Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Activity Recognition for Attachments of Construction Machinery Using Decision Trees
https://orcid.org/http://orcid.org/0009-0009-4416-5046
https://orcid.org/http://orcid.org/0000-0002-5647-4526
Activity recognition in construction helps operators and managers by providing information about current and past use of machines and tools. At the same time, excavator attachments enable excavators to perform other physical tasks in addition to earthmoving like screening or compacting. This work addresses the prototype of an activity recognition for excavator attachments independent of the carrier machine. Activity recognition was implemented for buckets, compactors, grabs, and screeners. The recognition is based on acceleration data and angular velocities collected by an inertial measurement unit on the attachment. Decision trees are used for classification. The activity classes “Operation”, “Transport”, and “Rest” were defined as target classes for the activity recognition. The decision trees achieved accuracies comparable to other machine learning algorithms. The results indicate that activity recognition of attachments should distinguish between different attachment classes like buckets and crabs.
Activity Recognition for Attachments of Construction Machinery Using Decision Trees
https://orcid.org/http://orcid.org/0009-0009-4416-5046
https://orcid.org/http://orcid.org/0000-0002-5647-4526
Activity recognition in construction helps operators and managers by providing information about current and past use of machines and tools. At the same time, excavator attachments enable excavators to perform other physical tasks in addition to earthmoving like screening or compacting. This work addresses the prototype of an activity recognition for excavator attachments independent of the carrier machine. Activity recognition was implemented for buckets, compactors, grabs, and screeners. The recognition is based on acceleration data and angular velocities collected by an inertial measurement unit on the attachment. Decision trees are used for classification. The activity classes “Operation”, “Transport”, and “Rest” were defined as target classes for the activity recognition. The decision trees achieved accuracies comparable to other machine learning algorithms. The results indicate that activity recognition of attachments should distinguish between different attachment classes like buckets and crabs.
Activity Recognition for Attachments of Construction Machinery Using Decision Trees
https://orcid.org/http://orcid.org/0009-0009-4416-5046
https://orcid.org/http://orcid.org/0000-0002-5647-4526
Activity recognition in construction helps operators and managers by providing information about current and past use of machines and tools. At the same time, excavator attachments enable excavators to perform other physical tasks in addition to earthmoving like screening or compacting. This work addresses the prototype of an activity recognition for excavator attachments independent of the carrier machine. Activity recognition was implemented for buckets, compactors, grabs, and screeners. The recognition is based on acceleration data and angular velocities collected by an inertial measurement unit on the attachment. Decision trees are used for classification. The activity classes “Operation”, “Transport”, and “Rest” were defined as target classes for the activity recognition. The decision trees achieved accuracies comparable to other machine learning algorithms. The results indicate that activity recognition of attachments should distinguish between different attachment classes like buckets and crabs.
Activity Recognition for Attachments of Construction Machinery Using Decision Trees
Lecture Notes in Civil Engineering
Fottner, Johannes (Herausgeber:in) / Nübel, Konrad (Herausgeber:in) / Matt, Dominik (Herausgeber:in) / Theobald, Marc (Autor:in) / Top, Felix (Autor:in)
International Conference on Construction Logistics, Equipment, and Robotics ; 2023 ; Munich , Germany
19.09.2023
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
| Europäisches Patentamt | 2020
Europäisches Patentamt | 2018