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Safe manipulation in unknown, crowded environments via sensor-based interleaving planner: interleaving software and sensitive skin hardware

Published online by Cambridge University Press:  11 February 2016

Dugan Um  and
Dongseok Ryu
Dugan Um*
Affiliation:
Texas A&M University - Corpus Christi 6300 Ocean Dr. Unit 5797 Corpus Christi, TX 78412, USA
Dongseok Ryu
Affiliation:
Korea Atomic Energy Research Institute 989-111 Daedeok-daero, Yuseong-gu Daejeon, 305-353, South Korea Email: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

As various robots are anticipated to coexist with humans in the near future, safe manipulation in unknown, cluttered environments becomes an important issue. Manipulation in an unknown environment, however, has been proven to be NP-Hard and the risk of unexpected human--robot collision hampers the dawning of the era of human--robot coexistence. We propose a non-contact-based sensitive skin as a means to provide safe manipulation hardware and interleaving planning between the workspace and the configuration space as software to solve manipulation problems in unknown, crowded environments. Novelty of the paper resides in demonstration of real time and yet complete path planning in an uncertain and crowded environment. To that end, we introduce the framework of the sensor-based interleaving planner (SBIP) whereby search completeness and safe manipulation are both guaranteed in cluttered environments. We study an interleaving mechanism between sensation in a workspace and execution in the corresponding configuration space for real-time planning in uncertain environments, thus the name interleaving planner implies.

Applications of the proposed system include manipulators of a humanoid robot, surgical manipulators, and robotic manipulators working in hazardous and uncertain environments such as underwater, unexplored planets, and unstructured indoor spaces.

