Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Predicting bucket-filling control actions of a wheel-loader operator using aneural network ensemble
Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.ORCID-id: 0000-0001-7395-7557
Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.ORCID-id: 0000-0001-5662-825x
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
2018 (Engelska)Ingår i: 2018 International Joint Conference on Neural Networks (IJCNN), Piscataway, NJ: IEEE, 2018, artikel-id 8489388Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Automatic bucket filling is an open problem since three decades. In this paper, we address this problem with supervised machine learning using data collected from manual operation. The range-normalized actuations of lift joystick, tilt joystick and throttle pedal are predicted using information from sensors on the machine and the prediction errors are quantified. We apply linear regression, k-nearest neighbors, neural networks, regression trees and ensemble methods and find that an ensemble of neural networks results in the most accurate predictions. The prediction root-mean-square-error (RMSE) of the lift action exceeds that of the tilt and throttle actions, and we obtain an RMSE below 0.2 for complete bucket fillings after training with as little as 135 bucket filling examples

Ort, förlag, år, upplaga, sidor
Piscataway, NJ: IEEE, 2018. artikel-id 8489388
Serie
Proceedings of the International Joint Conference on Neural Networks, E-ISSN 2161-4407
Nationell ämneskategori
Medieteknik Annan elektroteknik och elektronik
Forskningsämne
Industriell elektronik; Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-71382DOI: 10.1109/IJCNN.2018.8489388Scopus ID: 2-s2.0-85055724313ISBN: 978-1-5090-6014-6 (digital)OAI: oai:DiVA.org:ltu-71382DiVA, id: diva2:1259751
Konferens
2018 International Joint Conference on Neural Networks (IJCNN);8-13 July 2018;Rio de Janeiro, Brazil
Tillgänglig från: 2018-10-30 Skapad: 2018-10-30 Senast uppdaterad: 2019-02-11Bibliografiskt granskad
Ingår i avhandling
1. Automation of Wheel-Loaders
Öppna denna publikation i ny flik eller fönster >>Automation of Wheel-Loaders
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[sv]
Automation av hjullastare
Abstract [en]

Automation and tele-remote operation of mobile earth moving machines is desired for safety and productivity reasons. With tele-operation and automation, operators can avoid harsh ergonomic conditions and hazardous environments with poor air quality, and the productivity can in principle be improved by saving the time required to commute to and from work areas. Tele-remote operation of a wheel-loader is investigated and compared with manual operation, and it is found that the constrained perception of the machine is a challenging problem with remote operations. Real-time video transmission over wireless is difficult, but presents a way towards improving the remote operator’s quality of experience. To avoid glitches in the real-time video, arising from variable wireless conditions, the use of SCReAM (Self-Clocked Rate Adaptation for Multimedia) protocol is proposed. Experiments with a small scale robot over LTE show the usefulness of SCReAM for time-critical remote control applications. Automation of the bucket-filling step in the loading cycle of a wheel-loader has been an open problem, despite three decades of research. To address the bucket-filling problem, imitation learning has been applied using expert operator data, experiments are performed with a 20-tonne Volvo L180H wheel-loader and an automatic bucket-filling solution is proposed, developed and demonstrated in field-tests. The conducted experiments are in the realm of small data (100 bucket-filling examples), shallow time-delayed neural-network (TDNN), and a wheel-loader interacting with a non-stationary pile-environment. The total delay length of the TDNN model is found to be an important hyperparameter, and the trained and tuned model comes close to the performance of an expert operator with slightly longer bucket-filling time. The proposed imitation learning trained on medium coarse gravel succeeds in filling buckets in a gravel cobble pile. However, a general solution for automatic bucket-filling needs to be adaptive to possible changes in operating conditions. To adapt an initial imitation model for unseen operating conditions, a reinforcement learning approach is proposed and evaluated. A deterministic actor-critic algorithm is used to update actor (control policy) and critic (policy evaluation) networks. The experiments show that by use of a carefully chosen reward signal the models learns to improve and maximizes bucket weights in a gravel-cobble pile with only 40 bucket-filling trials. This shows that an imitation learning based bucket-filling solution equipped with a reinforcement learning agent is well suited for the continually changing operating conditions found in the construction industry. The results presented in this thesis are a demonstration of the use of artificial intelligence and machine learning methods for the operation of construction equipment. Wheel-loader OEMs can use these results to develop an autonomous bucket-filling function that can be used in manual, tele-remote or fully autonomous operations.

Ort, förlag, år, upplaga, sidor
Luleå: Luleå University of Technology, 2018
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Nationell ämneskategori
Teknik och teknologier Annan elektroteknik och elektronik
Forskningsämne
Industriell elektronik
Identifikatorer
urn:nbn:se:ltu:diva-71460 (URN)978-91-7790-258-4 (ISBN)978-91-7790-259-1 (ISBN)
Disputation
2019-01-31, A1545, Luleå, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-11-06 Skapad: 2018-11-06 Senast uppdaterad: 2019-02-19Bibliografiskt granskad

Open Access i DiVA

fulltext(354 kB)33 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 354 kBChecksumma SHA-512
f81292cef79a605868a4d69608971bd2835e10cace91e6dd01736542c80ead2da80b4792c35fbced51d48a770cdb34d8a3effa0dfc5da9d3fcb1da1d455b2acb
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Dadhich, SiddharthSandin, FredrikBodin, Ulf

Sök vidare i DiVA

Av författaren/redaktören
Dadhich, SiddharthSandin, FredrikBodin, Ulf
Av organisationen
EISLABDatavetenskap
MedieteknikAnnan elektroteknik och elektronik

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 33 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 53 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf