Abstract
In this paper, an approach is presented that allows a human supervisor to efficiently interact with task allocation in a multi-robot team (MRTA). The interaction is based on online modification of the setting of the employed MRTA optimization algorithm during its computation. For the example of a computationally expensive mixed-integer linear programming algorithm it is demonstrated how to achieve up to optimal solution quality, while simultaneously reducing the required calculation time compared to a fully autonomous optimization. The supervisor is enabled to rate feasible, intermediate solutions based on objective or subjective quality criteria and personal expertise. In that way, also suboptimal solutions can be chosen to be satisfactory, and the solver can be terminated without the need to wait for the completion of the computation of the optimal solution. An event-based communication concept with queries is used as an efficient means of implementation of the interaction. Furthermore, the supervisor can support the MRTA solver in finding good solutions by defining crucial parts of the solution structure. These intuitive commands are internally translated into constraints and are added to the problem as lazy constraints. This combination of human expertise and state-of-the-art optimization algorithms allows to achieve up to potentially optimal task allocation in much shorter time.
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References
S. da Costa Botelho and R. Alami. M+ : a scheme for multi-robot cooperation through negotiated task allocation and achievement. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages 1234–1239, 1999.
M. B. Dias and A. Stentz. A free market architecture for distributed control of a multirobot system. In 6th Int. Conf. on Intelligent Autonomous Systems (IAS-6), pages 115–122, 2000.
M. B. Dias and A. T. Stentz. Opportunistic optimization for market-based multirobot control. In Proc. of the 2002 IEEE/RSJ Int.l Conf. on Intelligent Robots and Systems (IROS ’02), volume 3, pages 2714–2720, 2002.
D. Gale. The theory of linear economic models. McGraw-Hill, New York, 1960.
B. P. Gerkey and M. J. Mataric. Sold!: Auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation, 18(5):758–768, 2002.
B. P. Gerkey and M. J. Mataric. A formal analysis and taxonomy of task allocation in multi-robot systems. The Int. Journal of Robotics Research, 23(9):939–954, 2004.
Gurobi optimizer reference manual, 2014. Gurobi Optimization Inc, http://www.gurobi.com/.
B. Kalyanasundaram and K. Pruhs. Online weighted matching. Journal of Algorithms, 14(3):478–488, 1993.
M. Koes, I. Nourbakhsh, and K. Sycara. Constraint optimization coordination architecture for search and rescue robotics. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages 3977–3982, 2006.
C. R. Kube and H. Zhang. Collective robotics: from social insects to robots. Adapt. Behav., 2:189–218, September 1993.
H. W. Kuhn. The hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2:83–97, 1955.
E. L. Lawler, J. K. Lenstra, A. R. Kan, and D. B. Shmoys, editors. The Traveling Salesman Problem: a guided tour of combinatorial optimization. Wiley, 1985.
R. Parasuraman, R. Molloy, and I. L. Singh. Performance consequences of automation-induced “complacency”. The Int. Journal of Aviation Psychology, 3(1):1–23, 1993.
L. E. Parker. Alliance: An architecture for fault tolerant multi-robot cooperation. IEEE Transactions on Robotics and Automation, 14(2):220–240, April 1998.
K. Petersen and O. von Stryk. An event-based communication concept for human supervision of autonomous robot teams. Int. Journal on Advances in Intelligent Systems, 4(3&4):357–369, 2011.
K. Petersen, A. Kleiner, and O. von Stryk. Fast task-sequence allocation for heterogeneous robot teams with a human in the loop. In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pages 1648–1655, 2013.
C. Reinl and O. von Stryk. Optimal control of multi-vehicle systems under communication constraints using mixed-integer linear programming. In Proc. of the First Int. Conf. on Robot Communication and Coordination (RoboComm), 2007.
S. Sariel, T. Balch, and N. Erdogan. Incremental multi-robot task selection for resource constrained and interrelated tasks. In Proc. of the 2007 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2007.
R. G. Smith. The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers, C-29(12):1104–1113, Dec. 1980.
M. Tambe. Towards flexible teamwork. Journal of Artificial Intelligence Research, 7:83–124, 1997.
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This research has been supported by the German Research Foundation (DFG) within GRK 1362 “Cooperative, adaptive and responsive monitoring in mixed mode environments”.
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Kurowski, K., von Stryk, O. (2016). Online Interaction of a Human Supervisor with Multi-Robot Task Allocation. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_70
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DOI: https://doi.org/10.1007/978-3-319-08338-4_70
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