien, T.K.; Verl, A. Cooperation of Human and Machines in Assembly Lines. CIRP Ann. 2009, 58, 628–646.
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Subjects: Robotics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Congyu Zhang Sprenger
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Juan Antonio Corrales Ramón
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Norman Urs Baier
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Update Date: 15 Jan 2024
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