TR2025-103

Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints


    •  Cardona, G., Vasile, C.-I., Di Cairano, S., "Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints", American Control Conference (ACC), July 2025.
      BibTeX TR2025-103 PDF
      • @inproceedings{Cardona2025jul,
      • author = {Cardona, Gustavo and Vasile, Cristian-Ioan and {Di Cairano}, Stefano},
      • title = {{Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints}},
      • booktitle = {American Control Conference (ACC)},
      • year = 2025,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2025-103}
      • }
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  • Research Areas:

    Control, Dynamical Systems, Optimization, Robotics

Abstract:

We consider the coordination of a fleet of tractor trucks to manage trailers in a large warehouse complex and propose an approach that leverages Metric Temporal Logic (MTL) to describe missions to be executed. Each mission includes multiple tasks, such as reaching a trailer, connecting to it, moving it to a sequence of specific warehouse regions, such as loading docks, internal holding areas, and departure parking lots, and eventually disconnecting from it. The electric- powered tractor trucks must also be recharged by visiting charging stations. The MTL formulation avoids an operator manually designing a mission specification, which can quickly become unfeasible with many requests and possible assignments of tractor trucks. MTL specifications and motion dynamics are formulated as a mixed integer linear programming (MILP) approach, where the cost function includes performance ob- jectives such as minimizing the trailer motions and energy- efficient usage. Since missions are added and removed during operation and to also reduce the computation time, we modify the method to allow for a receding horizon approach that allows for partial satisfaction of the MTL specification and uses the cost function to favor the progress towards completion of partially satisfied specifications. We compare different MILP formulations in simulations.