This paper presents an optimization strategy to coordinate a fleet of Automated Guided Vehicles (AGVs) traveling on ad-hoc pre-defined roadmaps. Specifically, the objective is to maximize traffic throughput of AGVs navigating in an automated warehouse by minimizing the time AGVs spend negotiating complex traffic patterns to avoid collisions with other AGVs. In this work, the coordination problem is posed as a Quadratic Program where the optimization is performed in a centralized manner. The proposed method is validated by means of simulations and experiments for different industrial warehouse scenarios. The performance of the proposed strategy is then compared with a recently proposed decentralized coordination strategy that relies on local negotiations for shared resources. The results show that the proposed coordination strategy successfully maximizes vehicle throughput and significantly minimizes the time vehicles spend negotiating traffic under different scenarios.
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This condition is usually not conservative. In fact, in an automated warehouse, all the AGVs have typically the same size.
The bounds may depend on the AGV and on the segment to be crossed. In order to keep the notation simple, we have considered constant values. All the results obtained in the paper can be easily extended to variable bounds.
Alami, R., Fleury, S., Herrb, M., Ingrand, F., & Robert, F. (1998). Multi-robot cooperation in the martha project. IEEE Robotics Automation Magazine, 5(1), 36–47. https://doi.org/10.1109/100.667325.
Beinschob, P., Meyer, M., Reinke, C., Digani, V., Secchi, C., & Sabattini, L. (2017). Semi-automated map creation for fast deployment of AGV fleets in modern logistics. Robotics and Autonomous Systems, 87, 281–295.
Bennewitz, M., Burgard, W., & Thrun, S. (2001). Constraint-based optimization of priority schemes for decoupled path planning techniques. In: KI 2001: Advances in artificial intelligence (pp. 78–93). Springer.
Cap, M., Novak, P., Kleiner, A., & Selecky, M. (2015). Prioritized planning algorithms for trajectory coordination of multiple mobile robots. IEEE Transactions on Automation Science and Engineering, 12(3), 835–849. https://doi.org/10.1109/TASE.2015.2445780.
Digani, V., Hsieh, M., Sabattini, L., & Secchi, C. (2015a). A quadratic programming approach for coordinating multi-AGV systems. In Proceedings of the IEEE international conference on automation science and engineering (CASE). Gothenburg.
Digani, V., Sabattini, L., & Secchi, C. (2016). A probabilistic eulerian traffic model for the coordination of multiple AGVs in automatic warehouses. IEEE Robotics and Automation Letters, 1(1), 26–32.
Digani, V., Sabattini, L., Secchi, C., & Fantuzzi, C. (2014a). An automatic approach for the generation of the roadmap for multi-AGV systems in an industrial environment. In IEEE International conference on intelligent robots and systems (IROS).
Digani, V., Sabattini, L., Secchi, C., & Fantuzzi, C. (2014b). Hierarchical traffic control for partially decentralized coordination of multi AGV systems in industrial environments. In IEEE International conference on robotics and automation (ICRA).
Digani, V., Sabattini, L., Secchi, C., & Fantuzzi, C. (2015b). Ensemble coordination approach in multi-AGV systems applied to industrial warehouses. IEEE Transactions on Automation Science and Engineering, 12(3), 922–934. https://doi.org/10.1109/TASE.2015.2446614.
Fanti, M., Mangini, A., Pedroncelli, G., & Ukovich, W. (2015). Decentralized deadlock-free control for AGV systems. In American control conference (ACC), 2015 (pp. 2414–2419). https://doi.org/10.1109/ACC.2015.7171094.
Habib, D., Jamal, H., & Khan, S. A. (2013). Employing multiple unmanned aerial vehicles for co-operative path planning. International Journal of Advanced Robotic Systems, 10, 235.
Hoshino, S., & Seki, H. (2013). Multi-robot coordination for jams in congested systems. Robotics and Autonomous Systems, 61, 808–820.
Hui, N. (2010). Coordinated motion planning of multiple mobile robots using potential field method. In: 2010 International conference on industrial electronics, control robotics (IECR) (pp. 6–11). https://doi.org/10.1109/IECR.2010.5720131.
Jager, M., & Nebel, B. (2001). Decentralized collision avoidance, deadlock detection, and deadlock resolution for multiple mobile robots. In Proceedings of the IEEE/RSJ International conference on intelligent robots and systems, 2001 (Vol. 3, pp. 1213–1219). https://doi.org/10.1109/IROS.2001.977148.
Kavraki, L., Svestka, P., Latombe, J. C., & Overmars, M. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.
LaValle, S., & Hutchinson, S. (1998). Optimal motion planning for multiple robots having independent goals. IEEE Transactions on Robotics and Automation, 14(6), 912–925. https://doi.org/10.1109/70.736775.
LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press.
Makarem, L. & Gillet, D. (2012). Fluent coordination of autonomous vehicles at intersections. IEEE International conference on systems, man, and cybernetics (SMC), 2012 (pp. 2557–2562). https://doi.org/10.1109/ICSMC.2012.6378130.
