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Synthesis and analysis of distributed ensemble control strategies for allocation to multiple tasks

Published online by Cambridge University Press:  02 December 2013

T. William Mather*
Affiliation:
Department of Mechanical Engineering and Mechanics, Drexel University, Philadelphia, PA 19104, USA
M. Ani Hsieh
Affiliation:
Department of Mechanical Engineering and Mechanics, Drexel University, Philadelphia, PA 19104, USA
*
*Corresponding author. E-mail: [email protected]

Summary

We present the synthesis and analysis of distributed ensemble control policies to enable a team of robots to control their distribution across a collection of tasks. We assume that individual robot controllers are modeled as a sequential composition of individual task controllers. A macroscopic description of the team dynamics is then used to synthesize ensemble feedback control strategies that maintain the desired distribution of robots across the tasks. We present a distributed implementation of the ensemble feedback strategy that can be implemented with minimal communication requirements. Different from existing strategies, the approach results in individual robot control policies that maintain the desired mean and the variance of the robot populations at each task. We present the stability properties of the ensemble feedback strategy, verify the feasibility of the distributed ensemble controller through high-fidelity simulations, and examine the robustness of the strategy to sensing and/or actuation failures. Specifically, we consider the case when robots are subject to estimation and navigation errors resulting from lossy inter-agent wireless communication links and localization errors.

Type
Articles
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Berman, S., Halasz, A. M., Hsieh, M. A. and Kumar, V., “Navigation-Based Optimization of Stochastic Strategies for Allocating a Robot Swarm Among Multiple Sites,” In: The Proceedings of 2008 IEEE Conference on Decision & Control (CDC'08), Cancun, Mexico (2008) pp. 43764381.Google Scholar
2.Correll, N. and Martinoli, A., “Multirobot inspection of industrial machinery,” IEEE Robot. Autom. Mag. 16 (1), 103112 (Mar. 2009).CrossRefGoogle Scholar
3.Cortes, J., Martinez, S., Karatas, T. and Bullo, F., “Coverage Control for Mobile Sensing Networks,” In: The Proceeding of IEEE International Conference on Robotics and Automation (ICRA'02), Washington, DC (2002) pp. 13271332.Google Scholar
4.Dahl, T. S., Mataric, M. J. and Sukhatme, G. S., “A Machine Learning Method for Improving Task Allocation in Distributed Multi-Robot Transportation,” In: Understanding Complex Systems: Science Meets Technology (Braha, D., Minai, A. and Bar-Yam, Y., eds.) (Springer, Berlin, Germany, Jun. 2006) pp. 307337.Google Scholar
5.Dias, M. B., Zlot, R. M., Kalra, N.. and Stentz, A., “Market-based multirobot coordination: A survey and analysis,” Proc. IEEE 94 (7), 12571270 (Jul. 2006).Google Scholar
6.Gerkey, B. P. and Mataric, M. J., “Sold!: Auction methods for multi-robot control,” IEEE Trans. Robot. Autom. 18 (5), 758768 (Oct. 2002).Google Scholar
7.Gerkey, B. P. and Mataric, M. J, “A formal framework for the study of task allocation in multi-robot systems,” Int. J. Robot. Res. 23 (9), 939954 (Sep. 2004).Google Scholar
8.Gillespie, D., “A general method for numerically simulating the stochastic time evolution of coupled chemical reactions,” J. Comput. Phys. 22 (4), 403434 (1976).Google Scholar
9.Gillespie, D., “Exact stochastic simulation of coupled chemical reactions,” J. Phys. Chem. 81, 23402361 (1977).CrossRefGoogle Scholar
10.Golfarelli, M., Maio, D. and Rizzi, S., “Multi-Agent Path Planning Based on Task-Swap Negotiation,” Proceedings of the 16th UK Planning & Scheduling SIG Workshop (PlanSIG), Durham, England (1997).Google Scholar
11.Halasz, A., Hsieh, M. A., Berman, S. and Kumar, V., “Dynamic Redistribution of a Swarm of Robots Among Multiple Sites,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'07), San Diego, CA, USA (Oct.–Nov. 2007), pp. 23202325.Google Scholar
12.Hespanha, J. P., “Moment Closure for Biochemical Networks,” Proceedings of the 3rd International Symposium on Control, Communications and Signal Processing (Mar. 2008).Google Scholar
13.Hosokawa, K., Shimoyama, I. and Miura, H., “Dynamics of self-assembling systems: Analogy with chemical kinetics,” Artif. Life 1 (4), 413427 (1994).Google Scholar
14.Hsieh, M. A., Halasz, A., Berman, S. and Kumar, V., “Biologically inspired redistribution of a swarm of robots among multiple sites,” Swarm Intell. 2 (2), 121141 (2008).Google Scholar
15.Jadbabaie, A., Lin, J. and Morse, A. S., “Coordination of groups of mobile autonomous agents using nearest neighbor rules,” IEEE Trans. Autom. Control 48 (6), 9881001 (June 2003).Google Scholar
16.Klavins, E., “Proportional-Integral Control of Stochastic Gene Regulatory Networks,” Proceedings of the 2010 IEEE Conference on Decision & Control (CDC'10) (2010).Google Scholar
17.Lerman, K., Jones, C., Galstyan, A. and Mataric, M. J., “Analysis of dynamic task allocation in multi-robot systems,” Int. J. Robot. Res. 25 (3), 225242 (2006).CrossRefGoogle Scholar
18.Martinoli, A., Easton, K. and Agassounon, W., “Modeling of swarm robotic systems: A case study in collaborative distributed manipulation,” Int. J. Robot. Res. (Special Issue on Experimental Robotics) 23 (4–5), 415436 (2004).Google Scholar
19.Mather, T. W. and Hsieh, M. A., “Distributed Robot Ensemble Control for Deployment to Multiple Sites,” Proceedings of the 2011 Robotics: Science and Systems, Los Angeles, CA, USA (Jun.–Jul. 2011).Google Scholar
20.Mather, T. W. and Hsieh, M. A., “Ensemble Synthesis of Distributed Control and Communication Strategies,” Proceedins of the IEEE International Conferences on Robotics and Automation (ICRA'12), Minneapolis, MN, USA (May 2012).Google Scholar
21.Michael, N., Belta, C. and Kumar, V., “Controlling Three-Dimensional Swarms of Robots,” In: Proceedings of the IEEE International Conference on Robotics & Automation (ICRA) 2006, Orlando, FL, USA (Apr. 2006), pp. 964969.Google Scholar
22.Napp, N., Burden, S. and Klavins, E., “Setpoint Regulation for Stochastically Interacting Robots,” In: Robotics: Science and Systems V (MIT Press, Cambridge, MA, 2009), pp. 129136.Google Scholar
23.USARSim. “Unified system for automation and robot simulation,” available at: http://usarsim.sourceforge.net (2007) (accessed Nov. 2009).Google Scholar