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Offline decoupled path planning approach for effective coordination of multiple robots

Published online by Cambridge University Press:  26 May 2009

Shital S. Chiddarwar
Affiliation:
Manufacturing Engineering Section, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India
N. Ramesh Babu*
Affiliation:
Manufacturing Engineering Section, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India
*
*Corresponding author. Email: nrbabu@iitm.ac.in

Summary

In this paper, a decoupled offline path planning approach for determining the collision-free path of end effectors of multiple robots involved in coordinated manipulation is proposed. The proposed approach for decoupled path planning is a two-phase approach in which the path for coordinated manipulation is generated with a coupled interaction between collision checking and path planning techniques. Collision checking is done by modelling the links and environment of robot using swept sphere volume technique and utilizing minimum distance heuristic for interference check. While determining the path of the end effector of robots involved in coordinated manipulation, the obstacles present in the workspace are considered as static obstacles and the links of the robots are viewed as dynamic obstacles by the other robot. Coordination is done in offline mode by implementing replanning strategy which adopts incremental A* algorithm for searching the collision-free path. The effectiveness of proposed decoupled approach is demonstrated by considering two examples having multiple six degrees of freedom robots operating in 3D work cell environment with certain static obstacles.

Type
Article
Copyright
Copyright © Cambridge University Press 2009

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References

1.van der Berg, J. P. and Overmars, M. H., “Prioritized Motion Planning for Multiple Robots,” Proceedings of IEEE/RSJ International Conference on Robotics and Systems, Edmonton, Canada (2005) pp. 22172222.Google Scholar
2.Aronov, B., de Berg, M., Van der Stappen, A. F., Svestka, P. and Vleugels, J., “Motion planning for multiple robots,” Discr. Comput. Geomet. 22, 505525 (1999).CrossRefGoogle Scholar
3.Sanchez, G. and Latombe, J. C., “Using a PRM Planner to Compare Centralized and Decoupled Planning for Multi-Robot Systems,” Proceedings of IEEE International Conference on Robotics and Automation, Washington, DC, Vol. 2 (2002) pp. 21122119.Google Scholar
4.Latombe, J. C., Robot Motion Planning. (Kluwer academic press, Boston, 1991).CrossRefGoogle Scholar
5.Barraquand, and Latombe, J. C., “A Monte-Carlo Algorithm for Path Planning with Many Degrees Of Freedom,” Proceedings of IEEE International Conference on Robotics and Automation, Cincinnati, OH, Vol. 3, (1990) pp. 17121717.CrossRefGoogle Scholar
6.Warren, C., “Multiple Robot Path Coordination Using Artificial Potential Fields,” Proceedings of IEEE International Conference on Robotics and Automation, Cincinnati, OH (1990) pp. 500505.CrossRefGoogle Scholar
7.Li, T. Y. and Chou, H. C., “Motion Planning for a Crowd of Robots,” Proceedings of IEEE International Conference on Robotics and Automation, Taipei, Taiwan, ROC (2003) pp. 42154221.Google Scholar
8.O'Donnell, P. A. and Lozano-Perez, T., “Deadlock-Free and Collision-Free Coordination of two Robot Manipulators,” Proceedings of IEEE International Conference on Robotics and Automation, Scottdale, AZ (1989) pp. 484489.Google Scholar
9.Hwang, K., Ju, M. and Chen, Y., “Speed alteration strategy for multijoint robots in co-working environment,” IEEE Trans. Indus. Electr. 50 (2), 385393 (2003).CrossRefGoogle Scholar
10.Cheng, X., “Online Collision Free Path Planning for Service and Assembly Tasks by a Two Arm Robot,” Proceedings of IEEE International Conference on Robotics and Automation, Nagoya, Japan (1995) pp. 15231528.Google Scholar
11.Kant, K. G. and Zucker, S. W., “Towards efficient trajectory planning: Path velocity decomposition,” Int. J. Rob. Res. 5, 7289 (1986).CrossRefGoogle Scholar
