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Automation of Wheel-Loaders
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-7395-7557
2018 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Automation av hjullastare (Swedish)
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: urn:nbn:se:ltu:diva-71460ISBN: 978-91-7790-258-4 (print)ISBN: 978-91-7790-259-1 (electronic)OAI: oai:DiVA.org:ltu-71460DiVA, id: diva2:1261019
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
List of papers
1. Remote Controlled Short-Cycle Loading of Bulk Material in Mining Applications
Open this publication in new window or tab >>Remote Controlled Short-Cycle Loading of Bulk Material in Mining Applications
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2015 (English)Article in journal (Refereed) Published
Abstract [en]

High-capacity wireless IP networks with limited delays are nowadays being deployed in both underground and open-pit mines. This allows for advanced remote control of mining machinery with improved feedback to operators and extensive monitoring of machine status, wear and fatigue. Wireless connectivity varies however depending on channel impairments caused by obstacles, multi-path fading and other radio issues. Therefore remote control and monitoring should be capable of adapting their sending rates to handle variations in communications quality. This paper presents key challenges in advanced remote control and monitoring of working machines via high-capacity wireless IP networks in mining environments. We reason about these challenges in context of underground short-cycle load, haul and dump operation with large-volume built wheel-loaders and present a generic communication solution for an operator assistance concept capable of adapting to varying communication properties

National Category
Computer Sciences Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Dependable Communication and Computation Systems; Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-28592 (URN)10.1016/j.ifacol.2015.10.077 (DOI)2-s2.0-84992523126 (Scopus ID)26e4003f-9b37-46fa-8048-7ad42ea96ff7 (Local ID)26e4003f-9b37-46fa-8048-7ad42ea96ff7 (Archive number)26e4003f-9b37-46fa-8048-7ad42ea96ff7 (OAI)
Conference
IFAC Workshop on Mining, Mineral and Metal Processing : 25/08/2015 - 27/08/2015
Note

Konferensartikel i tidskrift

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-11-06Bibliographically approved
2. Key challenges in automation of earth-moving machines
Open this publication in new window or tab >>Key challenges in automation of earth-moving machines
2016 (English)In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 68, p. 212-222Article in journal (Refereed) Published
Abstract [en]

A wheel loader is an earth-moving machine used in construction sites, gravel pits and mining to move blasted rock, soil and gravel. In the presence of a nearby dump truck, the wheel loader is said to be operating in a short loading cycle. This paper concerns the moving of material (soil, gravel and fragmented rock) by a wheel loader in a short loading cycle with more emphasis on the loading step. Due to the complexity of bucket-environment interactions, even three decades of research efforts towards automation of the bucket loading operation have not yet resulted in any fully autonomous system. This paper highlights the key challenges in automation and tele-remote operation of earth-moving machines and provides a survey of different areas of research within the scope of the earth-moving operation. The survey of publications presented in this paper is conducted with an aim to highlight the previous and ongoing research work in this field with an effort to strike a balance between recent and older publications. Another goal of the survey is to identify the research areas in which knowledge essential to automate the earth moving process is lagging behind. The paper concludes by identifying the knowledge gaps to give direction to future research in this field.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communication Technology
Research subject
Industrial Electronics; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-9131 (URN)10.1016/j.autcon.2016.05.009 (DOI)000379371100019 ()2-s2.0-84971665331 (Scopus ID)7b11fd1b-e46b-4cf4-8a9c-b0ecae42f1a0 (Local ID)7b11fd1b-e46b-4cf4-8a9c-b0ecae42f1a0 (Archive number)7b11fd1b-e46b-4cf4-8a9c-b0ecae42f1a0 (OAI)
Note

Validerad; 2016; Nivå 2; 20160531 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-11-06Bibliographically approved
3. Machine Learning approach to Automatic Bucket Loading
Open this publication in new window or tab >>Machine Learning approach to Automatic Bucket Loading
2016 (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
Series
Mediterranean Conference on Control and Automation, E-ISSN 2325-369X
Keywords
Information technology - Automatic control, 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:nbn:se:ltu:diva-28755 (URN)10.1109/MED.2016.7535925 (DOI)000391154900208 ()2-s2.0-84986226543 (Scopus ID)2a83ec63-323c-43a5-a69f-f77d7f9abfb0 (Local ID)978-1-4673-8345-5 (ISBN)2a83ec63-323c-43a5-a69f-f77d7f9abfb0 (Archive number)2a83ec63-323c-43a5-a69f-f77d7f9abfb0 (OAI)
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
4. From Tele-remote Operation to Semi-automated Wheel-loader
Open this publication in new window or tab >>From Tele-remote Operation to Semi-automated Wheel-loader
2018 (English)In: International Journal of Electrical and Electronic Engineering and Telecommunications, ISSN 2319-2518, Vol. 7, no 4, p. 178-182Article in journal (Refereed) Published
Abstract [en]

This paper presents experimental results with tele-remote operation of a wheel-loader and proposes a method to semi-automate the process. The different components of the tele-remote setup are described in the paper. We focus on the short loading cycle, which is commonly used at quarry and construction sites for moving gravel from piles onto trucks. We present results from short-loading-cycle experiments with three operators, comparing productivity between tele-remote operation and manual operation. A productivity loss of 42% with tele-remote operation motivates the case for more automation. We propose a method to automate the bucket-filling process, which is one of the key operations performed by a wheel-loader.

