Analysis of the Operation of Industrial Trucks based on Position Data

TL;DR

This paper presents a novel approach using position data and signal processing to analyze industrial truck operations in warehouses, including a new app with validated motion detection and analysis methods.

Contribution

It introduces a system for analyzing industrial truck operations solely based on position data, with a new signal processing scheme and analysis methods, implemented in an open-source app.

Findings

Low pass Butterworth filter performed best in static experiments.

Motion detection scheme showed good detection quality in realistic tests.

The system enables effective analysis of warehouse truck operations.

Abstract

Indoor positioning systems (IPSs) can make an important contribution to the analysis and optimization of internal transport processes. The overall aim of this work is to examine how position data can be used to analyze the operation of industrial trucks in warehouses. This is achieved by developing a concept for the analysis of industrial truck operations based merely on position data. The concept consists of a signal processing scheme to derive kinematic data and three analysis methods - Monitoring, Area analysis, and Motion analysis. Schemes for the signal processing and detection of motion events were developed and implemented as part of the TrOpLocerApp (Truck Operation Localization Analyzer-Application) for recording, displaying, and processing position data, according to the proposed system concept. The TrOpLocer-App source code is published on GitLab. Different filter algorithms were examined, as part of the signal processing scheme, from which the low pass Butterworth filter has shown the best results in static experiments. Validation of the motion detection scheme shows good detection quality for distinct events in a realistic movement experiment.