Skip to main content
×
×
Home

searchUCSG: a fast coalition structure search algorithm for modular robot reconfiguration under uncertainty

  • Ayan Dutta (a1), Prithviraj Dasgupta (a1), José Baca (a1) and Carl Nelson (a2)
Summary

We consider the problem of dynamic reconfiguration by modular self-reconfigurable robots (MSRs) in the presence of uncertainty in their motion and the environment. Specifically, we consider the situation where the MSR is unable to continue its motion in its current configuration and needs to identify a new configuration among the existing modules, which would be the most configuration suitable for performing the robot's assigned task under the current circumstances. To address this problem, we propose a new data structure called an uncertain coalition structure graph (UCSG) that accommodates uncertainty in the MSR's motion and the environment, using a framework from cooperative game theory called the coalition structure graph. We then propose a new search algorithm called searchUCSG that intelligently prunes nodes from the UCSG using a modified branch-and-bound technique. We have shown analytically that our algorithm is anytime, that is, if it terminates arbitrarily, it returns the best solution found thus far, which is guaranteed to be within a constant bound from the optimal solution. We have verified the performance of our algorithm experimentally in simulation and shown that it is able to find a solution that is within the worst bound of 80% of the optimal solution while exploring only half of the nodes in the UCSG. Our algorithm also takes lesser computation time than the existing algorithms (that do not model uncertainty) for solving similar problems. Finally, to verify the operation of our algorithm, we have implemented it to partition a set of mobile e-puck robots into clusters and shown how different number of robots and different robot motion uncertainty parameters affect the formed clusters.

Copyright
Corresponding author
*Corresponding author. E-mail: adutta@unomaha.edu, duttaayan.cs@gmail.com
References
Hide All
1.Butler, Z., Brynes, S. and Rus, D., “Distributed Motion Planning for Modular Robots with Unit Decompressable Modules,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2001) pp. 790–796.
2.Chirikjian, G., Pamecha, A. and Ebert-Uphoff, I., “Evaluating efficiency of self-reconfiguration in a class of modular robots,” Robot. Syst. 13, 317–338 (1996).
3.Chu, K., Hossain, S. G. M. and Nelson, C., “Design of a Four-DOF Modular Self-Reconfigurable Robot with Novel Gaits,” ASME International Design Enggineering Technical Conferences (DETC2011-47746) (2011).
4.Dasgupta, P., Ufimtsev, V., Nelson, C. and Mamur, S. M. G., “Dynamic Reconfiguration in Modular Robots Using Graph Partitioning-Based Coalitions,” International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (2012) pp. 121–128.
5.Evans, J., Optimization Algorithms for Networks and Graphs, Vol. 1 (CRC Press, Boca Raton, FL1992).
6.Hossain, S. M. G., Nelson, C., Dasgupta, P., “RoGenSiD A Rotary-Plate Genderless Single-Sided Docking Mechanism for Modular Self-Reconfigurable Robots, ASME 2013 International Design Engineering Technical Conferences (ASME IDETC) (Aug. 4–7, 2013).
7.Kamimura, A., Yoshida, E., Murata, S., Hurokawa, H., Tomita, K. and Kokaji, S., “Distributed Self-Reconfiguration of M-TRAN III Modular Robotic System,” Intl. J. Robot. 27 (1), 373386 (2008).
8.Meier, P., “Variance of a weighted mean,” Biometrics 9 (1), 5973 (1953).
9.Myerson, R., Game Theory: Analysis of Conflict (Harvard University Press, Cambridge, MA, 1997).
10.Narendra, M. and Fukunaga, K., “A branch and bound algorithm for feature subset selection,” IEEE Trans. Comput. 100 (9), 917922 (1977).
11.Rahwan, T. and Jennings, N. “An Improved Dynamic Programming Algorithm for Coalition Structure Generation,” Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, Vol. 3 (2008) pp. 1417–1420.
12.Rahwan, T., Ramchurn, S., Jennings, N. and Giovannucci, A., “An anytime algorithm for optimal coalition structure generation,” J. Artif. Intell. Res. (JAIR) 34, 521567 (2009).
13.Ramaekers, Z., Dasgupta, P., Ufimtsev, V., Hossain, S. G. M. and Nelson, C., “Self-Reconfiguration in Modular Robots Using Coalition Games with Uncertainty,” Proceedings of the Automated Action Planning for Autonomous Mobile Robots Workshop (2011).
14.Ramchurn, S., Polukarov, M., Farinelli, A., Truong, C. and Jennings, N., “Coalition Formation with Spatial and Temporal Constraints,” Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (2010) pp. 1181–1188.
15.Rosa, M., Goldstein, S., Lee, P., Campbell, J. and Pillai, P., “Scalable Shape Sculpturing via Hole Motions,” Proceedings of 2006 IEEE International Conference on Robotics and Automation, Orlando, FL (2006) pp. 14621468.
16.Rothkopf, M., Pekeč, A. and Harstad, R., “Computationally manageable combinational auctions,” Manage. Sci. 44 (8), 11311147 (1998).
17.Russell, S. and Norvig, P., Artificial Intelligence: A Modern Approach (MIT Press, Cambridge, MA, 2008).
18.Sandholm, T., Larson, K., Andersson, M., Shehory, O. and Tohme, F., “Coalition structure generation with worst case guarantees,” Artif. Intell. 111 (1–2), 209238 (1999).
19.Sen, S. and Dutta, P. S., “Searching for Optimal Coalition Structures,” Proceedings of Fourth International Conference on Multi-Agent Systems (2000) pp. 287–292.
20.Shehory, O. and Kraus, S., “Methods for task allocation via agent coalition formation,” Artif. Intell. 101 (1), 165200 (1998) (Elsevier).
21.Stoy, K., Brandt, D. and Christensen, D., Self-Reconfigurable Robots: An Introduction (MIT Press, Cambridge, MA, 2010).
22.Winter, D., Biomechanics and Motor Control of Human Gait: Normal, Elderly and Pathological (Waterloo Biomechanics, Ontario Canada, 1991). ISBN 0-88898-105-8.
23.Yim, M.et al., “Modular self-reconfigurable robot systems: Challenges and opportunities for the future,” IEEE Robot. Autom. Mag. 14 (1), 4353 (2007).
24.Zilberstein, S., “Using Anytime Algorithms in Intelligent Systems,” AI Mag. 17 (3), 73 (1996).
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Robotica
  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 19 *
Loading metrics...

Abstract views

Total abstract views: 188 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 22nd April 2018. This data will be updated every 24 hours.