Falsos positivos y negativos en la prevención de colisiones humano-robot: evaluación en realidad virtual
Barra lateral del artículo
Contenido principal del artículo
Introducción: en la colaboración humano-robot (HRC), detectar con precisión la proximidad del operador es esencial para garantizar la
seguridad sin comprometer la productividad. Sin embargo, los sensores de distancia convencionales enfrentan una disyuntiva entre falsos
positivos (FP), que pueden provocar paradas innecesarias del robot, y falsos negativos (FN), que pueden derivar en colisiones peligrosas.
Objetivos: este estudio tiene como objetivo analizar el equilibrio entre FP y FN en la detección de proximidad en entornos HRC, utilizando
un entorno de realidad virtual (VR). Se busca identificar configuraciones óptimas de sensores que minimicen los FN sin incrementar excesivamente
los FP.
Materiales y Métodos: se diseñó un entorno de simulación en VR en el que se evaluaron distintos parámetros: distancia de detección,
ángulo de apertura del haz del sensor y disposición espacial de los sensores en el robot. Se aplicó un modelo ANOVA y pruebas post-hoc
para determinar el impacto estadístico de cada variable. Adicionalmente, se realizó una segunda prueba con la participación de un usuario
humano, a fin de observar el comportamiento de los sensores en presencia realista de intervención humana.
Resultados: los análisis identificaron configuraciones de sensores que reducen significativamente los FN sin aumentar de forma considerable los FP. La disposición angular y la cobertura espacial de los sensores resultaron ser factores determinantes. Las pruebas con intervención humana revelaron desafíos adicionales relacionados con la variabilidad del movimiento humano y la necesidad de ajustar la sensibilidad del
sistema.
Conclusiones: los resultados ofrecen criterios técnicos clave para la selección y configuración de sensores en aplicaciones colaborativas. Se
proponen estrategias para mitigar FP, incluyendo la integración de tecnologías como visión artificial y sensores de radar de onda milimétrica.
Este trabajo contribuye al diseño de sistemas más seguros y eficientes en la interacción humano-robot en entornos industriales.
- Colaboración Humano-Robot
- Realidad Virtual
- Detección de Colisiones
- Manufactura Colaborativa
(1) Gámez López Manuel de Jesús. Formación profesional y el impacto de la industria 4.0. Revista Tecnológica. 2021;14:51-56. http://www.redicces.org.sv/jspui/bitstream/10972/4419/1/RevistaTecnologica2021_Art11.pdf
(2) Arteaga Félix. La cuarta revolución industrial (4RI): un enfoque de seguridad nacional. Documento de trabajo 12/2018. Real Instituto Elcano, 2018. https://media.realinstitutoelcano.org/wp-content/uploads/2021/10/dt12-2018-arteaga-cuarta-revolucion-industrial-enfoque-seguridad-nacional.pdf
(3) Bragança S, Costa E, Castellucci I, Arezes PM. A brief overview of the use of collab- orative robots in Industry 4.0: Human role and safety. Studies in Systems, Decision and Control. 2019;202:641-650. https://doi.org/10.1007/978-3-030-14730-3_68 DOI: https://doi.org/10.1007/978-3-030-14730-3_68
(4) Zhang Z, Qian K, Schuller BW, Wollherr D. An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory. IEEE Transactions on Automation Science and Engineering. 2021;18(3):1144-1156. https://doi.org/10.1109/TASE.2020.2997094 DOI: https://doi.org/10.1109/TASE.2020.2997094
