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Machine Learning approach to Automatic Bucket Loading
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-7395-7557
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825X
Number of Authors: 42016 (English)In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 1260-1265, article id 7535925Conference paper, Published paper (Refereed)
Abstract [en]

The automation of bucket loading for repetitive tasks of earth-moving operations is desired in several applications at mining sites, quarries and construction sites where larger amounts of gravel and fragmented rock are to be moved. In load and carry cycles the average bucket weight is the dominating performance parameter, while fuel efficiency and loading time also come into play with short loading cycles. This paper presents the analysis of data recorded during loading of different types of gravel piles with a Volvo L110G wheel loader. Regression models of lift and tilt actions are fitted to the behavior of an expert driver for a gravel pile. We present linear regression models for lift and tilt action that explain most of the variance in the recorded data and outline a learning approach for solving the automatic bucket loading problem. A general solution should provide good performance in terms of average bucket weight, cycle time of loading and fuel efficiency for different types of material and pile geometries. We propose that a reinforcement learning approach can be used to further refine models fitted to the behavior of expert drivers, and we briefly discuss the scooping problem in terms of a Markov decision process and possible value functions and policy iteration schemes.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2016. p. 1260-1265, article id 7535925
Series
Mediterranean Conference on Control and Automation, E-ISSN 2325-369X
Keywords [en]
Information technology - Automatic control
Keywords [sv]
Informationsteknik - Reglerteknik
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communication Technology Control Engineering
Research subject
Industrial Electronics; Mobile and Pervasive Computing; Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-28755DOI: 10.1109/MED.2016.7535925ISI: 000391154900208Scopus ID: 2-s2.0-84986226543Local ID: 2a83ec63-323c-43a5-a69f-f77d7f9abfb0ISBN: 978-1-4673-8345-5 (electronic)OAI: oai:DiVA.org:ltu-28755DiVA, id: diva2:1001959
Conference
Mediterranean Conference on Control and Automation : 21/06/2016 - 24/06/2016
Note

Godkänd; 2016; 20160819 (andbra)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-11-06Bibliographically approved
In thesis
1. Automation of Wheel-Loaders
Open this publication in new window or tab >>Automation of Wheel-Loaders
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[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.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-71460 (URN)978-91-7790-258-4 (ISBN)978-91-7790-259-1 (ISBN)
Public defence
2019-01-31, A1545, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2019-02-19Bibliographically approved

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Dadhich, SiddharthBodin, UlfSandin, FredrikAndersson, Ulf

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