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Automatic track guidance of industrial trucks with time-variant vehicle parameters using AI-based controllers

  • This paper presents an extension of a self-learning control concept for automatic track guidance of industrial trucks in intralogistic systems. The presented approach is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI) and is able to adapt itself to different industrial truck variants and the associated specific vehicle parameters. Moreover, time-variant parameters during operation, such as the vehicle's velocity are taken into account. In order to consider the existing a priori knowledge of the controlled system and to avoid starting the whole training process of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only finetuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. In order to take into account the time-variant vehicle parameters during operation, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably extended. In this way, the varying speed can be considered in both training steps and the control parameters can be optimized accordingly. Thus, in case of the investigated scenarios a stable control loop behavior can be guaranteed for the entire speed range of industrial trucks. In order to demonstrate this, the new approach is compared with a RL control concept, not considering time-variant parameters.

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Metadaten
Author:Timm Sauer, Manuel Gorks, Luca Spielmann, Klaus Zindler, Ulrich Jumar
Parent Title (German):Advanes in Automotive Control - AAC
Document Type:Conference Proceeding
Language:English
Year of Completion:2022
Release Date:2022/10/25
Tag:Artificial Intelligence; Automatic control; Intelligent Transportation Systems
GND Keyword:Künstliche Intelligenz; Flurförderer
Volume:2022
Page Number:7
Urheberrecht:1
Institutes:Fakultät Ingenieurwissenschaften / Labore / Simulation, Steuerung und Regelung
research focus :Intelligent Systems / Intelligent Mobility
Licence (German):Keine Lizenz - es gilt das deutsche Urheberrecht
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