Abstract
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.
Similar content being viewed by others
References
Aragues, R., Cortes, J., & Sagues, C. (2012). Distributed consensus on robot networks for dynamically merging feature-based maps. IEEE Transactions on Robotics, 28(4), 840–854.
Brass, P., Cabrera-Mora, F., Gasparri, A., & Jizhong, X. (2011). Multirobot tree and graph exploration. IEEE Transactions on Robotics, 27(4), 707–717. doi:10.1109/TRO.2011.2121170.
Burgard, W., Moors, M., Stachniss, C., & Schneider, F. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–386. doi:10.1109/TRO.2004.839232.
Choi, H., Brunet, L., & How, J. (2009). Consensus-based decentralized auctions for robust task allocation. IEEE Transactions on Robotics, 25(4), 912–926.
Costelha, H., & Lima, P. (2012). Robot task plan representation by Petri nets: Modelling, identification, analysis and execution. Autonomous Robots, 33(4), 337–360. doi:10.1007/s10514-012-9288-x.
Defoort, M., Floquet, T., Kokosy, A., & Perruquetti, W. (2008). Sliding-mode formation control for cooperative autonomous mobile robots. IEEE Transactions on Industrial Electronics, 55(11), 3944–3953. doi:10.1109/TIE.2008.2002717.
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.
Gasparri, A., & Prosperi, M. (2008). A bacterial colony growth algorithm for mobile robot localization. Autonomous Robots, 24(4), 349–364. doi:10.1007/s10514-007-9076-1.
Gerkey, B. P., & Matarić, M. J. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 23(9), 939–954.
Giordano, V., Ballal, P., Lewis, F., Turchiano, B., & Zhang, J. (2006). Supervisory control of mobile sensor networks: Math formulation, simulation, implementation. IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(4), 806–819.
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.
Huq, R., Mann, G., & Gosine, R. (2006). Behavior-modulation technique in mobile robotics using fuzzy discrete event system. IEEE Transactions on Robotics, 22(5), 903–916. doi:10.1109/TRO.2006.878937.
Ji, M., & Sarkar, N. (2007). Supervisory fault adaptive control of a mobile robot and its application in sensor-fault accommodation. IEEE Transactions on Robotics, 23(1), 174–178. doi:10.1109/TRO.2006.889481.
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.
Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541–580.
Pallottino, L., Scordio, V., & Frazzoli, A. B. E. (2007). Decentralized cooperative policy for conflict resolution in multivehicle systems. IEEE Transactions on Robotics, 23(6), 1170–1183. doi:10.1109/TRO.2007.909810.
Pinedo, M. L. (2008). Scheduling: Theory, algorithms, and systems. Berlin: Springer.
Song, M., Tarn, T., & Xi, N. (2000). Integration of task scheduling, action planning, and control in robotic manufacturing systems. Proceedings of the IEEE, 88(7), 1097–1107. doi:10.1109/5.871311.
Tacconi, D., & Lewis, F. (1997). A new matrix model for discrete event systems: Application to simulation. IEEE Control Systems Magazine, 17(5), 62–71.
Tiehua, C., & Sanderson, A. (1995). Task sequence planning using fuzzy Petri nets. IEEE Transactions on Systems, Man and Cybernetics, 25(5), 755–768. doi:10.1109/21.376489.
Wang, Z., & Gu, D. (2012). Cooperative target tracking control of multiple robots. IEEE Transactions on Industrial Electronics, 59(8), 3232–3240. doi:10.1109/TIE.2011.2146211.
Zavlanos, M., & Pappas, G. J. (2008). Distributed connectivity control of mobile networks. IEEE Transactions on Robotics, 24(6), 1416–1428.
Zhang, H., Lewis, F., & Qu, Z. (2012). Lyapunov, adaptive, and optimal design techniques for cooperative systems on directed communication graphs. IEEE Transactions on Industrial Electronics, 59(7), 3026–3041. doi:10.1109/TIE.2011.2160140.
Zouaghi, L., Alexopoulos, A., Wagner, A., & Badreddin, E. (2014). Mission-based online generation of probabilistic monitoring models for mobile robot navigation using Petri nets. Robotics and Autonomous Systems, 62(1), 61–67. doi:10.1016/j.robot.2012.07.012.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 1 (avi 22962 KB)
Appendix: A Boolean algebra and matrix operations
Appendix: A Boolean algebra and matrix operations
A Boolean algebra \(\{ \mathbb {B}, \, \otimes , \, \oplus ,\, \lnot , \, 0,\, 1\}\) is a six-tuple consisting of a set \(\mathbb {B}\) called universe, equipped with two binary operations \(\otimes \) called and, \(\oplus \) called or, a unary operation \(\lnot \) called complement and two elements \(0\) and \(1\), such that the following axioms hold: associativity, commutativity, absorption, distributivity and complements.
Let us define a general \(n \times m\) logical matrix as \(\mathbf {M} \in \mathbb {B}^{n \times m}\), where \(\mathbb {B}\) is the boolean set \(\{ 0,1\}\) and let us introduce the logical matrix product as follows:
Definition 23
(Logical matrix product) Let us consider two logical matrices \(\mathbf {A} \in \mathbb {B}^{n\times m}\), \(\mathbf {B} \in \mathbb {B}^{m\times p}\). The logical matrix product \(\mathbf {C} \in \mathbb {B}^{n\times p}\) can be defined as: \(\mathbf {C} = \mathbf {A} \odot \mathbf {B}\), where each element \(\mathbf {C}_{i,j}\) is defined as:
for each pair \((i,j)\) with \(i = \{1,\ldots ,n \}\) and \(j = \{1,\ldots , p\}\).
Heres an example to clarify the above definition:
Example
Given the Boolean matrix \(\mathbf {A} \in \mathbb {B}^{2 \times 2}\), and the Boolean column vector \(\mathbf {b} \in \mathbb {B}^{2}\), defined as follows:
the logical matrix product \(\mathbf {c} \in \mathbb {B}^{2}\), is given by
Rights and permissions
About this article
Cite this article
Di Paola, D., Gasparri, A., Naso, D. et al. Decentralized dynamic task planning for heterogeneous robotic networks. Auton Robot 38, 31–48 (2015). https://doi.org/10.1007/s10514-014-9395-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-014-9395-y