Keywords

Sensor-based planningpoint cloudunknown environment motion planningcollision avoidance
Type
Articles
Information
Robotica , Volume 35 , Issue 5 , May 2017 , pp. 1176 - 1191
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Orthey, A. and Stasse, O., “Towards Reactive Whole-Body Motion Planning in Cluttered Environments by Precomputing Feasible Motion Spaces,” Proceedings of 13th IEEE-RAS International Conference on Humanoid Robots, (Humanoids), Atlanta, GA (Oct. 15–17, 2013) pp. 274–279.Google Scholar
2. Hussain, A., Qureshi, I., Qamar, K. F., Islam, S. M. F., Ayaz, Y. and Muhammad, N., “Potential Guided Directional-RRT* for Accelerated Motion Planning in Cluttered Environments,” Proceedings of IEEE International Conference on Mechatronics and Automation, Takamatsu, Japan (Aug. 4–7, 2013) pp. 519–524.Google Scholar
3. Shiller, Z. and Sharma, S., “High Speed On-Line Motion Planning in Cluttered Environments,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Algarve, Portugal (Oct. 7–12, 2012) pp. 596–601.Google Scholar
4. Hornung, A., Bottcher, S., Schlagenhauf, J., Dornhege, C., Hertle, A. and Bennewitz, M., “Mobile Manipulation in Cluttered Environments with Humanoids: Integrated Perception, Task Planning, and Action Execution,” Proceedings of the Prof. of 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids, Madrid, Spain (Nov. 18–20, 2014) pp. 773–778.Google Scholar
5. Kitaev, N., Mordatch, I., Patil, S. and Abbeel, P., “Physics-Based Trajectory Optimization for Grasping in Cluttered Environments,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Washington State Convention Center Seattle, Washington (May 26–30, 2015) pp. 3102–3109.CrossRefGoogle Scholar
6. Ambros-Ingerson, J. A. and Steel, S., “Integrating Planning, Execution and Monitoring,” Proceedings of in AAAI, vol. 88, Saint Paul, Minnesota (Aug. 21–26, 1988) pp. 21–26.Google Scholar
7. Nourbakhsh, I. R., Interleaving Planning and Execution for Autonomous Robots (Kluwer Academic Publishers, Massachusetts, 1997).Google Scholar
8. Kaelbling, L. and Lozano-Perez, T., “Hierarchical Task and Motion Planning in the Now,” Proceedings of Int. Conf. on Robotics and Automation (ICRA), Shanghai, China (May 9–13, 2011) pp. 1470–1477.Google Scholar
9. Silva, C. S., Bernardino, A. M. and Pinto-Ferreira, C., “Interleaving Real-Time Multi-Agent Planning and Execution: An Application,” Proceedings of 6th. Conf. on Tools with Artificial Intelligence, New Orleans, Louisiana (Nov. 1994) pp. 6–9.Google Scholar
10. de Silva, L., Pandey, A. K. and Alami, R., “An Interface for Interleaved Symbolic-Geometric Planning and Backtracking,” Proceedings Int. Conf. Intelligent Robots and Systems (IROS), Tokyo Big Sight, Japan (Nov. 3–8. 2013) pp. 3–7.Google Scholar
11. Jain, A., Killpack, M. D., Edsinger, A. and Kemp, C. C., “Reaching in clutter with whole-arm tactile sensing,” Int. J. Robot. Res. 32 (4), (2013) pp. 458–482.Google Scholar
12. Kavraki, L. and Latombe, J.-C., “Randomized Preprocessing of Configuration Space for Fast Path Planning,” Proceedings Int. Conf. on Robotics & Automation, San Diego (May 1994) pp. 2138–2145.Google Scholar
13. LaValle, S. M. and Kuffner, J. J., “RRT-Connect: An Efficient Approach to Single-Query Path Planning,” Proceedings of the 2000 IEEE International Conference on Robotics & Automation, San Francisco, CA (Apr. 2000) pp. 995–1001.Google Scholar
14. Zhao, C. S., Farooq, M. and Bayoumi, M. M., “Analytical Solution for Configuration Space Obstacle Computation and Representation,” Proceedings IEEE 21st Int. Conf. on Industrial Electronics, Control, and Instrumentation, vol. 2, Piscataway, NJ (Nov. 6–10, 1995) pp. 1278–1283.Google Scholar
15. Yu, Y. and Gupta, K., “C-space entropy: A measure for view planning and exploration for general robot-sensor systems in unknown environments,” Int. J. Robot. Res. 23 (12), 1197–1223 (Dec. 2, 2004).Google Scholar
16. Kieda, K., Tanaka, H., and Zhang, T. Z., “On-line Optimization of Avoidance Ability for Redundant Manipulator,” Proceedings of IEEE/RSJ Int. Conf. on Intelligent Robotics and Systems, Beijing, China (Oct. 9–15, 2006) pp. 592–597.Google Scholar
17. Livingstone, F. R., King, L., Beraldin, J.-A. and Rioux, M., “Development of a Real-Time Laser Scanning System for Object Recognition, Inspection, and Robot Control,” SPIE Proceedings, Telemanipulator Technology and Space Telerobotics, vol. 2057, Boston, Massachusetts, (Sep. 7–10, 1993) pp. 451–461.Google Scholar
18. Ringbeck, T. and Hagebeuker, B., “A 3D Time of Flight Camera for Object Detection,” Optical 3-D Measurement Techniques, ETH Zürich Plenary Session 1: Range Imaging, Jul., 2007.Google Scholar
19. Malzbender, T., Wilburn, B., Gelb, D. and Ambrisco, B., “Surface Enhancement Using Real-Time Photometric Stereo and Reflectance Transformation,” Proceedings of the 17th Eurographics conference on Rendering Techniques, Cyprus (Jun. 26–28, 2006) pp. 245–250.Google Scholar
20. Um, D., Ryu, D. and Kal, M., “Multiple intensity differentiation for 3D surface reconstruction with mono-vision infrared proximity array sensor,” IEEE Sensors J. 11 (12), 3352–3358 (Jun. 2011).CrossRefGoogle Scholar
21. Sergio, G., “Fitting Primitive Shapes to Point Clouds for Robotic Grasping,” KTH Computer Science and Communication, Master of Science Thesis, Stockholm, Sweden (2009).Google Scholar
22. Koenig, S. and Likhachev, M., “D* Lite,” Proceedings of 14th Conf. on Innovative Applications of Artificial Intelligence (AAAI/IAAI), Alberta, Canada (Jul. 28–Aug. 1, 2002) pp. 476–483.Google Scholar
23. Rusu, R., Sucan, I., Gerkey, B., Chitta, S., Beetz, M. and Kavraki, L., “Real-Time Perception-Guided Motion Planning for a Personal Robot,” Proceedings Int. Conf. Intelligent Robots and Systems (IROS), St. Louis, MO (Oct. 11–15, 2009) pp. 4245–4252.Google Scholar
24. Um, D., Stankovic, B., Giles, K., Hammond, T. and Lumelsky, V., “A Modularized Sensitive Skin for Motion Planning in Uncertain Environments,” Proceedings Int. Conf. on Robotics and Automation (ICRA), vol. 1, Belgium, (May 1998) pp. 7–12.Google Scholar
25. Um, D., “How to tackle sensor based manipulator planning problems using model based planners: Conversion and implementation,” Int. J. Robot. Autom. 24 (2), 137–146 (2009).Google Scholar
26. Park, D., Kapusta, A., Hawke, J. and Kemp, C. C., “Interleaving planning and control for efficient haptically-guided reaching in unknown environments,” Proceedings 14th IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), Madrid, Spain (Nov. 18–20, 2014) pp. 809–816.Google Scholar
27. Um, D., “How to tackle sensor based manipulator planning problems using model based planners: Conversion and implementation,” Int. J. Robot. Autom. (Impact Factor: 0.206), 24 (2), 137–146 (2009).Google Scholar