Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Jianbin Xin, Xuwen Wu, Andrea D'Ariano, Rudy Negenborn, Fangfang Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0-1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment.

Original languageEnglish
Pages (from-to)6943-6954
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Automated guided vehicles
  • Mathematical models
  • mixed logical dynamical model
  • model predictive control
  • Path planning
  • path planning
  • Planning
  • Predictive models
  • Robot kinematics
  • Robots
  • Task analysis


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