TR2025-103
Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints
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- "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}
- }
,
- "Truck Fleet Coordination for Warehouse Trailer Management by Temporal Logic with Energy Constraints", American Control Conference (ACC), July 2025.
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Research Areas:
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.
Related News & Events
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NEWS MERL researchers present 13 papers at ACC 2025 Date: July 8, 2025 - July 10, 2025
Where: Denver, USA
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Purnanand Elango; Jordan Leung; Saviz Mowlavi; Diego Romeres; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Electric Systems, Machine Learning, Multi-Physical Modeling, RoboticsBrief- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.