(5) Dobra Z, Dhir KS. Technology jump in the industry: human-robot cooperation in pro- duction. Industrial Robot: the international journal of robotics research and application. 2020; https://doi.org/10.1108/IR-02-2020-0039 DOI: https://doi.org/10.1108/IR-02-2020-0039
(6) Chen JH, Song KT. Collision-Free Motion Planning for Human-Robot Collaborative Safety under Cartesian Constraint. 2018;4348-4354. https://doi.org/10.1109/ICRA.2018.8460185 DOI: https://doi.org/10.1109/ICRA.2018.8460185
(7) Hu Y, Wang Y, Hu K, Li W. Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing. Journal of Intelligent Manufacturing. 2023;34(2):789- 807. https://bd.univalle.edu.co/scholarly-journals/adaptive-obstacle-avoidance-path-planning/docview/2766897519/se-2?accountid=174776 DOI: https://doi.org/10.1007/s10845-021-01825-9
(8) Faroni M, Beschi M, Pedrocchi N. Safety-Aware Time-Optimal Motion Planning With Uncertain Human State Estimation. IEEE Robotics and Automation Letters. 2022;7(4):12219- 12226. https://doi.org/10.1109/LRA.2022.3211493 DOI: https://doi.org/10.1109/LRA.2022.3211493
(9) Lei Y, Su Z, Cheng C. Virtual reality in human-robot interaction: Challenges and bene- fits. Electronic Research Archive. 2023;31(5): https://doi.org/10.3934/era.2023121 DOI: https://doi.org/10.3934/era.2023121
(10) Tong Z, Hu H, Wu Z, Xie S, Chen G, Zhang S. An Ultrasonic Proximity Sensing Skin for Robot Safety Control by Using Piezoelectric Micromachined Ultrasonic Transducers (PMUTs). IEEE Sensors Journal. 2022;22(18):17351-17361. https://doi.org/10.1109/JSEN.2021.3068487 DOI: https://doi.org/10.1109/JSEN.2021.3068487
(11) Tsuji S, Kohama T. Proximity Skin Sensor Using Time-of-Flight Sensor for Human Col- laborative Robot. IEEE Sensors Journal. 2019;19(14):5859-5864. https://doi.org/10.1109/JSEN.2019.2905848 DOI: https://doi.org/10.1109/JSEN.2019.2905848
(12) Hernández OG, Morell V, Ramón JL, García GJ, Jara CA. RGBD Data Analysis for the Evaluation of Trajectory Planning Algorithms in Human Robot Interaction Environments for Rehabilitation. Lecture Notes in Networks and Systems. 2023;589:361-372. https://doi.org/10.1007/978-3-031-21065-5_30 DOI: https://doi.org/10.1007/978-3-031-21065-5_30
(13) Scimmi LS, Melchiorre M, Troise M, Mauro S, Pastorelli S. A Practical and Effec- tive Layout for a Safe Human-Robot Collaborative Assembly Task. Applied Sciences. 2021;11(4):1763. https://doi.org/10.3390/app11041763 DOI: https://doi.org/10.3390/app11041763
(14) Liu HL, Wang LW. Collision-free human-robot collaboration based on context aware- ness. Robotics and Computer-Integrated Manufacturing. 2021;67:101997. https://doi.org/10.1016/j.rcim.2020.101997 DOI: https://doi.org/10.1016/j.rcim.2020.101997
(15) Amin FM, Rezayati M, Venn HW, Karimpour H. A mixed-perception approach for safe human-robot collaboration in industrial automation. Sensors. 2020;20(21):6347. https://doi.org/10.3390/s20216347 DOI: https://doi.org/10.3390/s20216347
(16) Huang Z, Yang B, Liu C. RDCa-Net: Residual dense channel attention symmetric network for infrared and visible image fusion. Infrared Physics and Technology. 2023;130:104589. https://doi.org/10.1016/j.infrared.2023.104589 DOI: https://doi.org/10.1016/j.infrared.2023.104589
(17) Park J, Sørensen LC, Mathiesen SF, Schlette C. A Digital Twin-based Workspace Moni- toring System for Safe Human-Robot Collaboration. 2022;24-30. https://doi.org/10.1109/ICCMA56665.2022.10011622 DOI: https://doi.org/10.1109/ICCMA56665.2022.10011622
