Learning-based path planning for automatic guided vehicles in container terminals

A case study at TBA Group

Master thesis (2022)

Authors

P. Wijnands Mechanical Engineering

Contributors

R.R. Negenborn Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 1)
F. Schulte Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 2)
M.B. Bokkers (supervisor 2)
A.W. ter Mors (coach)
Faculty
Mechanical Engineering
Copyright: © 2022 Patrick Wijnands
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Published Date

23-06-2022

Language

English

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Faculty

Mechanical Engineering

Abstract

This thesis has provided insight into how machine learning can be beneficial to path planning in container terminals. Path planning algorithms can be used in environments with automated vehicles. A well known algorithm is the A* path planning algorithm, which is the fastest optimal path planning algorithm under satisfied conditions. However, the behaviour of a container terminal is unknown beforehand, costs can change over iterations. Therefore, Liu et al. [Liu et al., 2019] and Keselman et al. [Keselman et al., 2018] show the advantage of combining A* with Machine Learning. This way, the exploring part of the ML algorithm is combined with the fast andmore precise properties of the A* PP algorithm. This thesis has proposed the machine learning algorithm Vehicle Aware Reinforcement Learning Path Planning Algorithm VARLPPA. This algorithm uses Monte Carlo Control method. This is a model free approach, which has been shown in both experiments to find more efficient solutions in exceptional situations.