The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Feasibility Prediction-Based Obstacle Removal Planning and Contactable Disinfection Robot System for Surface Disinfection in an Untidy Environment

비정돈 환경의 표면 소독을 위한 실현성 예측 기반의 장애물 제거 계획법 및 접촉식 방역 로봇 시스템

  • Received : 2021.05.20
  • Accepted : 2021.06.07
  • Published : 2021.08.31
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Abstract

We propose a task and motion planning algorithm for clearing obstacles and wiping surfaces, which is essential for surface disinfection during the pathogen disinfection process. The proposed task and motion planning algorithm determines task parameters such as grasping pose and placement location during the planning process without using pre-specified or discretized values. Furthermore, to quickly inspect many unit motions, we propose a motion feasibility prediction algorithm consisting of collision checking and an SVM model for inverse mechanics and self-collision prediction. Planning time analysis shows that the feasibility prediction algorithm can significantly increase the planning speed and success rates in situations with multiple obstacles. Finally, we implemented a hierarchical control scheme to enable wiping motion while following a planner-generated joint trajectory. We verified our planning and control framework by conducted an obstacle-clearing and surface wiping experiment in a simulated disinfection environment.

Keywords

Acknowledgement

This research was supported by Korea Advanced Research Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (2020M3H8A1114905)

References

  1. D. B. Lee, "Post-Corona Era: The Present and Future of Antisaphtic Robots," Convergence Research Policy Center, Seoul, Korea, [Online], https://crpc.kist.re.kr/user/nd49151.do?View&boardNo=00007243.
  2. "Coronavirus-19 Response Group Facility, Multi-Use Facility Disinfection Guide", KDCA, Sejong, Korea, [Online], http://ncov.mohw.go.kr/shBoardView.do?brdId=2&brdGubun=25&ncvContSeq=3411.
  3. "Cleaning and disinfection of environmental surfaces in the context of COVID-19", World Health Organization, Geneva, Swiss, [Online]. https://www.who.int/publications/i/item/cleaningand-disinfection-of-environmental-surfaces-inthe-context-of-covid-19.
  4. L. P. Kaelbling and T. Lozano-Perez, "Hierarchical task and motion planning in the now," 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, pp. 1470-1477, 2011, DOI: 10.1109/ICRA.2011.5980391.
  5. S. Srivastava, E. Fang, L. Riano, R. Chitnis, S. Russell, and P. Abbeel, "Combined task and motion planning through an extensible planner-independent interface layer," 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, pp. 639-646, 2014, DOI: 10.1109/ICRA.2014.6906922.
  6. N. T. Dantam, Z. K. Kingston, S. Chaudhuri, and L. E. Kavraki, "An incremental constraint-based framework for task and motion planning," The International Journal of Robotics Research, vol. 37, no. 10, pp. 1134-1151, 2018, DOI: 10.1177/0278364918761570.
  7. P. S. Schmitt, F. Wirnshofer, K. M. Wurm, G. von Wichert, and W. Burgard, "Planning reactive manipulation in dynamic environments," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, pp. 136-143, 2019, DOI: 10.1109/IROS40897.2019.8968452.
  8. D. Driess, J.-S. Ha, and M. Toussaint, "Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image," arXiv preprint arXiv:2006.05398, 2020, [Online], https://arxiv.org/abs/2006. 05398.
  9. A. M. Wells, N. T. Dantam, A. Shrivastava, and L. E. Kavraki, "Learning feasibility for task and motion planning in tabletop environments," IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1255-1262, 2019, DOI: 10.1109/LRA.2019. 2894861.
  10. D. Driess, O. Oguz, J.-S. Ha, and M. Toussaint, "Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning," 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, pp. 9563-9569, 2020, DOI: 10.1109/ICRA40945. 2020.9197291.
  11. J. Mirabel and F. Lamiraux, "Manipulation planning: addressing the crossed foliation issue," 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, pp. 4032-4037, 2017, DOI: 10.1109/ICRA.2017.7989462.
  12. K. Hauser and V. Ng-Thow-Hing, "Randomized multi-modal motion planning for a humanoid robot manipulation task," The International Journal of Robotics Research, vol. 30, no. 6, pp. 678-698, 2011, DOI: 10.1177/0278364910386 985.
  13. J. J. Kuffner and S. M. LaValle, "RRT-connect: An efficient approach to single-query path planning," 2000 IEEE International Conference on Robotics and Automation, San Francisco, USA, pp. 995-1001, 2000, DOI: 10.1109/ ROBOT.2000.844730.
  14. Z. Kingston, M. Moll, and L. E. Kavraki, "Exploring implicit spaces for constrained sampling-based planning," The International Journal of Robotics Research, vol. 38, no.10-11, pp. 1151-1178, 2019, DOI: 10.1177/02783649198 68530.
  15. I. Rodriguez, K. Nottensteiner, D. Leidner, M. KaBecker, F. Stulp, and A. Albu-Schaffer, "Iteratively refined feasibility checks in robotic assembly sequence planning," IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1416-1423, 2019, DOI: 10.1109/LRA.2019.2895845.
  16. E. G. Gilbert, D. W. Johnson, and S. S. Keerthi, "A fast procedure for computing the distance between complex objects in three-dimensional space," IEEE Journal on Robotics and Automation, vol. 4, no. 2, pp.193-203, 1988, DOI: 10.1109/56.2083.
  17. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011, [Online], https://www.jmlr.org/papers/v12/pedregosa11a.html.
  18. M. J. Kim, Y. Choi, and W. K. Chung, "Bringing Nonlinear H∞ Optimality to Robot Controllers," IEEE Transactions on Robotics, vol. 31, no. 3, pp. 682-698, 2015, DOI: 10.1109/TRO.2015.2419871.
  19. A. Dietrich, C. Ott, and A. Albu-Schaffer, "Multi-objective compliance control of redundant manipulators: Hierarchy, control, and stability," 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, pp. 3043-3050, 2013, DOI: 10.1109/IROS.2013.6696787.
  20. I. A. Sucan, M. Moll, and L. E. Kavraki, "The open motion planning library," IEEE Robotics & Automation Magazine, vol. 19, no. 4, pp. 72-82, 2012, DOI: 10.1109/MRA.2012.2205651.
  21. S. Garrido-Jurado, R. Munoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marin-Jimenez, "Automatic generation and detection of highly reliable fiducial markers under occlusion," Pattern Recognition, vol. 47, no. 6, pp. 2280-2292, 2014, DOI: 10.1016/j.patcog.2014.01.005.