Research Article
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Year 2026, Volume: 15 Issue: 2, 127 - 138, 29.01.2026

Abstract

Project Number

2

References

  • [1] Y. R. Stürz, L. M. Affolter, and R. S. Smith, “Parameter Identification of the KUKA LBR iiwa Robot Including Constraints on Physical Feasibility,” in IFAC-PapersOnLine, Elsevier B.V., Jul. 2017, pp. 6863–6868. doi: 10.1016/j.ifacol.2017.08.1208.
  • [2] “LBR iiwa | KUKA AG.” Accessed: Jul. 16, 2023. [Online]. Available: https://www.kuka.com/en-de/products/robot-systems/industrial-robots/lbr-iiwa
  • [3] D. Calandra, A. Cannavò, and F. Lamberti, “Improving AR-powered remote assistance: a new approach aimed to foster operator’s autonomy and optimize the use of skilled resources,” International Journal of Advanced Manufacturing Technology, vol. 114, no. 9–10, pp. 3147–3164, Jun. 2021, doi: 10.1007/s00170-021-06871-4.
  • [4] T. Rossmeissl, E. Groß, L. Zarco, T. Schlegel, J. Siegert, and T. Bauernhansl, “Approach for extending evaluation criteria for scalable and modular industrial robots,” in Procedia CIRP, Elsevier B.V., 2019, pp. 1022–1027. doi: 10.1016/j.procir.2019.03.245.
  • [5] A. Hietanen, R. Pieters, M. Lanz, J. Latokartano, and J. K. Kämäräinen, “AR-based interaction for human-robot collaborative manufacturing,” Robot Comput Integr Manuf, vol. 63, Jun. 2020, doi: 10.1016/j.rcim.2019.101891.
  • [6] S. Hjorth and D. Chrysostomou, “Human–robot collaboration in industrial environments: A literature review on non-destructive disassembly,” Feb. 01, 2022, Elsevier Ltd. doi: 10.1016/j.rcim.2021.102208.
  • [7] F. Semeraro, A. Griffiths, and A. Cangelosi, “Human–robot collaboration and machine learning: A systematic review of recent research,” Feb. 01, 2022, Elsevier Ltd. doi: 10.1016/j.rcim.2022.102432.
  • [8] M. Vistein, J. Faber, C. Schmidt-Eisenlohr, and D. Reiter, “Automated handling of auxiliary materials using a multi-kinematic gripping system,” in Procedia Manufacturing, Elsevier B.V., 2019, pp. 1276–1283. doi: 10.1016/j.promfg.2020.01.220.
  • [9] K. Chen, J. Wen, J. Wu, and Z. Ji, “Automated robot-based large-scale 3D surface imaging,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 2949–2958. doi: 10.1016/j.procs.2020.09.208.
  • [10] F. Müller, C. Deuerlein, and M. Koch, “Cyber-physical-system for representing a robot end effector,” in Procedia CIRP, Elsevier B.V., 2021, pp. 307–312. doi: 10.1016/j.procir.2021.05.071.
  • [11] M. Ikeda, N. Chitturi, M. Ganglbauer, and A. Pichler, “Knowledge based accuracy improvement in programming by demonstration of point based processes,” in Procedia Manufacturing, Elsevier B.V., 2021, pp. 16–23. doi: 10.1016/j.promfg.2021.10.004.
  • [12] V. V. Nair, D. Kuhn, and V. Hummel, “Development of an easy teaching and simulation solution for an autonomous mobile robot system,” in Procedia Manufacturing, Elsevier B.V., 2019, pp. 270–276. doi: 10.1016/j.promfg.2019.03.043.
  • [13] D. Calandra, F. G. Pratticò, A. Cannavò, C. Casetti, and F. Lamberti, “Digital twin- and extended reality-based telepresence for collaborative robot programming in the 6G perspective,” Digital Communications and Networks, Oct. 2022, doi: 10.1016/j.dcan.2022.10.007.
  • [14] H. Zhong, P. Li, L. Gao, and X. Li, “Low-delay admittance control of hydraulic series elastic actuator for safe human-robot collaboration,” in Procedia Manufacturing, Elsevier B.V., 2020, pp. 147–153. doi: 10.1016/j.promfg.2020.05.031.
  • [15] M. N. Vu, F. Beck, M. Schwegel, C. Hartl-Nesic, A. Nguyen, and A. Kugi, “Machine learning-based framework for optimally solving the analytical inverse kinematics for redundant manipulators,” Mechatronics, vol. 91, p. 102970, May 2023, doi: 10.1016/j.mechatronics.2023.102970.
  • [16] M. Safeea, P. Neto, and R. Béarée, “Model-based hardware in the loop control of collaborative robots,” in Procedia Manufacturing, Elsevier B.V., 2020, pp. 133–139. doi: 10.1016/j.promfg.2020.10.020.
  • [17] T. Weingartshofer, B. Bischof, M. Meiringer, C. Hartl-Nesic, and A. Kugi, “Optimization-based path planning framework for industrial manufacturing processes with complex continuous paths,” Robot Comput Integr Manuf, vol. 82, Aug. 2023, doi: 10.1016/j.rcim.2022.102516.
  • [18] M. Linsinger, J. Stecken, J. Kutschinski, and B. Kuhlenkötter, “Situational task change of lightweight robots in hybrid assembly systems,” in Procedia CIRP, Elsevier B.V., 2019, pp. 81–86. doi: 10.1016/j.procir.2019.03.015.
  • [19] P. Gümbel, X. He, and K. Dröder, “Precision optimized pose and trajectory planning for vertically articulated robot arms,” in Procedia CIRP, Elsevier B.V., 2022, pp. 185–190. doi: 10.1016/j.procir.2022.02.176.