(18) Kianoush S, Savazzi S, Beschi M, Sigg S, Rampa V. A Multisensory Edge-Cloud Plat- form for Opportunistic Radio Sensing in Cobot Environments. IEEE Internet of Things Journal. 2021;8(2):1154-1168. https://doi.org/10.1109/JIOT.2020.3011809 DOI: https://doi.org/10.1109/JIOT.2020.3011809
(19) Mukherjee D, Gupta K, Chang LH, Najjaran H. A Survey of Robot Learning Strate- gies for Human-Robot Collaboration in Industrial Settings. Robotics and Computer- Integrated Manufacturing. 2022;73:102231. https://doi.org/10.1016/j.rcim.2021.102231 DOI: https://doi.org/10.1016/j.rcim.2021.102231
(20) Lanzoni D, Cattaneo A, Vitali A, Regazzoni D, Rizzi C. Markerless Motion Capture and Virtual Reality for Real-Time Ergonomic Analysis of Operators in Workstations with Collaborative Robots: a preliminary study. 2023;1183-1194. https://doi.org/10.1007/978-3-031-15928-2_103 DOI: https://doi.org/10.1007/978-3-031-15928-2_103
(21) Choi SH, Park KB, Roh DH, Lee JY, Mohammed M, Ghasemi Y. An integrated mixed reality system for safety-aware human-robot collaboration using deep learning and digital twin generation. Robotics and Computer-Integrated Manufacturing. 2022;73:102258. https://doi.org/10.1016/j.rcim.2021.102258 DOI: https://doi.org/10.1016/j.rcim.2021.102258
(22) Fonseca D, Safeea M, Neto P. A Flexible Piezoresistive/Self-Capacitive Hybrid Force and Proximity Sensor to Interface Collaborative Robots. IEEE Transactions on Industrial Informatics. 2023;19(3):2485-2495. https://doi.org/10.1109/TII.2022.3174708 DOI: https://doi.org/10.1109/TII.2022.3174708
(23) Wu H, Wang X, Li Y. Placement Optimization of Flexible Proximity Sensors for Human- Robot Collaboration. IEEE Robotics and Automation Letters. 2024;9(6):5591-5598. https://doi.org/10.1109/LRA.2024.3396105 DOI: https://doi.org/10.1109/LRA.2024.3396105
(24) Nguyen TD, Kim T, Noh J, Phung H, Kang G, Choi HR. Skin-Type Proximity Sensor by Using the Change of Electromagnetic Field. IEEE Transactions on Industrial Electronics. 2021;68(3):2379-2388. https://doi.org/10.1109/TIE.2020.2975503 DOI: https://doi.org/10.1109/TIE.2020.2975503
(25) Xing C. Perspective of Proximity Sensors on Robots Using Cross-Thought. Highlights in Science, Engineering and Technology. 2023;38:875-887. https://doi.org/10.54097/hset.v38i.5973 DOI: https://doi.org/10.54097/hset.v38i.5973
(26) Tsuji S, Kohama T. Self-Capacitance Proximity and Tactile Skin Sensor With Shock- Absorbing Structure for a Collaborative Robot. IEEE Sensors Journal. 2020;20(24):15075- 15084. https://doi.org/10.1109/JSEN.2020.3011701 DOI: https://doi.org/10.1109/JSEN.2020.3011701
(27) Malik AA, Masood T, Bilberg A. Virtual reality in manufacturing: immersive and col- laborative artificial-reality in design of human-robot workspace. International Journal of Computer Integrated Manufacturing. 2020;33: https://doi.org/10.1080/0951192X.2019.1690685 DOI: https://doi.org/10.1080/0951192X.2019.1690685
(28) Aschenbrenner D, Tol DH, Rusák Z, Werker C. Using Virtual Reality for scenario-based Responsible Research and Innovation approach for Human Robot Co-production. Pro- ceedings - 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). 2020;146-150. https://doi.org/10.1109/AIVR50618.2020.00033 DOI: https://doi.org/10.1109/AIVR50618.2020.00033
(29) Nenna F, Orso V, Zanardi D, Gamberini L. The virtualization of human-robot interac- tions: a user-centric workload assessment. Virtual Reality. 2022;27(9):1-19. https://doi.org/10.1007/s10055-022-00667-x DOI: https://doi.org/10.1007/s10055-022-00667-x
