Decentralized dynamic task planning for heterogeneous robotic networks
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In this paper, we propose a decentralized model and control framework for the assignment and execution of tasks, i.e. the dynamic task planning, for a network of heterogeneous robots. The proposed modeling framework allows the design of missions, defined as sets of tasks, in order to achieve global objectives regardless of the actual characteristics of the robotic network. The concept of skills, defined by the mission designer and considered as constraints for the mission execution, is exploited to distribute tasks across the robotic network. In addition, we develop a decentralized control algorithm, based on the concept of skills for decoupling the mission design from its deployment, which combines task assignment and execution through a consensus-based approach. Finally, conditions upon which the proposed decentralized formulation is equivalent to a centralized one are discussed. Experimental results are provided to validate the effectiveness of the proposed framework in a real-world scenario.
KeywordsHeterogenous multi-robot systems Task sequencing Distributed cooperation
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- Di Paola, D., Gasparri, A., Naso, D., & Lewis, F. (2012). Decentralized discrete-event modeling and control of task execution for robotic networks. In 2012 IEEE 51st Annual Conference on Decision and Control (CDC), (pp. 7346–7351). doi: 10.1109/CDC.2012.6426687.
- Di Paola, D., Gasparri, A., Naso, D., Ulivi, G., & Lewis, F. L. (2011). Decentralized task sequencing and multiple mission control for heterogeneous robotic networks. In Proceedings of 2011 IEEE International Conference on Robotics and Automation. doi: 10.1109/ICRA.2011.5980405.
- Di Rocco, M., La Gala, F., & Ulivi, G. (2012). Saetta: A small and cheap mobile unit to test multirobot algorithms. IEEE Robotics Automation Magazine. doi: 10.1109/MRA.2012.2185991.
- Fagiolini, A., Pellinacci, M., Valenti, G., Dini., G., & Bicchi, A. (2008). Consensus-based distributed intrusion detection for multi-robot systems. In IEEE International Conference on Robotics and Automation (ICRA), 2008 (pp. 120–127). doi: 10.1109/ROBOT.2008.4543196.
- Giordano, V., Jing, B. Z., Naso, D., & Lewis, F. (2008). Integrated supervisory and operational control of a warehouse with a matrix-based approach. IEEE Transactions on Automation Science and Engineering, 5(1), 53–70. doi: 10.1109/TASE.2007.891472.
- Jones, C. V., & Matarić, M. J. (2005). Behavior-based coordination in multi-robot systems. In S. Ge & F. Lewis (Eds.), Autonomous mobile robots: Sensing, control, decision-making, and applications. New York: Marcel Dekker, Inc. Retrieved from http://robotics.usc.edu/publications/466/.
- Meyer, W., & Drathen, A. (2012). Collaboration and collision functions for plan-based and event-driven mission control. In D. Yang (Ed.), Informatics in control, automation and robotics. Lecture notes in electrical engineering (Vol. 133, pp. 503–510). Berlin: Springer. doi: 10.1007/978-3-642-25992-0_69.
- Mireles, J., & Lewis, F. (2002). Deadlock analysis and routing on free-choice multipart reentrant flow lines using a matrix-based discrete event controller. In Proceedings of the 41st IEEE Conference on Decision and Control, 2002 (Vol. 1, pp. 793–798). doi: 10.1109/CDC.2002.1184602.
- Pinedo, M. L. (2008). Scheduling: Theory, algorithms, and systems. Berlin: Springer.Google Scholar