  • [20] Y. Bulut and E. S. Conkur, “A real-time path-planning algorithm with extremely tight maneuvering capabilities for hyper-redundant manipulators,” Engineering Science and Technology, an International Journal, vol. 24, no. 1, pp. 247–258, Feb. 2021, doi: 10.1016/j.jestch.2020.07.002.
  • [21] H. Maithani, J. A. C. Ramon, L. Lequievre, Y. Mezouar, and M. Alric, “Exoscarne: Assistive strategies for an industrial meat cutting system based on physical human-robot interaction,” Applied Sciences (Switzerland), vol. 11, no. 9, May 2021, doi: 10.3390/app11093907.
  • [22] C. Deuerlein, M. Langer, J. Seßner, P. Heß, and J. Franke, “Human-robot-interaction using cloud-based speech recognition systems,” in Procedia CIRP, Elsevier B.V., 2020, pp. 130–135. doi: 10.1016/j.procir.2020.05.214.
  • [23] J. Chimento and R. Secoli, “Kinematic Calibration of a Seven Revolute Joints Serial Manipulator,” Master Degree, Polytechnic University, London, 2019.
  • [24] M. Safeea, P. Neto, and R. Bearee, “Precise hand-guiding of redundant manipulators with null space control for in-contact obstacle navigation,” in IEEE Industrial Electronics Society, M. Safeea, P. Neto, and R. Bearee, Eds., Lisbon, Portugal: IEEE Industrial Electronics Society, Oct. 2019, pp. 14–17.
  • [25] M. Safeea, P. Neto, and R. Béarée, “Task execution combined with in-contact obstacle navigation by exploiting torque feedback of sensitive robots,” in Procedia Manufacturing, Elsevier B.V., 2020, pp. 187–192. doi: 10.1016/j.promfg.2020.10.027.
  • [26] S. J. Martinez, J. G. Guarnizo, and J. Avendano, “Design and implementation of a remote virtual laboratory by internet applied to kuka lbr iiwa 14 r820,” in Lecture Notes in Electrical Engineering, Springer, 2021, pp. 442–452. doi: 10.1007/978-3-030-53021-1_45.
  • [27] T. Xu et al., “Dynamic Identification of the KUKA LBR iiwa Robot with Retrieval of Physical Parameters Using Global Optimization,” IEEE Access, vol. 8, pp. 108018–108031, 2020, doi: 10.1109/ACCESS.2020.3000997.
  • [28] A. Shafikov, A. Sagitov, H. Li, N. Schiefermeier-Mach, and E. Magid, “Robotic palpation modeling for kuka lbr iiwa using gazebo simulator,” in Proceedings of International Conference on Artificial Life and Robotics, ALife Robotics Corporation Ltd, 2020, pp. 436–439. doi: 10.5954/ICAROB.2020.OS18-3.
  • [29] M. Ostanin, R. Yagfarov, and A. Klimchik, “Interactive robots control using mixed reality,” in IFAC-PapersOnLine, Elsevier B.V., Sep. 2019, pp. 695–700. doi: 10.1016/j.ifacol.2019.11.307.
  • [30] M. L. Pinto, “Developing Collaborative Applications Using the Kuka Iiwa - ProQuest,” Master degree , Instituto Politecnico de Leiria, Portugal, 2021. Accessed: Jul. 11, 2023. [Online]. Available: https://www.proquest.com/docview/2724235186?pq-origsite=gscholar&fromopenview=true
  • [31] “System Software KUKA Sunrise.OS 1.16 KUKA Sunrise.Workbench 1.16 Operating and Programming Instructions for System Integrators,” 2019. [Online]. Available: www.kuka.com
  • [32] J. He, G. Yang, Y. Feng, J. Luo, and S. Chen, “A Sensorless Self-calibration Method with Pose Constraint for Collaborative Robot,” IEEE Trans Instrum Meas, pp. 1–1, 2025, doi: 10.1109/TIM.2025.3565047.
  • [33] C. Faria, F. Ferreira, W. Erlhagen, S. Monteiro, and E. Bicho, “Position-based kinematics for 7-DoF serial manipulators with global configuration control, joint limit and singularity avoidance,” Mech Mach Theory, vol. 121, pp. 317–334, Mar. 2018, doi: 10.1016/J.MECHMACHTHEORY.2017.10.025.
  • [34] C. Faria, F. Ferreira, W. Erlhagen, S. Monteiro, and E. Bicho, “Position-based kinematics for 7-DoF serial manipulators with global configuration control, joint limit and singularity avoidance,” Mech Mach Theory, vol. 121, pp. 317–334, Mar. 2018, doi: 10.1016/J.MECHMACHTHEORY.2017.10.025.
  • [35] S. Doliwa, “Calculation of the inverse kinematics for the KUKA LBR iiwa R800 (7 DOF),” Zenodo, Oct. 2020, doi: 10.5281/zenodo.4063575.
  • [36] G. Singh and V. K. Banga, “Kinematics and trajectory planning analysis based on hybrid optimization algorithms for an industrial robotic manipulators,” Soft comput, vol. 26, no. 21, pp. 11339–11372, Nov. 2022, doi: 10.1007/s00500-022-07423-y.
  • [37] Z. Fu, E. Spyrakos-Papastavridis, Y. H. Lin, and J. S. Dai, “Analytical Expressions of Serial Manipulator Jacobians and their High-Order Derivatives based on Lie Theory∗,” in Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., May 2020, pp. 7095–7100. doi: 10.1109/ICRA40945.2020.9197131.
  • [38] C. Snipes et al., “Efficacy and Safety of a First-in-Class Investigational Prescription Digital Therapeutic for Episodic Migraine (CT-132): Phase 3 Double-Blind, Randomized, Controlled Trial (P4-12.006),” Neurology, vol. 104, no. 7_Supplement_1, Apr. 2025, doi: 10.1212/WNL.0000000000210426.

