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Adaptive Video with SCReAM over LTE for Remote-Operated Working Machines
Ericsson AB, Luleå.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.ORCID-id: 0000-0001-7395-7557
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
Ericsson AB, Luleå.
2018 (engelsk)Inngår i: Wireless Communications & Mobile Computing, ISSN 1530-8669, E-ISSN 1530-8677, Vol. 2018, artikkel-id 3142496Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
John Wiley & Sons, 2018. Vol. 2018, artikkel-id 3142496
HSV kategori
Forskningsprogram
Industriell elektronik; Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-70502DOI: 10.1155/2018/3142496ISI: 000441534400001Scopus ID: 2-s2.0-85051520009OAI: oai:DiVA.org:ltu-70502DiVA, id: diva2:1240200
Merknad

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

Tilgjengelig fra: 2018-08-20 Laget: 2018-08-20 Sist oppdatert: 2018-11-06bibliografisk kontrollert
Inngår i avhandling
1. Automation of Wheel-Loaders
Åpne denne publikasjonen i ny fane eller vindu >>Automation of Wheel-Loaders
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[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.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2018
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
HSV kategori
Forskningsprogram
Industriell elektronik
Identifikatorer
urn:nbn:se:ltu:diva-71460 (URN)978-91-7790-258-4 (ISBN)978-91-7790-259-1 (ISBN)
Disputas
2019-01-31, A1545, Luleå, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-11-06 Laget: 2018-11-06 Sist oppdatert: 2019-02-19bibliografisk kontrollert

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Dadhich, SiddharthBodin, Ulf

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