Joshua Marshall, PhD, PEng, was born in Iqaluit (ᐃᖃᓗᐃᑦ) and was raised in St. Lucia (Caribbean), Edmonton, Winnipeg, and North Bay. He is a multi-disciplinary engineering scientist, educator, and technical consultant with expertise in systems control engineering, mobile robotics, autonomous vehicle navigation and mapping. He has a special interest in and experience with application heavy vehicle automation, particularly in mining, space, marine, and defence, as well as in other harsh-environment applications.
Dr. Marshall earned his doctoral degree in electrical and computer engineering, specializing in systems control, from the University of Toronto in 2005 under the supervision of Profs. Bruce Francis and Mireille Broucke. He joined Queen’s University in 2010 and has been instrumental in building the multidisciplinary Offroad Robotics research group and, most recently, leading the collaborative robotics and artificial intelligence-focused Ingenuity Labs Research Institute as its inaugrual Director. At Queen’s, Dr. Marshall is also cross-appointed to the Department of Mechanical & Materials Engineering and to The Robert M. Buchan Department of Mining where he supervises graduate research. He has served as a departmental Graduate Admissions Coordinator (2019-present) and as Associate Head of department (2017-19).
Dr. Marshall is also a founding member of the NSERC Canadian Robotics Network (NCRN). In 2016-17 he was the KKS International Visiting Professor of Computer Science at the Centre for Applied Autonomous Sensor Systems (AASS) in the School of Science and Technology at Örebro University, Sweden. Prior to joining Queen’s, Dr. Marshall was an Assistant Professor in the Department of Mechanical & Aerospace Engineering at Carleton University where he lead the Robotic Vehicles Group. Before that, he served as an R&D/Control Systems Engineer on both space and terrestrial robotics projects at the robotics firm MacDonald, Dettwiler, and Associates (MDA), Inc., where he worked on field and mobile robotics projects for industry clients in Canada, the US, Australia, and Sweden.
Dr. Marshall is a Senior Member of the IEEE. He served (2019-2022) as a Technical Editor for the IEEE/ASME Transactions on Mechatronics and currently serves as an Associate Editor for the International Journal of Robotics Research (IJRR). He was an elected Associate Editor from 2017-20 to the Conference Editorial Board (CEB) of the IEEE Control Systems Society (CSS) and as an Editor for the IEEE/RSJ International Conference on Robots and Systems (IROS) from 2017-21. He has also served on three occasions as co-organizer of the Control and Robotics Symposium of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) and, from 2018-20, as a member of the IEEE Medal for Environmental & Safety Technologies Committee. Dr. Marshall’s work is featured in the From Earth to Us exhibit at the Canada Science and Technology Museum and has been commercialized through companies such as Epiroc AB (formerly Atlas Copco AB) and spin-off RockMass Technologies, Inc. Dr. Marshall is a licensed Professional Engineer in the province of Ontario and has served as a Program Visitor and Vice-Chair on multiple program accreditation site visits for the Canadian Engineering Accreditation Board (CEAB) of Engineers Canada.
Josh and his wife Jill live in Kingston where they are the proud parents of Owen, Maeve, and Finn.
For more information about Dr. Marshall and his research, visit the Offroad Robotics website and the Ingenuity Labs website. You can also find him as @botprof@robotics-ai.social on Mastodon.
Dr. Marshall is part of the Robotics, Intelligent Systems, and Biomedical Engineering research team in ECE and a faculty member at the Ingenuity Labs Research Institute. He is also a founding member of the NSERC Canadian Robotics Network (NCRN) and the NSERC CREATE for Building Trust in Connected and Autonomous Vehicles. For more information about Dr. Marshall's research activities, visit the Offroad Robotics webpage.
ELEC 845 Autonomous Vehicle Control & Navigation (W 2020, F 2020, W 2022, W 2023)
MREN 203 Mechatronics & Robotics Design II (W 2023)
ELEC 299 Mechatronics Project (W 2022)
ELEC 443 Linear Control Systems (F 2019)
Currently faculty advisor (with B. Surgenor and K. Hashtrudi-Zaad) for the Queen's AutoDrive Challenge II Team in the SAE/GM AutoDrive II Challenge competition.
Previous (2010-2021) faculty advisor for the Queen's Space Engineering Team (QSET).
MINE 855 Autonomous Ground Vehicles Engineering (W 2018-19)
MINE 472 Mining Systems, Automation & Robotics (W 2016-19)
MINE 471 Mine-Mechanical Design Project (W 2012, FW 2012-13, W 2014-16, 2018-19)
APSC 200 Engineering Design and Practice II (F 2011-13, 2018)
MINE 202 Instrumentation & Computer Applications in Mining (F 2010-13, 2017, 2018)
MINE 853 Mining Robotics (W 2012-16)
MINE 459 Reliability, Maintenance, & Risk Assessment (F 2014-15)
MINE 201 Introduction to Mining & Mineral Processing (F 2011-15)
MINE 852 Mine Mechanization & Automation (W 2011)
MECH 5504 Guidance, Navigation, & Control (W 2008-10 at Carleton)
ECOR 1010 Introduction to Engineering (F 2008-09 at Carleton)
MAAE 4500 Feedback Control Systems (F 2008 at Carleton)
For a full list of Dr. Marshall's publications, visit the Offroad Robotics webpage and QSpace for free pre-prints.