Design and Implementation of a UFP Interface for Controlling KUKA LBR iiwa Robot Via Real-Time Hand Guiding

Year 2026, Volume: 15 Issue: 2, 127 - 138, 29.01.2026

Abstract

This paper presents a comprehensive study focused on the design and implementation of a User-Friendly Platform (UFP) interface tailored for real-time control of the KUKA LBR iiwa 7 R800 robot. The aim of the study is to enhance the interaction and collaboration between humans and robots in industrial settings, particularly in manual assembly tasks. The research emphasizes simplifying complex programming, ensuring safety, optimizing trajectory, enhancing accuracy, and facilitating seamless system integration. A particular focus is placed on the accessibility of the interface, enabling non-programmers to interact intuitively with the system, overcoming limitations associated with traditional robot programming methods. The developed interface, implemented through Java for the smartPAD control panel via Sunrise Workbench software, provides a touch-based, user-friendly solution for real-time control and monitoring of robot movements. This study utilizes a comprehensive approach to qualitative data collection and analysis, which directly informs the interface design and functionality. The research underscores the significance of this advancement in promoting efficient and safe industrial operations, with particular attention to human-robot collaboration, safety mechanisms, and ease of use. Future research directions are also outlined, emphasizing the potential for broader industrial adoption and integration with emerging technologies such as IoT and Industry 4.0.

