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DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking (2403.16786v1)

Published 25 Mar 2024 in cs.RO, cs.SY, and eess.SY

Abstract: Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demanding static objects and fixed collisions, lead to deployment limitations, operational inefficiencies, and process unreliability. This paper introduces a Dynamic Bin-Picking Framework (DBPF) that challenges traditional static assumptions. The DBPF endows the robot with the reactivity to pick multiple moving arbitrary objects while avoiding dynamic obstacles, such as the moving bin. Combined with scene-level pose generation, the proposed pose selection metric leverages the Tendency-Aware Manipulability Network optimizing suction pose determination. Heuristic task-specific designs like velocity-matching, dynamic obstacle avoidance, and the resight policy, enhance the picking success rate and reliability. Empirical experiments demonstrate the importance of these components. Our method achieves an average 84% success rate, surpassing the 60% of the most comparable baseline, crucially, with zero collisions. Further evaluations under diverse dynamic scenarios showcase DBPF's robust performance in dynamic bin-picking. Results suggest that our framework offers a promising solution for efficient and reliable robotic bin-picking under dynamics.

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References (31)
  1. S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International journal of robotics research, vol. 37, no. 4-5, pp. 421–436, 2018.
  2. Z. Dong, S. Liu, T. Zhou, H. Cheng, L. Zeng, X. Yu, and H. Liu, “Ppr-net: point-wise pose regression network for instance segmentation and 6d pose estimation in bin-picking scenarios,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 1773–1780.
  3. J. Yang, Y. Gao, D. Li, and S. L. Waslander, “Robi: A multi-view dataset for reflective objects in robotic bin-picking,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 9788–9795.
  4. H. Cao, J. Zhou, J. Huang, Y. Li, N. C. Meng, R. Cao, Q. Dou, and Y. Liu, “Two-stage grasping: A new bin picking framework for small objects,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 2584–2590.
  5. J. Yang, D. Li, and S. L. Waslander, “Probabilistic multi-view fusion of active stereo depth maps for robotic bin-picking,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4472–4479, 2021.
  6. X. Li, R. Cao, Y. Feng, K. Chen, B. Yang, C.-W. Fu, Y. Li, Q. Dou, Y.-H. Liu, and P.-A. Heng, “A sim-to-real object recognition and localization framework for industrial robotic bin picking,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3961–3968, 2022.
  7. X. Zhang, Y. Domae, W. Wan, and K. Harada, “Learning efficient policies for picking entangled wire harnesses: An approach to industrial bin picking,” IEEE Robotics and Automation Letters, vol. 8, no. 1, pp. 73–80, 2022.
  8. Y. Domae, H. Okuda, Y. Taguchi, K. Sumi, and T. Hirai, “Fast graspability evaluation on single depth maps for bin picking with general grippers,” in 2014 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2014, pp. 1997–2004.
  9. J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis—a survey,” IEEE Transactions on robotics, vol. 30, no. 2, pp. 289–309, 2013.
  10. M. Roy, R. A. Boby, S. Chaudhary, S. Chaudhury, S. D. Roy, and S. K. Saha, “Pose estimation of texture-less cylindrical objects in bin picking using sensor fusion,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2016, pp. 2279–2284.
  11. C. De Farias, M. Adjigble, B. Tamadazte, R. Stolkin, and N. Marturi, “Dual quaternion-based visual servoing for grasping moving objects,” in 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE).   IEEE, 2021, pp. 151–158.
  12. I. Akinola, J. Xu, S. Song, and P. K. Allen, “Dynamic grasping with reachability and motion awareness,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 9422–9429.
  13. F. Islam, O. Salzman, A. Agarwal, and M. Likhachev, “Provably constant-time planning and replanning for real-time grasping objects off a conveyor belt,” The International Journal of Robotics Research, vol. 40, no. 12-14, pp. 1370–1384, 2021.
  14. J. Ichnowski, M. Danielczuk, J. Xu, V. Satish, and K. Goldberg, “Gomp: Grasp-optimized motion planning for bin picking,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 5270–5277.
  15. H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” IEEE Transactions on Robotics, 2023.
  16. T. H. Bui, Y. G. Son, S. J. Moon, Q. H. Nguyen, I. Rhee, J. Y. Hong, and H. R. Choi, “Deep learning based 6-dof antipodal grasp planning from point cloud in random bin-picking task using single-view,” IEEE Robotics and Automation Letters, 2023.
  17. J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg, “Learning ambidextrous robot grasping policies,” Science Robotics, vol. 4, no. 26, p. eaau4984, 2019.
  18. D. Buchholz, M. Futterlieb, S. Winkelbach, and F. M. Wahl, “Efficient bin-picking and grasp planning based on depth data,” in 2013 IEEE International Conference on Robotics and Automation.   IEEE, 2013, pp. 3245–3250.
  19. K. Kleeberger, C. Landgraf, and M. F. Huber, “Large-scale 6d object pose estimation dataset for industrial bin-picking,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 2573–2578.
  20. A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. R. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo et al., “Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching,” The International Journal of Robotics Research, vol. 41, no. 7, pp. 690–705, 2022.
  21. B. Burgess-Limerick, C. Lehnert, J. Leitner, and P. Corke, “DGBench: An open-source, reproducible benchmark for dynamic grasping,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 3218–3224.
  22. H. Cao, H.-S. Fang, W. Liu, and C. Lu, “Suctionnet-1billion: A large-scale benchmark for suction grasping,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8718–8725, 2021.
  23. M. Togai, “An application of the singular value decomposition to manipulability and sensitivity of industrial robots,” Siam journal on algebraic discrete methods, vol. 7, no. 2, pp. 315–320, 1986.
  24. N. Vahrenkamp, T. Asfour, G. Metta, G. Sandini, and R. Dillmann, “Manipulability analysis,” in 2012 12th ieee-ras international conference on humanoid robots (humanoids 2012).   IEEE, 2012, pp. 568–573.
  25. R. E. Kalman et al., “A new approach to linear filtering and prediction problems,” Journal of basic Engineering, vol. 82, no. 1, pp. 35–45, 1960.
  26. M. Bhardwaj, B. Sundaralingam, A. Mousavian, N. D. Ratliff, D. Fox, F. Ramos, and B. Boots, “Storm: An integrated framework for fast joint-space model-predictive control for reactive manipulation,” in Conference on Robot Learning.   PMLR, 2022, pp. 750–759.
  27. G. Williams, A. Aldrich, and E. A. Theodorou, “Model predictive path integral control: From theory to parallel computation,” Journal of Guidance, Control, and Dynamics, vol. 40, no. 2, pp. 344–357, 2017.
  28. L. Berscheid and T. Kröger, “Jerk-limited real-time trajectory generation with arbitrary target states,” arXiv preprint arXiv:2105.04830, 2021.
  29. S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez, “Automatic generation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognition, vol. 47, no. 6, pp. 2280–2292, 2014.
  30. J. J. Kuffner and S. M. LaValle, “Rrt-connect: An efficient approach to single-query path planning,” in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), vol. 2.   IEEE, 2000, pp. 995–1001.
  31. L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE transactions on Robotics and Automation, vol. 12, no. 4, pp. 566–580, 1996.
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