Keywords
automation, bucket-filling, construction, quarry, tele-operation, wheel-loader
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communication Technology
Research subject
Industrial Electronics; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71381 (URN)10.18178/ijeetc.7.4.178-182 (DOI)
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-11-23Bibliographically approved
5. Adaptive Video with SCReAM over LTE for Remote-Operated Working Machines
Open this publication in new window or tab >>Adaptive Video with SCReAM over LTE for Remote-Operated Working Machines
2018 (English)In: Wireless Communications & Mobile Computing, ISSN 1530-8669, E-ISSN 1530-8677, Vol. 2018, article id 3142496Article in journal (Refereed) Published
Abstract [en]

Remote operation is a step toward the automation of mobile working machines. Safe and efficient teleremote operation requires good-quality video feedback. Varying radio conditions make it desirable to adapt the video sending rate of cameras to make the best use of the wireless capacity. The adaptation should be able to prioritize camera feeds in different directions depending on motion, ongoing tasks, and safety concerns. Self-Clocked Rate Adaptation for Multimedia (SCReAM) provides a rate adaptation algorithm for these needs. SCReAM can control the compression used for multiple video streams using differentiating priorities and thereby provide sufficient congestion control to achieve both low latency and high video throughput. We present results from the testing of prioritized adaptation of four video streams with SCReAM over LTE and discuss how such adaptation can be useful for the teleremote operation of working machines.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
National Category
Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communication Technology
Research subject
Industrial Electronics; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70502 (URN)10.1155/2018/3142496 (DOI)000441534400001 ()2-s2.0-85051520009 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-08-24 (svasva)

Available from: 2018-08-20 Created: 2018-08-20 Last updated: 2018-11-06Bibliographically approved
6. Predicting bucket-filling control actions of a wheel-loader operator using aneural network ensemble
Open this publication in new window or tab >>Predicting bucket-filling control actions of a wheel-loader operator using aneural network ensemble
2018 (English)In: 2018 International Joint Conference on Neural Networks (IJCNN), Piscataway, NJ: IEEE, 2018, article id 8489388Conference paper, Published paper (Refereed)
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

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018
Series
Proceedings of the International Joint Conference on Neural Networks, E-ISSN 2161-4407
National Category
Media and Communication Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71382 (URN)10.1109/IJCNN.2018.8489388 (DOI)2-s2.0-85055724313 (Scopus ID)978-1-5090-6014-6 (ISBN)
Conference
2018 International Joint Conference on Neural Networks (IJCNN);8-13 July 2018;Rio de Janeiro, Brazil
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2019-02-11Bibliographically approved
7. Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders
Open this publication in new window or tab >>Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders
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2019 (English)In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 97, p. 1-12Article in journal (Refereed) Published
Abstract [en]

Automation of earth-moving industries (construction, mining and quarry) require automatic bucket-filling algorithms for efficient operation of front-end loaders. Autonomous bucket-filling is an open problem since three decades due to difficulties in developing useful earth models (soil, gravel and rock) for automatic control. Operators make use of vision, sound and vestibular feedback to perform the bucket-filling operation with high productivity and fuel efficiency. In this paper, field experiments with a small time-delayed neural network (TDNN) implemented in the bucket control-loop of a Volvo L180H front-end loader filling medium coarse gravel are presented. The total delay time parameter of the TDNN is found to be an important hyperparameter due to the variable delay present in the hydraulics of the wheel-loader. The TDNN network successfully performs the bucket-filling operation after an initial period (100 examples) of imitation learning from an expert operator. The demonstrated solution show only 26% longer bucket-filling time, an improvement over manual tele-operation performance.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Neural-network, Bucket-filling, Wheel-loader, Automation, Construction
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communication Technology
Research subject
Industrial Electronics; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71383 (URN)10.1016/j.autcon.2018.10.013 (DOI)000453623600001 ()2-s2.0-85055696994 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-11-07 (johcin) 

Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2019-01-30Bibliographically approved
8. Adaption of imitation based bucket-filling model in an hour using reinforcement learning
Open this publication in new window or tab >>Adaption of imitation based bucket-filling model in an hour using reinforcement learning
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(English)Manuscript (preprint) (Other academic)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:ltu:diva-71459 (URN)
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2018-11-06

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Dadhich, Siddharth

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