Ethical Statement

Ethical considerations are paramount throughout the research, with a strong emphasis on prioritizing the safety of human participants and ensuring the secure operation of the robot during the testing and implementation phases. Personal or sensitive data is not involved in the research process.

Supporting Institution

Bochum University of Applied Sciences

Project Number

2

Thanks

So thankful for the European Tempus-JIM2l project and Bochum university of applied science

References

  • [1] Y. R. Stürz, L. M. Affolter, and R. S. Smith, “Parameter Identification of the KUKA LBR iiwa Robot Including Constraints on Physical Feasibility,” in IFAC-PapersOnLine, Elsevier B.V., Jul. 2017, pp. 6863–6868. doi: 10.1016/j.ifacol.2017.08.1208.
  • [2] “LBR iiwa | KUKA AG.” Accessed: Jul. 16, 2023. [Online]. Available: https://www.kuka.com/en-de/products/robot-systems/industrial-robots/lbr-iiwa
  • [3] D. Calandra, A. Cannavò, and F. Lamberti, “Improving AR-powered remote assistance: a new approach aimed to foster operator’s autonomy and optimize the use of skilled resources,” International Journal of Advanced Manufacturing Technology, vol. 114, no. 9–10, pp. 3147–3164, Jun. 2021, doi: 10.1007/s00170-021-06871-4.
  • [4] T. Rossmeissl, E. Groß, L. Zarco, T. Schlegel, J. Siegert, and T. Bauernhansl, “Approach for extending evaluation criteria for scalable and modular industrial robots,” in Procedia CIRP, Elsevier B.V., 2019, pp. 1022–1027. doi: 10.1016/j.procir.2019.03.245.
  • [5] A. Hietanen, R. Pieters, M. Lanz, J. Latokartano, and J. K. Kämäräinen, “AR-based interaction for human-robot collaborative manufacturing,” Robot Comput Integr Manuf, vol. 63, Jun. 2020, doi: 10.1016/j.rcim.2019.101891.
  • [6] S. Hjorth and D. Chrysostomou, “Human–robot collaboration in industrial environments: A literature review on non-destructive disassembly,” Feb. 01, 2022, Elsevier Ltd. doi: 10.1016/j.rcim.2021.102208.
  • [7] F. Semeraro, A. Griffiths, and A. Cangelosi, “Human–robot collaboration and machine learning: A systematic review of recent research,” Feb. 01, 2022, Elsevier Ltd. doi: 10.1016/j.rcim.2022.102432.
  • [8] M. Vistein, J. Faber, C. Schmidt-Eisenlohr, and D. Reiter, “Automated handling of auxiliary materials using a multi-kinematic gripping system,” in Procedia Manufacturing, Elsevier B.V., 2019, pp. 1276–1283. doi: 10.1016/j.promfg.2020.01.220.
  • [9] K. Chen, J. Wen, J. Wu, and Z. Ji, “Automated robot-based large-scale 3D surface imaging,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 2949–2958. doi: 10.1016/j.procs.2020.09.208.
  • [10] F. Müller, C. Deuerlein, and M. Koch, “Cyber-physical-system for representing a robot end effector,” in Procedia CIRP, Elsevier B.V., 2021, pp. 307–312. doi: 10.1016/j.procir.2021.05.071.
  • [11] M. Ikeda, N. Chitturi, M. Ganglbauer, and A. Pichler, “Knowledge based accuracy improvement in programming by demonstration of point based processes,” in Procedia Manufacturing, Elsevier B.V., 2021, pp. 16–23. doi: 10.1016/j.promfg.2021.10.004.
  • [12] V. V. Nair, D. Kuhn, and V. Hummel, “Development of an easy teaching and simulation solution for an autonomous mobile robot system,” in Procedia Manufacturing, Elsevier B.V., 2019, pp. 270–276. doi: 10.1016/j.promfg.2019.03.043.
  • [13] D. Calandra, F. G. Pratticò, A. Cannavò, C. Casetti, and F. Lamberti, “Digital twin- and extended reality-based telepresence for collaborative robot programming in the 6G perspective,” Digital Communications and Networks, Oct. 2022, doi: 10.1016/j.dcan.2022.10.007.