Manca, S., Fagiolini, A., & Pallottino, L. (2011). Decentralized coordination system for multiple AGVs in a structured environment. In 18th World congress of the international federation of automatic control (IFAC 2011) (Vol. 18, pp. 6005–6010).
Mather, T. W., Braun, C., & Hsieh, M. A. (2010). Distributed filtering for time-delayed deployment to multiple sites (best paper award winner). In 10th International symposium on distributed autonomous robotics systems (DARS 2010). Lausanne.
Mather, T. W., & Hsieh, M. A. (2010). Ensemble modeling and control for congestion management in automated warehouses (best paper finalist). In IEEE International conference on automation science and engineering (CASE 2012). Seoul.
Olmi, R., Secchi, C., & Fantuzzi, C. (2008). Coordination of multiple AGVs in an industrial application. In IEEE International conference on robotics and automation, 2008. ICRA 2008 (pp. 1916–1921).
Olmi, R., Secchi, C., & Fantuzzi, C. (2011). Coordination of industrial AGVs. International Journal of Vehicle Autonomous Systems, 9(1), 5–25.
Pallottino, L., Scordio, V. G., Bicchi, A., & Frazzoli, E. (2007). Decentralized cooperative policy for conflict resolution in multivehicle systems. IEEE Transactions on Robotics, 23(6), 1170–1183. https://doi.org/10.1109/TRO.2007.909810.
Park, B., Choi, J., & Chung, W. K. (2012). An efficient mobile robot path planning using hierarchical roadmap representation in indoor environment. IEEE International conference on robotics and automation, 2012 (pp. 180–186). https://doi.org/10.1109/ICRA.2012.6225368.
Pecora, F., Cirillo, M., & Dimitrov, D. (2012). On mission-dependent coordination of multiple vehicles under spatial and temporal constraints. In IEEE/RSJ International conference on intelligent robots and systems (IROS) (pp. 5262–5269). IEEE.
Peng, J., & Akella, S. (2005) Coordinating multiple double integrator robots on a roadmap: Convexity and global optimality. In Robotics and automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International conference on (pp. 2751–2758). IEEE.
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., et al. (2009). ROS: An open-source robot operating system. ICRA Workshop on Open Source Software, 3, 5.
Reveliotis, S., & Roszkowska, E. (2011). Conflict resolution in free-ranging multivehicle systems: A resource allocation paradigm. IEEE Transactions on Robotics, 27(2), 283–296. https://doi.org/10.1109/TRO.2010.2098270.
Sabattini, L., Digani, V., Lucchi, M., Secchi, C., & Fantuzzi, C. (2015). Mission assignment for multi-vehicle systems in industrial environments. In Proceedings of the IFAC symposium on robot control (SYROCO). Salvador.
Sabattini, L., Digani, V., Secchi, C., Cotena, G., Ronzoni, D., Foppoli, M., et al. (2013). Technological roadmap to boost the introduction of AGVs in industrial applications. In IEEE International conference on intelligent computer communication and processing (ICCP).
Sabattini, L., Digani, V., Secchi, C., & Fantuzzi, C. (2016). Hierarchical coordination strategy for multi-AGV systems based on dynamic geodesic environment partitioning. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS). Daejeon.
Secchi, C., Olmi, R., Rocchi, F., & Fantuzzi, C. (2015). A dynamic routing strategy for the traffic control of AGVs in automatic warehouses. In Robotics and automation (ICRA), 2015 IEEE International conference on (pp. 3292–3297). https://doi.org/10.1109/ICRA.2015.7139653.
Siméon, T., Leroy, S., & Lauumond, J. P. (2002). Path coordination for multiple mobile robots: A resolution-complete algorithm. IEEE Transactions on Robotics and Automation, 18(1), 42–49.
van den Berg, J., & Overmars, M. (2005). Prioritized motion planning for multiple robots. In Intelligent robots and systems, 2005 (IROS 2005). 2005 IEEE/RSJ International Conference on (pp. 430–435). https://doi.org/10.1109/IROS.2005.1545306.
Yang, P., Freeman, R., & Lynch, K. (2008). Multi-agent coordination by decentralized estimation and control. IEEE Transactions on Automatic Control, 53(11), 2480–2496.
Zhang, Y., & Mehrjerdi, H. (2013). A survey on multiple unmanned vehicles formation control and coordination: Normal and fault situations. In International conference on unmanned aircraft systems (ICUAS) (pp. 1087–1096). IEEE.
Zheng, K., Tang, D., Gu, W., & Dai, M. (2013). Distributed control of multi-AGV system based on regional control model. Production Engineering, 7, 433–441.
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Digani, V., Hsieh, M.A., Sabattini, L. et al. Coordination of multiple AGVs: a quadratic optimization method. Auton Robot 43, 539–555 (2019) doi:10.1007/s10514-018-9730-9
- Multi-robot coordination
- Quadratic optimization
- AGV systems
- Path planning