(30) Havard V, Jeanne B, Lacomblez M, Baudry D. Digital twin and virtual reality: a co- simulation environment for design and assessment of industrial workstations. Production & Manufacturing Research. 2019;7:472-489. https://doi.org/10.1080/21693277.2019.1660283 DOI: https://doi.org/10.1080/21693277.2019.1660283
(31) Xu B, Zhong L, Zhang G, Liang X, Virtue D, Madan R. CushSense: Soft, Stretchable, and Comfortable Tactile-Sensing Skin for Physical Human-Robot Interaction. 2024;5694- 5701. https://doi.org/10.1109/ICRA57147.2024.10610014 DOI: https://doi.org/10.1109/ICRA57147.2024.10610014
(32) Escobedo C, Strong M, West M, Aramburu A, Roncone A. Contact Anticipation for Physical Human-Robot Interaction with Robotic Manipulators using Onboard Proximity Sensors. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2021;7255-7262. https://doi.org/10.1109/IROS51168.2021.9636130 DOI: https://doi.org/10.1109/IROS51168.2021.9636130
(33) Li S, Zheng P, Liu S, Wang Z, Wang XV, Zheng L. Proactive human-robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives. Robotics and Computer- Integrated Manufacturing. 2023;81:1-30. https://doi.org/10.1016/j.rcim.2022.102510 DOI: https://doi.org/10.1016/j.rcim.2022.102510
(34) Phan PT, Chao PC, Cai JJ, Wang YJ, Wang SC, Wong K. A novel 6-DOF force/torque sensor for COBOTs and its calibration method. IEEE International Conference on Ap- plied System Innovation (ICASI). 2018;2018:1228-1231. https://doi.org/10.1109/ICASI.2018.8394511 DOI: https://doi.org/10.1109/ICASI.2018.8394511
(35) Scimmi LS, Melchiorre M, Mauro S, Pastorelli SP. Implementing a Vision-Based Col- lision Avoidance Algorithm on a UR3 Robot. 2019 23rd International Conference on Mechatronics Technology (ICMT). 2019;1-6. https://doi.org/10.1109/ICMECT.2019.8932143
(36) Melchiorre M, Scimmi LS, Pastorelli SP, Mauro S. Collison Avoidance using Point Cloud Data Fusion from Multiple Depth Sensors: A Practical Approach. 2019 23rd In- ternational Conference on Mechatronics Technology (ICMT). 2019;1-6. https://doi.org/10.1109/ICMECT.2019.8932143 DOI: https://doi.org/10.1109/ICMECT.2019.8932143
(37) Safeea M, Neto P, Béarée R. On-line collision avoidance for collaborative robot ma- nipulators by adjusting off-line generated paths: An industrial use case. Robotics and Autonomous Systems. 2019;119:278-288. https://doi.org/10.1016/j.robot.2019.07.013 DOI: https://doi.org/10.1016/j.robot.2019.07.013
(38) Lu S, Xu Z, Wang B. Human-robot Collision Detection Based on the Improved Camshift Algorithm and Bounding Box. International Journal of Control, Automation and Sys- tems. 2022;20(10):3347-3360. https://doi.org/10.1007/s12555-021-0280-0 DOI: https://doi.org/10.1007/s12555-021-0280-0
(39) Süto˝ S, Forgó Z, Tolvaly-Ros¸ca F. Simulation Based Human-robot Co-working. Procedia Engineering. 2017;181:503-508. https://doi.org/10.1016/j.proeng.2017.02.425 DOI: https://doi.org/10.1016/j.proeng.2017.02.425
(40) Ko D, Lee S, Park J. A study on manufacturing facility safety system using multimedia tools for cyber physical systems. Multimedia Tools and Applications. 2021;80(26-27):1- 18. https://doi.org/10.1007/s11042-020-09925-z DOI: https://doi.org/10.1007/s11042-020-09925-z
(41) Shu B, Sziebig G, Pieskä S. Human-robot collaboration: Task sharing through Virtual Reality. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Soci- ety. 2018;6040-6044. https://doi.org/10.1109/IECON.2018.8591102 DOI: https://doi.org/10.1109/IECON.2018.8591102
