Model Predictive Path Planning of AGVs

Mixed Logical Dynamical Formulation and Distributed Coordination

Journal article (2023)

Authors

Jianbin Xin Zhengzhou University

Xuwen Wu Zhengzhou University

Andrea D'Ariano University of Roma Tre

R.R. Negenborn Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering

Fangfang Zhang Zhengzhou University

Research Group

Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI: https://doi.org/10.1109/TITS.2023.3254147

Robots Planning Model predictive control Path planning Path planning Predictive models Automated guided vehicles Task analysis Robot kinematics Mathematical models Mixed logical dynamical model

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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

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.

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