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Laser based intersection detection for reactive navigation in an underground mine
Örebro University, School of Science and Technology. (AASS)
Örebro University, School of Science and Technology. (AASS)
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0001-8229-1363
2008 (English)In: IEEE/RSJ international conference on intelligent robots and systems, 2008, IROS 2008, 2008, 2222-2227 p.Conference paper, (Refereed)
Abstract [en]

In this paper we propose a new feature detection algorithm to enable junction recognition intended for high speed reactive navigation in tunnel like environments. We also present an extensive experimental evaluation of the algorithm based on data recorded in a real mine. The algorithm is faster and has less environmental constraints than similar algorithms that can be found in the litterature.

Place, publisher, year, edition, pages
2008. 2222-2227 p.
National Category
Computer Science Engineering and Technology
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-7137DOI: 10.1109/IROS.2008.4650911ISBN: 978-1-4244-2057-5 (print)OAI: oai:DiVA.org:oru-7137DiVA: diva2:221522
Conference
IEEE/RSJ international conference on intelligent robots and systems, IROS 2008, Nice, France
Available from: 2009-06-04 Created: 2009-06-04 Last updated: 2016-08-10Bibliographically approved
In thesis
1. Unmanned operation of load-haul-dump vehicles in mining environments
Open this publication in new window or tab >>Unmanned operation of load-haul-dump vehicles in mining environments
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Underground mines typically do not represent the best working conditions for humans, and many mining companies have the intent to remove all humans from the ore extraction areas. To achieve this goal automation of the mining machinery is required. One of the riskier jobs in a mine is to operate the Load-Haul-Dump (LHD) vehicles that are used to transport the ore from the blast site to a truck, lorry or directly to a crusher. Today these vehicles are typically controlled by an on-board manual operator. The purpose of the work presented in this thesis is to develop and evaluate algorithms and methods to enable high productivity unmanned operation of LHDs, including two different operating modes.

The first mode is fully autonomous navigation, applicable to static environments, where the LHDs are repeatedly driven along the same paths for extended periods. Here, an existing framework for reactive navigation based on fuzzy logic has been extended with novel feature detection algorithms for tunnel following and topological localisation based on 2D laser range scanner data. These algorithms have been verified in quantitative tests to be fast, reliable and tolerant to noise in the sensor data. Moreover, the whole navigation system has been evaluated in qualitative tests in indoor environments using an ordinary research robot. The autonomous navigation system for LHDs currently commercialized by Atlas Copco is partly based on the experiences gained from the work presented here.

The second mode explored is semi-autonomous operation, where local-autonomy functionality on-board the machine assists a tele-remote operator in driving the vehicle along a collision-free path. This mode is intended for mines where the driving path of the machine changes frequently, so the setup needed for a fully autonomous system becomes impractical. In this part of the work a user study in a real mine has been performed, showing that local autonomy has the potential to significantly improve the productivity of a tele-remote operated LHD. Based on these results, a commercial tele-operating system for underground mines has been extended with a novel local autonomy functionality, inspired by existing autonomous navigation systems. The performance of this system has been verified in experiments performed on a real 38 tonnes LHD in a test mine, and in simulations aimed to show that the system works in arbitrary underground mine environments.

Place, publisher, year, edition, pages
Örebro: Örebro university, 2011. 51 p.
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 51
National Category
Computer and Information Science
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-22264 (URN)
Public defence
2011-12-14, Hörsal T, Örebro universitet, Fakultetsgatan 1, Örebro, 13:15 (Swedish)
Opponent
Supervisors
Note
Prof. Alessandro Saffiotti is a member of Centre for Applied Autonomous Sensor Systems (AASS) , Örebro UniversityAvailable from: 2012-03-28 Created: 2012-03-28 Last updated: 2012-03-28Bibliographically approved

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Publisher's full texthttp://www.aass.oru.se/~asaffio/Papers/iros08c.html

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