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.
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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). https://doi.org/10.1007/s10514-018-9730-9
- Multi-robot coordination
- Quadratic optimization
- AGV systems
- Path planning