(42) Perez L, Diez E, Usamentiaga R, Garcia FD. Industrial robot control and operator training using virtual reality interfaces. Computers in Industry. 2019;109:114-120. https://doi.org/10.1016/j.compind.2019.05.001 DOI: https://doi.org/10.1016/j.compind.2019.05.001
(43) Franklin CS, Dominguez EG, Fryman JD, Lewandowski ML. Collaborative robotics: New era of human-robot cooperation in the workplace. Journal of Safety Research. 2020;74: https://doi.org/10.1016/j.jsr.2020.06.013 DOI: https://doi.org/10.1016/j.jsr.2020.06.013
(44) Ding Y, Thomas U. Collision Avoidance with Proximity Servoing for Redundant Serial Robot Manipulators. 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020;10249-10255. https://doi.org/10.1109/ICRA40945.2020.9196759 DOI: https://doi.org/10.1109/ICRA40945.2020.9196759
(45) Rupavatharam S, Fan X, Escobedo C, Lee D, Jackel L, Howard R. AmbiSense: Acoustic Field Based Blindspot-Free Proximity Detection and Bearing Estimation. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2023;5974-5981. https://doi.org/10.1109/IROS55552.2023.10341766 DOI: https://doi.org/10.1109/IROS55552.2023.10341766
(46) Hoffmann A, Poeppel A, Schierl A, Reif W. Environment-aware proximity detection with capacitive sensors for human-robot-interaction. 2016 IEEE/RSJ International Con- ference on Intelligent Robots and Systems (IROS). 2016;145-150. https://doi.org/10.1109/IROS.2016.7759047 DOI: https://doi.org/10.1109/IROS.2016.7759047
(47) Schöffmann C, Ubezio B, Muehlbacher-Karrer S, Zangl H. Radar Sensors in Collabo- rative Robotics: Fast Simulation and Experimental Validation. 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020;10452-10458. https://doi.org/10.1109/ICRA40945.2020.9197180 DOI: https://doi.org/10.1109/ICRA40945.2020.9197180
(48) Fan X, Simmons-Edler R, Lee D, Jackel L, Howard R, Lee D. AuraSense: Robot Colli- sion Avoidance by Full Surface Proximity Detection. 2021 IEEE/RSJ International Con- ference on Intelligent Robots and Systems (IROS). 2021;1763-1770. https://doi.org/10.1109/IROS51168.2021.9635919 DOI: https://doi.org/10.1109/IROS51168.2021.9635919
(49) Fan X, Lee D, Chen Y, Prepscius C, Isler V, Jackel L. Acoustic Collision Detection and Localization for Robot Manipulators. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2020;9529-9536. https://doi.org/10.1109/IROS45743.2020.9341719 DOI: https://doi.org/10.1109/IROS45743.2020.9341719
(50) Schmider E, Ziegler M, Danay E, Beyer L, Buhner M. Is it really robust? Reinvesti- gating the robustness of ANOVA against the normal distribution assumption. Method- ology: European Journal of Research Methods for the Behavioral and Social Sciences. 2010;6(4):147-151. https://doi.org/10.1027/1614-2241/a000016 DOI: https://doi.org/10.1027/1614-2241/a000016
(51) Delacre M, Lakens D, Leys C. Why Psychologists Should by Default Use Welch's t-test Instead of Student's t-test. International Review of Social Psychology. 2017;30:92. https://doi.org/10.5334/irsp.82 DOI: https://doi.org/10.5334/irsp.82
Descargas
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Los autores que publican en esta revista están de acuerdo con los siguientes términos:
Los autores ceden los derechos patrimoniales a la revista y a la Universidad del Valle sobre los manuscritos aceptados, pero podrán hacer los reusos que consideren pertinentes por motivos profesionales, educativos, académicos o científicos, de acuerdo con los términos de la licencia que otorga la revista a todos sus artículos.
Los artículos serán publicados bajo la licencia Creative Commons 4.0 BY-NC-SA (de atribución, no comercial, sin obras derivadas).