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.
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
Robotic excavation machines are of interest in mining and construction, where the aim is remove operators from hazardous environments, improve machine utilization, productivity, and task repeatability, and to reduce maintenance costs. However, what makes robotic excavation challenging is the nature of the bucket-rock interactions. For example, the resistance faced by a bucket as it attempts to penetrate a rock pile may vary significantly depending upon the properties of the media (e.g., density and hardness), the rock pile geometry, and the distribution of particle sizes and shapes. Indeed, it would be very difficult to predetermine the exact nature of future bucket-rock interactions prior to the execution of any particular excavation operation.
Our focus is on excavation in fragmented rock, as is common in mining and construction, using a load-haul-dump (LHD) excavation machine. We have devised an admittance-based excavation control strategy that, in extensive field trials, has proven to be an effective approach to autonomous excavation.