We have been at IROS 2017 this week, where Lukas Dekker did a fantastic job presenting our work on the combination of ILC and feedback linearization for path following as applied to large vehicles. Check out the paper Industrial-scale autonomous wheeled-vehicle path following by combining iterative learning control with feedback linearization, which was a collaboration between MSL and the Rocktec Automation Division of Atlas Copco Rock Drills AB in Örebro, Sweden.
Congratulations to MSL alumnus Marc Gallant, now with Quanergy Systems, who was recently nominated by Queen’s University’s Department of Electrical and Computer Engineering for the prestigious Governor General’s Academic Gold Medal for his research in robotic geotechnical and his thesis Axis Mapping: The Estimation of Surface Orientations and its Applications in Vehicle Localization and Structural Geology.
Congrats to MSL’s Jordan Mitchell for placing 3rd in this year’s poster competition at the 2017 SME Annual Conference & Expo and CMA’s 119th National Western Mining Conference & Exhibition last week in Denver, CO! Jordan also recently placed 1st at the 2017 CIM Conference & Exhibition‘s student poster competition in Montreal, QC.
This year (August 2016 to July 2017) several MSL researchers including Joshua Marshall, Heshan Fernando, Jordan Mitchell, and Lukas Dekker are working full-time from Örebro, Sweden! This work is part of a unique collaboration between Queen’s MSL, Örebro University‘s Centre for Applied Autonomous Sensor Systems, and Atlas Copco Rock Drill AB (Örebro, Sweden), and is jointly funded by NSERC in Canada and the Swedish Knowledge Foundation.
We are now half-way there and so far we have had some really great success. Our achievements are in no small part due to generous and unprecedented access to Atlas Copco’s Kvarntorp underground test mine and use of their fully-equipped and automation-ready ST14 and ST18 LHD machines! We have also made some wonderful new friends and are looking forward to many years of continued work together.
MapKey Auto-Rotating Cavity Scanner
Jordan Mitchell is leading the development of our MapKey auto-rotating cavity scanner concept, prototyping, and initial field testing, which is happening on the Örebro University campus in collaboration with AASS.
J. M. Mitchell and J. A. Marshall. Design of a novel auto-rotating UAV platform for underground mine cavity surveying. To appear in Proceedings of the 2017 SME Annual Conference & Expo and CMA’s 119th National Western Mining Conference & Exhibition, Denver, CO, February 2017.
Auto-Tunable Robotic Loading
Heshan Fernando is leading the development of automatic tuning algorithms for autonomous loading of fragmented rock (see this CIM Magazine article about our ongoing work on this with Atlas Copco), which is happening in collaboration with Atlas Copco Rock Drills AB and in conjunction with field work at the Kvarntorp underground facility. Heshan is also working Atlas Copco engineers and software developers to create a “load-assist” version of our technology to help operators that use radio-remote controlled LHDs.
ILC+FBL for Fast Autonomous Driving
Lukas Dekker is leading research on a new approach to Iterative learning-based path following for high-accuracy and high-speed autonomous driving of underground mining vehicles. This work is being carried out using Atlas Copco ST14 and ST18 underground loaders and also at the Kvarntorp underground facility.
Collecting strike and dip measurements with a Brunton compass is tedious and time consuming. And, in some cases, can be dangerous if you have to get close to unsupported and/or newly excavated rock. There are other, less developed, ways of doing this by using camera (photogrammetry) or stationary LiDAR measurements, but there can big problems with these methods, including price, accuracy, and the need for significant human input (and error). Hence, these approaches are not widely used.
For several years now, MSL researchers Marc Gallant and Joshua Marshall have been developing a better way; one that is automatic, mobile, accurate (better than a human?!), safe, and extremely fast.
Introducing the Mining Systems Laboratory’s automated geotechnical mapping system. It provides a quick and easy way for geotechnical engineers or geologists to automatically generate rich and complete stereonets that map the joint sets of exposed rock cuts, whether these are on surface, underground, or in hard-to-reach places.
This spring, with the support of PARTEQ Innovations, Marc and Josh decided to give some entrepreneurial Queen’s students (via the QICSI program) the chance to exploit their newly developed intellectual property. Six students took up the challenge and we are happy to report that, after forming the spin-off company RockMass Technologies, they recently won the QICSI pitch competition! Congrats to RockMass Technologies! We look forward to working with you on the future of robotic and automated geotechnical mapping …
Congrats to MSL’s Lukas Dekker for winning the NSERC Michael Smith Foreign Study Supplement. Lukas will study this coming year at Örebro University‘s Centre for Applied Autonomous Sensor Systems (AASS) and work on a robotic vehicle project in collaboration with Atlas Copco’s Rocktec Automation division in Örebro, Sweden.
Together with co-inventor Joshua Marshall, Mining Systems Laboratory (MSL) technology entrepreneurs Marc Gallant and Jordan Mitchell won 1st and 2nd place, respectively, at this year’s NCFRN Ogopogo event. The Ogopogo event is a Dragons’ Den-like event held annually as part of the NSERC’s NCFRN Annual Meeting, this year in Sudbury, Ontario at Laurentian University. Ogopogo refers to the lake monster of Okanagan Lake, where the first event was held in 2015. NCFRN is a national field robotics network, that brings together researchers from across the country to focus on robotics for challenging outdoor applications.
— Joshua Marshall (@queensprofessor) June 12, 2016
The winning business and technology pitch was given by AxisMapper, a robotic geotechnical tool that is the focus of Marc’s PhD work. Second place went to MapKey, a novel cavity scanning technology that is the focus of Jordan’s Master’s research. AxisMapper took home a $10,000 prize, which will be used to develop a demonstrator unit, as well as some business development and market studies. MapKey took home an $8,000 prize, which will be used to fund prototype development and deployment at an underground facility during the coming year. For more information about these technologies, contact Joshua Marshall.
A shout-out to Ryan Gariepy at Clearpath for a great job as event MC!
Introducing the Mining Systems Laboratory’s automated geotechnical mapping system. It provides a quick and easy way for geotechnical engineers or geologists to automatically generate rich and complete stereonets that map the joint sets of exposed rock cuts, whether these are on surface, underground, or in hard-to-reach places. Our system is lightweight, mobile, fast, and accurate.
M. J. Gallant and J. A. Marshall. Automated rapid mapping of joint orientations with mobile LiDAR. In the International Journal of Rock Mechanics and Mining Sciences, vol. 90, pp. 1-14, December 2016. DOI: 10.1016/j.ijrmms.2016.09.014
M. J. Gallant and J. A. Marshall. The LiDAR Compass: Extremely lightweight heading estimation with axis maps. To appear in Robotics and Autonomous Systems, available online May 2016.
M. J. Gallant and J. A. Marshall. Automated three-dimensional axis mapping with a mobile platform. In Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May 2016.
M. J. Gallant and J. A. Marshall. Two-dimensional axis mapping using LiDAR. In IEEE Transactions on Robotics, vol. 32, no. 1, pp. 150-160, January 2016.
M. J. Gallant, J. A. Marshall, and B. K. Lynch. Estimating the heading of a Husky mobile robot with a LiDAR compass based on direction maps. Invited paper in Proceedings of the 2014 International Conference on Intelligent Unmanned Systems, Montreal, QC, September 2014.
Funding for this research was provided by in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the NSERC Canadian Field Robotics Network (NCFRN).
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