  • [14] H. Zhong, P. Li, L. Gao, and X. Li, “Low-delay admittance control of hydraulic series elastic actuator for safe human-robot collaboration,” in Procedia Manufacturing, Elsevier B.V., 2020, pp. 147–153. doi: 10.1016/j.promfg.2020.05.031.
  • [15] M. N. Vu, F. Beck, M. Schwegel, C. Hartl-Nesic, A. Nguyen, and A. Kugi, “Machine learning-based framework for optimally solving the analytical inverse kinematics for redundant manipulators,” Mechatronics, vol. 91, p. 102970, May 2023, doi: 10.1016/j.mechatronics.2023.102970.
  • [16] M. Safeea, P. Neto, and R. Béarée, “Model-based hardware in the loop control of collaborative robots,” in Procedia Manufacturing, Elsevier B.V., 2020, pp. 133–139. doi: 10.1016/j.promfg.2020.10.020.
  • [17] T. Weingartshofer, B. Bischof, M. Meiringer, C. Hartl-Nesic, and A. Kugi, “Optimization-based path planning framework for industrial manufacturing processes with complex continuous paths,” Robot Comput Integr Manuf, vol. 82, Aug. 2023, doi: 10.1016/j.rcim.2022.102516.
  • [18] M. Linsinger, J. Stecken, J. Kutschinski, and B. Kuhlenkötter, “Situational task change of lightweight robots in hybrid assembly systems,” in Procedia CIRP, Elsevier B.V., 2019, pp. 81–86. doi: 10.1016/j.procir.2019.03.015.
  • [19] P. Gümbel, X. He, and K. Dröder, “Precision optimized pose and trajectory planning for vertically articulated robot arms,” in Procedia CIRP, Elsevier B.V., 2022, pp. 185–190. doi: 10.1016/j.procir.2022.02.176.
  • [20] Y. Bulut and E. S. Conkur, “A real-time path-planning algorithm with extremely tight maneuvering capabilities for hyper-redundant manipulators,” Engineering Science and Technology, an International Journal, vol. 24, no. 1, pp. 247–258, Feb. 2021, doi: 10.1016/j.jestch.2020.07.002.
  • [21] H. Maithani, J. A. C. Ramon, L. Lequievre, Y. Mezouar, and M. Alric, “Exoscarne: Assistive strategies for an industrial meat cutting system based on physical human-robot interaction,” Applied Sciences (Switzerland), vol. 11, no. 9, May 2021, doi: 10.3390/app11093907.
  • [22] C. Deuerlein, M. Langer, J. Seßner, P. Heß, and J. Franke, “Human-robot-interaction using cloud-based speech recognition systems,” in Procedia CIRP, Elsevier B.V., 2020, pp. 130–135. doi: 10.1016/j.procir.2020.05.214.
  • [23] J. Chimento and R. Secoli, “Kinematic Calibration of a Seven Revolute Joints Serial Manipulator,” Master Degree, Polytechnic University, London, 2019.
  • [24] M. Safeea, P. Neto, and R. Bearee, “Precise hand-guiding of redundant manipulators with null space control for in-contact obstacle navigation,” in IEEE Industrial Electronics Society, M. Safeea, P. Neto, and R. Bearee, Eds., Lisbon, Portugal: IEEE Industrial Electronics Society, Oct. 2019, pp. 14–17.
  • [25] M. Safeea, P. Neto, and R. Béarée, “Task execution combined with in-contact obstacle navigation by exploiting torque feedback of sensitive robots,” in Procedia Manufacturing, Elsevier B.V., 2020, pp. 187–192. doi: 10.1016/j.promfg.2020.10.027.
  • [26] S. J. Martinez, J. G. Guarnizo, and J. Avendano, “Design and implementation of a remote virtual laboratory by internet applied to kuka lbr iiwa 14 r820,” in Lecture Notes in Electrical Engineering, Springer, 2021, pp. 442–452. doi: 10.1007/978-3-030-53021-1_45.
  • [27] T. Xu et al., “Dynamic Identification of the KUKA LBR iiwa Robot with Retrieval of Physical Parameters Using Global Optimization,” IEEE Access, vol. 8, pp. 108018–108031, 2020, doi: 10.1109/ACCESS.2020.3000997.