12.Bennewitz, M., Burgard, W. and Thru, S., “Finding and optimizing solvable priority schemes for decoupled path planning techniques for teams of mobile robots,” Rob. Autonom. Syst. 41 (2), 8999 (2002).CrossRefGoogle Scholar
13.Fraichard, Th., “Trajectory: a planning in a dynamic workspace state time space approach,” Adv. Rob. 13 (1), 7594 (1999).CrossRefGoogle Scholar
14.Todt, E., Raush, G. and Suarez, R., “Analysis and Classification of Multiple Robot Coordination Methods,” Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, CA, Vol. 4, (2000) pp. 31523157.Google Scholar
15.Gottschalk, S., Lin M, M. and Manocha, D., “OBB Tree : A Hierarchical Structure for Rapid Interference Detection,” Proceedings of 23rd International Conference on Computer Graphics and Interactive Techniques, New Orleans, LA (Aug. 4–9, 1996).Google Scholar
16.Ju, M., Liu, J. and Hwang, K., “Ellipsoidal modeling for articulated robot manipulators for interactive motion planning,” Technical Report, TR-IIS-00–008, Academia Sinica (2001).Google Scholar
17.Sammet, H. and Webber, R., “Hierarchical data structures and algorithms for computer graphics,” IEEE Trans. Comp. Graph. Appl. 4 (3), 4668 (1998).Google Scholar
18.Palmer, I. and Grimsdale, R., “Collision detection for animation using sphere trees,” Comp. Graph. Forum 14 (2), 105116 (1995).CrossRefGoogle Scholar
19.Brock, O. and Khatib, O., “Real Time Obstacles Avoidance and Motion Coordination in a Multi-Robot Workcell,” IEEE International Symposium on Assembly and Task planning, Porto, Portugal (1999) pp. 274279.Google Scholar
20.Fares, C. and Hamam, Y., “Collision Detection Between Virtual Objects Using Optimization Techniques,” Information processing: Recent Mathematical Advances in Optimization and Control, Paris (2005).Google Scholar
21.Fares, C. and Hamam, Y., “Collision Detection for Rigid Bodies: A State of the Art Review, Keynote Paper,” Proceedings of 15th International Conference on Computer Graphics and Applications, Novosibirsk Akademgorodok, Russia (Jun. 20–24, 2005).Google Scholar
22.Harden, T., Kapoor, C. and Tesar, D., “Obstacle Avoidance Influence Coefficients for Manipulator Motion Planning,” Proceedings of ASME IDETC/CIE, Long beach, CA (Sep. 24–28 2005) pp. 113.Google Scholar
23.Song, G., Thomas, S. and Amato, N., “A general frame work for PRM motion planning,” Proc. IEEE Int. Conf. on Robotics & Automation, Taipei, Taiwan, (Sep. 14–19, 2003) pp. 44454450.Google Scholar
24.Sanchez, G. and Latombe, J. C., “A Single-Query Bi-Directional Probabilistic Roadmap Planner with Lazy Collision Checking,” Proceedings of International Symposium on Robotics Research, Siena, Italy (2003) pp. 404417.Google Scholar
25.Trovato, K. I. and Dorst, L., “Differential A*,” IEEE Trans. Knowledge Data Eng. 14 (6), 12181229 (2002).CrossRefGoogle Scholar
26.Koenig, S. and Likhachev, M., “Incremental A*,” J. Adv. Neural Info. Process. Syst. 2 (14), 15391546 (2002).Google Scholar
27.Koenig, S., Likhachev, M. and Furcy, D., “Lifelong planning A*,” J. Artific. Intell. 155 (1–2), 93146 (2004).CrossRefGoogle Scholar
28.Koenig, S. and Likhachev, M., “A New Principle for Incremental Heuristic Search: Theoretical Results,” [Poster Abstract] International Conference on Automated Planning and Scheduling (ICAPS), The English Lake District, Cumbria, UK (2006) pp. 402405.Google Scholar
29.Kuffner, J., “Efficient Optimal Search of Uniform Cost Grid and Lattices,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, Vol. 2 (Sep. 28–Oct. 2, 2004) pp. 19461951.Google Scholar
30.LaValle, S., Branicky, M. and Lindemann, S., “On the relationship between classical grid search and probabilistic roadmap,” Int. J. Rob. Res. 23 (7–8), 673692 (2004).CrossRefGoogle Scholar
31.Stentz, A., “The Focussed D* Algorithm for Real-Time Replanning,” Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada (1995).Google Scholar
32.Koenig, S. and Likhachev, M., “D* Lite,” AAAI Conference of Artificial Intelligence (AAAI), Edmonton, Alberta, Canada (2002) pp. 476483.Google Scholar
33.Ferguson, D. and Stentz, A., “The Delayed D* Algorithm for Efficient Path Replanning,” IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005) pp. 20452050.Google Scholar