Abstract
In response to the growing relevance of collaborative robots, the need for empirical user studies in the domain of Human-Robot Collaboration become increasingly important. While collaborative robots incorporate internal safety features, their usage for user studies remains associated with inherent safety risks. This project addresses these challenges by introducing a toolbox for a robot arm to conduct Wizard-of-Oz studies by using advanced controls complemented by a sophisticated security system leveraging microcontrollers and human presence detection sensors. This approach unifies both control systems within a single application, seamlessly monitoring and synchronizing their respective inputs. The gamepad control scheme offers Wizard-of-Oz study supervisors an intuitive means of interacting with the robot, enabling precise and responsive control while maintaining safety. Meanwhile, the security system, built on microcontroller technology and human presence detection sensors, acts as a vigilant guardian, continuously assessing the robot’s surroundings for potential risks. This integrated application not only empowers users with effective control over the xArm 7 but also provides real-time feedback on the security status, enhancing the overall safety and usability of collaborative robots in various industrial settings. By bridging the gap between human operators and robots, this project contributes to the evolution of safer and more user-friendly human-robot collaboration.
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DIN EN ISO 10218-1:2012-01. Industrieroboter_- Sicherheitsanforderungen_- Teil_1: Roboter (ISO_10218-1:2011). Technical report, Beuth Verlag GmbH. https://doi.org/10.31030/1733801. https://www.beuth.de/de/-/-/136373717
HC-SR501 PIR Sensor, July 2021. https://components101.com/sensors/hc-sr501-pir-sensor
Arntz, A., Eimler, S.C., Hoppe, H.U.: A virtual sandbox approach to studying the effect of augmented communication on human-robot collaboration. Front. Robot. AI 8, 318 (2021). https://doi.org/10.3389/frobt.2021.728961. https://www.frontiersin.org/article/10.3389/frobt.2021.728961
Bdiwi, M.: Integrated sensors system for human safety during cooperating with industrial robots for handing-over and assembling tasks. Procedia CIRP 23, 65–70 (2014). https://doi.org/10.1016/j.procir.2014.10.099. https://linkinghub.elsevier.com/retrieve/pii/S2212827114011561
Bonaiuto, S., Cannavó, A., Piumatti, G., Paravati, G., Lamberti, F.: Tele-operation of robot teams: a comparison of gamepad-, mobile device and hand tracking-based user interfaces. In: 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), July 2017, vol. 2, pp. 555–560 (2017). ISSN 0730-3157. https://doi.org/10.1109/COMPSAC.2017.278
Crainic, M.F., Preitl, S.: Ergonomic operating mode for a robot arm using a game-pad with two joysticks. In: 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics, May 2015, pp. 167–170 (2015). https://doi.org/10.1109/SACI.2015.7208192
Fang, Z., et al.: A CMOS-integrated radar-assisted cognitive sensing platform for seamless human-robot interactions. In: 2021 IEEE International Symposium on Circuits and Systems (ISCAS), May 2021, pp. 1–4 (2021). ISSN 2158-1525. https://doi.org/10.1109/ISCAS51556.2021.9401535
García-Esteban, J.A., Piardi, L., Leitão, P., Curto, B., Moreno, V.: An interaction strategy for safe human co-working with industrial collaborative robots. In: 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), May 2021, pp. 585–590 (2021). https://doi.org/10.1109/ICPS49255.2021.9468160
Gong, P., Wang, C., Zhang, L.: MMPoint-GNN: graph neural network with dynamic edges for human activity recognition through a Millimeter-Wave Radar. In: 2021 International Joint Conference on Neural Networks (IJCNN), July 2021, pp. 1–7 (2021). ISSN 2161-4407. https://doi.org/10.1109/IJCNN52387.2021.9533989
Sh, K., Rejab, Rauf, W.E.: Wireless mobile robotic arm controlled by PS2 joystick based on microcontroller. Diyala J. Eng. Sci. 10(3), 44–53 (2017). https://doi.org/10.24237/djes.2017.10304. https://djes.info/index.php/djes/article/view/196
Shenzhen UFACTORY Co. Ltd.: UFACTORY xArm Developer Manual (V 1.10.0). https://www.ufactory.cc/_files/ugd/896670_a7d9cf96096a489bbc0d9ee4ef12939f.pdf
Shenzhen UFACTORY Co. Ltd.: UFACTORY xArm Overview. https://www.ufactory.cc/xarm-collaborative-robot
Shenzhen UFACTORY Co. Ltd.: UFACTORY xArm User Manual (V 1.11.0). https://www.ufactory.cc/_files/ugd/896670_5c6cd91dcf944ebaac182d1e2db1c938.pdf
Maurtua, I., Ibarguren, A., Kildal, J., Susperregi, L., Sierra, B.: Human-robot collaboration in industrial applications: safety, interaction and trust. Int. J. Adv. Robot. Syst. 14(4) (2017). https://doi.org/10.1177/1729881417716010. http://journals.sagepub.com/doi/10.1177/1729881417716010
Nanzer, J.A., Rogers, R.L.: Human presence detection using millimeter-wave radiometry. IEEE Trans. Microw. Theor. Tech. 55(12), 2727–2733 (2007). Conference Name: IEEE Transactions on Microwave Theory and Techniques. https://doi.org/10.1109/TMTT.2007.909872
Owens, T.: Collaborative robots: global market size 2020–2030, May 2021. https://www.statista.com/statistics/1239304/size-of-the-collaborative-robot-cobot-market/
Rahman, R., Rahman, M.S., Bhuiyan, J.R.: Joystick controlled industrial robotic system with robotic arm. In: 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), pp. 31–34, November 2019. https://doi.org/10.1109/RAAICON48939.2019.18
Reynolds, N.: LD2410. GitHub. https://github.com/ncmreynolds/ld2410
Shenzhen Hi-Link Electronic, Co., Ltd.: HLK-LD2410 user manual V1.02.pdf, August 2022. https://drive.google.com/file/d/1ZBhv4EmuiB2wA-VdW0Cx2oJJLwI_CmVn/view?usp=drive_open &usp=embed_facebook
Sherwani, F., Asad, M.M., Ibrahim, B.: Collaborative robots and industrial revolution 4.0 (IR 4.0). In: 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), March 2020, pp. 1–5 (2020). https://doi.org/10.1109/ICETST49965.2020.9080724
UFACTORY: xArm-Python-SDK-github. https://github.com/xArm-Developer/xArm-Python-SDK/blob/master/doc/api/xarm_api.md
Wagner, M., Avdic, D., Heß, P.: Gamepad control for industrial robots - new ideas for the improvement of existing control devices:. In: Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics, pp. 368–373. SCITEPRESS - Science and and Technology Publications, Lisbon, Portugal (2016). https://doi.org/10.5220/0005982703680373. http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0005982703680373
Acknowledgements
Gratitude is expressed towards Leopold Bletgen, Oliver Witzke, and Phillip Lösch for assisting in this project. Further, thanks go to Sabrina Eimler, Carolin Straßmann, André Helgert, Lukas Erle, Lara Timm, and Marcel Finkel for additional support throughout this project.
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Arntz, A. (2024). Enabling Safe Empirical Studies for Human-Robot Collaboration: Implementation of a Sensor Array Driven Control Interface. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2024. Lecture Notes in Computer Science, vol 14685. Springer, Cham. https://doi.org/10.1007/978-3-031-60412-6_4
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