  • [28] A. Shafikov, A. Sagitov, H. Li, N. Schiefermeier-Mach, and E. Magid, “Robotic palpation modeling for kuka lbr iiwa using gazebo simulator,” in Proceedings of International Conference on Artificial Life and Robotics, ALife Robotics Corporation Ltd, 2020, pp. 436–439. doi: 10.5954/ICAROB.2020.OS18-3.
  • [29] M. Ostanin, R. Yagfarov, and A. Klimchik, “Interactive robots control using mixed reality,” in IFAC-PapersOnLine, Elsevier B.V., Sep. 2019, pp. 695–700. doi: 10.1016/j.ifacol.2019.11.307.
  • [30] M. L. Pinto, “Developing Collaborative Applications Using the Kuka Iiwa - ProQuest,” Master degree , Instituto Politecnico de Leiria, Portugal, 2021. Accessed: Jul. 11, 2023. [Online]. Available: https://www.proquest.com/docview/2724235186?pq-origsite=gscholar&fromopenview=true
  • [31] “System Software KUKA Sunrise.OS 1.16 KUKA Sunrise.Workbench 1.16 Operating and Programming Instructions for System Integrators,” 2019. [Online]. Available: www.kuka.com
  • [32] J. He, G. Yang, Y. Feng, J. Luo, and S. Chen, “A Sensorless Self-calibration Method with Pose Constraint for Collaborative Robot,” IEEE Trans Instrum Meas, pp. 1–1, 2025, doi: 10.1109/TIM.2025.3565047.
  • [33] C. Faria, F. Ferreira, W. Erlhagen, S. Monteiro, and E. Bicho, “Position-based kinematics for 7-DoF serial manipulators with global configuration control, joint limit and singularity avoidance,” Mech Mach Theory, vol. 121, pp. 317–334, Mar. 2018, doi: 10.1016/J.MECHMACHTHEORY.2017.10.025.
  • [34] C. Faria, F. Ferreira, W. Erlhagen, S. Monteiro, and E. Bicho, “Position-based kinematics for 7-DoF serial manipulators with global configuration control, joint limit and singularity avoidance,” Mech Mach Theory, vol. 121, pp. 317–334, Mar. 2018, doi: 10.1016/J.MECHMACHTHEORY.2017.10.025.
  • [35] S. Doliwa, “Calculation of the inverse kinematics for the KUKA LBR iiwa R800 (7 DOF),” Zenodo, Oct. 2020, doi: 10.5281/zenodo.4063575.
  • [36] G. Singh and V. K. Banga, “Kinematics and trajectory planning analysis based on hybrid optimization algorithms for an industrial robotic manipulators,” Soft comput, vol. 26, no. 21, pp. 11339–11372, Nov. 2022, doi: 10.1007/s00500-022-07423-y.
  • [37] Z. Fu, E. Spyrakos-Papastavridis, Y. H. Lin, and J. S. Dai, “Analytical Expressions of Serial Manipulator Jacobians and their High-Order Derivatives based on Lie Theory∗,” in Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., May 2020, pp. 7095–7100. doi: 10.1109/ICRA40945.2020.9197131.
  • [38] C. Snipes et al., “Efficacy and Safety of a First-in-Class Investigational Prescription Digital Therapeutic for Episodic Migraine (CT-132): Phase 3 Double-Blind, Randomized, Controlled Trial (P4-12.006),” Neurology, vol. 104, no. 7_Supplement_1, Apr. 2025, doi: 10.1212/WNL.0000000000210426.
There are 38 citations in total.

Details

Primary Language English
Subjects Manufacturing and Industrial Engineering (Other)
Journal Section Research Article
Authors

Omar Shehata 0009-0000-6628-3574

Mohammad Baniyounis This is me

Rolf Biesenbach This is me 0000-0002-8638-6252

Jan Falkenhain This is me 0009-0009-9183-3641

Project Number 2
Submission Date September 20, 2024
Acceptance Date June 17, 2025
Publication Date January 29, 2026
Published in Issue Year 2026 Volume: 15 Issue: 2

Cite

APA Shehata, O., Baniyounis, M., Biesenbach, R., Falkenhain, J. (2026). Design and Implementation of a UFP Interface for Controlling KUKA LBR iiwa Robot Via Real-Time Hand Guiding. European Journal of Technique (EJT), 15(2), 127-138. https://doi.org/10.36222/ejt.1551931

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