Collision Identification of Collaborative Robots Using a Deep Neural Network

딥뉴럴네트워크를 이용한 다관절 로봇의 충돌 판별

  • Received : 2021.02.16
  • Accepted : 2021.04.01
  • Published : 2021.04.30


Human-robot interaction has received a lot of attention as collaborative robots became widely used in many industrial applications. This paper proposes a deep learning method for collision identification of collaborative robots. This method expands the idea of CollisionNet, which was proposed for collision detection, to identify locations of collisions. Collision identification is far more difficult compared to collision detection, because sensor data are highly correlated when collisions occur at close locations. To improve the identification accuracy, this paper proposes an auxiliary loss, which is called consistency loss. This auxiliary loss guides the training of a deep neural network to predict consistent predictions for each single collision event. In experiments, we demonstrate the effectiveness of the proposed method.



  1. M. A. Goodrich, A. C. Schultz, Human-robot interaction: a survey, Now Publishers Inc, 2008.
  2. A. Ajoudani, A. M. Zanchettin, S. Ivaldi, A. Albu-Schaffer, K. Kosuge, O. Khatib, "Progress and Prospects of the Human -robot Collaboration," Autonomous Robots, Vol. 42, No. 5, pp. 957-975, 2018.
  3. K. Berezina, O. Ciftci, C. Cobanoglu, C, Robots, artificial intelligence, and service automation in restaurants. In Robots, artificial intelligence, and service automation in travel, tourism and hospitality. Emerald Publishing Limited, 2019.
  4. G. Wilson, C. Pereyda, N. Raghunath, G. de la Cruz, S. Goel, S. Nesaei, D. J. Cook, "Robot-enabled Support of Daily Activities in Smart Home Environments," Cognitive Systems Research, Vol. 54, pp. 258-272, 2019.
  5. F. Vicentini, "Collaborative Robotics: a Survey. Journal of Mechanical Design," Vol. 143, No. 4, 2021.
  6. S. Zhang, S. Wang, F. Jing, M. Tan, M, "A Sensorless Hand Guiding Scheme Based on Model Identification and Control for Industrial Robot," IEEE Transactions on Industrial Informatics, Vol. 15, No. 9, pp. 5204-5213, 2019.
  7. S. Haddadin, A. De Luca, A. Albu-Schaffer, "Robot Collisions: A Survey on Detection, Isolation, and Identification," IEEE Transactions on Robotics, Vol. 33, No. 6, pp. 1292-1312, 2017.
  8. S. Morikawa, T. Senoo, A. Namiki, M. Ishikawa, "Realtime Collision Avoidance Using a Robot Manipulator with Light-weight Small High-speed Vision Systems," Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 794-799, 2007.
  9. S. Lu, J.H. Chung, S. A. Velinsky, "Human-robot Collision Detection and Identification Based on Wrist and Base Force/torque Sensors," Proceedings of the 2005 IEEE international Conference on Robotics and Automation, pp. 3796-3801, 2005.
  10. S.D, Lee, M.C. Kim, J.B. Song, "Sensorless Collision Detection for Safe Human-robot Collaboration," Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 2392-2397, 2015.
  11. L. Han, W. Xu, B. Li, P. Kang, "Collision Detection and Coordinated Compliance Control for a Dual-arm Robot Without Force/torque Sensing Based on Momentum Observer," IEEE/ASME Transactions on Mechatronics, Vol. 24, No. 5, pp. 2261-2272, 2019.
  12. A. De Luca, A. Albu-Schaffer, S. Haddadin, G. Hirzinger, "Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator arm," Proceedings of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623-1630, 2006.
  13. N. Briquet-Kerestedjian, M., Makarov, M., Grossard, P. Rodriguez-Ayerbe, "Generalized Momentum Based-observer for Robot Impact Detection-Insights and Guidelines Under Characterized Uncertainties," Proceedings of 2017 IEEE Conference on Control Technology and Applications (CCTA), pp. 1282-1287, 2017.
  14. S. Mamedov, S. Mikhel, "Practical Aspects of Model-based Collision Detection. Frontiers in Robotics and AI," Vol. 7, pp. 162, 2020.
  15. S. Jo, W. Kwon, "A Comparative Study on Collision Detection Algorithms based on Joint Torque Sensor Using Machine Learning," The Journal of Korea Robotics Society, Vol. 15, No. 2 , pp. 169-176, 2020 (in Korean).
  16. A. N. Sharkawy, P. N. Koustoumpardis, N. Aspragathos, "Neural Network Design for Manipulator Collision Detection Based only on the Joint Position Sensors," Robotica, Vol. 3, No. 2, pp. 1-19, 2019.
  17. Y.J. Heo, D, Kim, W. Lee, H, Kim, J, Park, W.K. Chung, "Collision Detection for Industrial Collaborative Robots: A Deep Learning Approach," IEEE Robotics and Automation Letters, Vol. 4, No. 2, pp. 740-746, 2019.
  18. T. Ren, Y. Dong, D. Wu, K. Chen, K, "Collision Detection and Identification for Robot Manipulators Based on Extended State Observer," Control Engineering Practice, Vol. 79, pp. 144-153, 2018.
  19. A. Kouris, F. Dimeas, N. Aspragathos, "A Frequency Domain Approach for Contact type Distinction in Human-robot Collaboration," IEEE robotics and automation letters, Vol. 3, No. 2, pp. 720-727, 2018.
  20. M. Geravand, F. Flacco, A. De Luca, "Human-robot Physical Interaction and Collaboration Using an Industrial Robot with a Closed Control Architecture," Proceedings of 2013 IEEE International Conference on Robotics and Automation, pp. 4000-4007, 2013.
  21. Z. Zhang, K. Qian, B. W. Schuller, D. Wollherr, D, "An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory," IEEE Transactions on Automation Science and Engineering, 2020.
  22. F. Min, G. Wang, N. Liu, N, "Collision Detection and Identification on Robot Manipulators Based on Vibration Analysis,". Sensors, Vol. 19, No. 5, pp. 1080, 2019.
  23. S. A. B. Birjandi, J. Kuhn, S. Haddadin, S, "Observer-extended Direct Method for Collision Monitoring in Robot Manipulators Using Proprioception and Imu Sensing," IEEE Robotics and Automation Letters, Vol. 5, No. 2, pp. 954-961, 2020.
  24. Oord, Aaron van den, et al. "Wavenet: A Generative Model for raw Audio." arXiv preprint arXiv:1609.03499 (2016).
  25. Kingma, Diederik P., and Jimmy Ba. "Adam: A Method for Stochastic Optimization." arXiv preprint arXiv:1412.6980 (2014).