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Abstract
Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.
Highlights
V EHICLES have an important role in the distribution of materials in industrial environments, current and future generation factories (Industry 4.0) can benefit from tracking these vehicles to increase productivity, improve logistics processes and enable device intercommunication
To overcome the main challenges of indoor vehicle localization, we propose TrackInFactory, a solution for the localization and tracking of industrial vehicles based on a particle filter (PF) that performs the sensor fusion in a novel way
Wi-Fi fingerprinting can be used either to provide a position estimate [Fig. 1(a)] or to obtain a set of similarities between the operational Wi-Fi samples (WSs) and the reference samples of the radio map (RM) [Fig. 1(b)]. Both outputs can serve as input for the PF, being the Wi-Fi position estimate related to a loose coupling (LC) approach, whereas the set of similarities related to a tight coupling (TC) approach
Summary
V EHICLES have an important role in the distribution of materials in industrial environments, current and future generation factories (Industry 4.0) can benefit from tracking these vehicles to increase productivity, improve logistics processes and enable device intercommunication. Positioning of industrial vehicles can have two distinct applications, namely, continuous tracking and monitoring of the vehicle’s position as it operates (main focus of this work), and as support for navigation of autonomous vehicles [3], [4]. Both applications have similar requirements, including coverage of the entire operating area (factory plant), submeter accuracy (or better for navigation), low maximum error, availability and high reliability. To overcome the main challenges of indoor vehicle localization, we propose TrackInFactory, a solution for the localization and tracking of industrial vehicles based on a particle filter (PF) that performs the sensor fusion in a novel way. A significant additional contribution of this article is the real-world experiments conducted in an industrial scenario with an industrial tow tractor, that allowed to compare the performance between the proposed solution and a loose coupling (LC) solution
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