T. M. C. Sears, M. R. Cooper, and J. A. Marshall. Mapping waves with an uncrewed surface vessel via Gaussian process regression. Proceedings of the 2023 IEEE International Conference on Robotics & Automation (ICRA), London, UK, May-June 2023. [IEEE Xplore, Preprint PDF]
D. Sacoransky, K. Hashtrudi-Zaad, and J. A. Marshall. Towards unsupervised filtering of millimetre-wave radar returns for autonomous vehicle road following. Proceedings of the 2023 IEEE International Conference on Robotics & Automation (ICRA), London, UK, May-June 2023. [IEEE Xplore, Preprint PDF]
J. Silveira, K. Cabral, S. Givigi, and J. A. Marshall. Real-time fast marching tree for mobile robot motion planning in dynamic environments. Proceedings of the 2023 IEEE International Conference on Robotics & Automation (ICRA), London, UK, May-June 2023. [IEEE Xplore]
J. Wang, M. Fader, and J. A. Marshall. Learning-based model predictive control for improved mobile robot path following using Gaussian processes and feedback linearization. To appear in Journal of Field Robotics, accepted January 31, 2023. DOI: 10.1002/rob.22165
L. Antonyshyn, J. Silveira, S. Givigi, and J. A. Marshall. Multiple mobile robot task and motion planning: A survey. In Computing Surveys, accepted September 14, 2022. DOI: 10.1145/3564696
L. Khaleghi, A. Sepas-Moghaddam, J. A. Marshall, and A. Etemad. Multi-view video-based 3D hand pose estimation. In IEEE Transactions on Artificial Intelligence, accepted July 29, 2022. DOI: 10.1109/TAI.2022.3195968
A. Farley, J. Wang, and J. A. Marshall. How to pick a mobile robot simulator: A quantitative comparison of CoppeliaSim, Gazebo, MORSE and Webots with a focus on accuracy of motion. In Simulation Modelling Practice and Theory, vol. 120, November, 2022. DOI: 10.1016/j.simpat.2022.102629
T. M. C. Sears and J. A. Marshall. Mapping of spatiotemporal scalar fields by mobile robots using Gaussian process regression. In Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, October 2022.
L. Khaleghi, J. A. Marshall, and A. Etemad. Exploiting sequential contexts using transformers for 3D hand pose estimation. In Proceedings of the 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, August 2022.
L. Khaleghi, U. Artan, A. Etemad, and J. A. Marshall. Touchless control of heavy equipment using low-cost hand gesture recognition. In the Special Issue on An End-to-end Machine Learning Perspective on Industrial IoT of the IEEE Internet of Things Magazine, vol. 5, no. 1, March 2022. DOI: 10.1109/IOTM.002.2200022.
J. Caldwell and J. A. Marshall. Towards efficient learning-based model predictive control via feedback linearization and Gaussian process regression. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, September 27, 2021. DOI: 10.1109/IROS51168.2021.9636755
A. Greisman, K. Hashtrudi-Zaad, and J. A. Marshall. Detection of conductive lane markers using mmWave FMCW automotive radar. In Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems(MFI), Karlsruhe, Germany, September 23, 2021. DOI: 10.1109/MFI52462.2021.9591167
U. Artan, H. Fernando, and J. A. Marshall. Automatic material classification via proprioceptive sensing and wavelet analysis during excavation. In Proceedings of the 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Delft, The Netherlands, July 12, 2021. DOI: 10.1109/AIM46487.2021.9517696
O. Mayuku, B. F. Surgenor, and J. A. Marshall. A self-supervised near-to-far approach for terrain-adaptive off-road autonomous driving. In Proceedings of the 2021 IEEE Conference on Robotics and Automation (ICRA), Xi’an, China, May 30, 2021. DOI: 10.1109/ICRA48506.2021.9562029
O. Mayuku, B. F. Surgenor, and J. A. Marshall. Multi-resolution and multi-domain analysis of off-road datasets for autonomous driving. In Proceedings of the 2021 Computer and Robot Vision Conference (CRV), Burnaby, BC, May 25, 2021. DOI: 10.1109/CRV52889.2021.00030
J. A. Marshall. The robot revolution is here: How it’s changing jobs and businesses in Canada. In, The Conversation, February 23, 2021 [Republished here in The National Post, February 24, 2021] [Republished here by Global News, February 27, 2021].
M. T. Ahmed, S. Ziauddin, J. A. Marshall, and M. Greenspan. Point cloud registration using virtual interest points from Macaulay’s resultant of quadric surfaces. In the Journal of Mathematical Imaging and Vision, January 2021. DOI: 10.1007/s10851-020-01013-z
H. Fernando and J. A. Marshall. What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning. In the special issue on Smart Operation of Heavy Construction Equipment in Automation in Construction, vol. 119, November, 2020. DOI: 10.1016/j.autcon.2020.103374
U. Artan and J. A. Marshall. Towards automatic classification of fragmented rock piles via proprioceptive sensing and wavelet analysis. In Proceedings of the 2020 IEEE Conference on Multisensor Fusion and Integration (MFI), Karlsruhe, Germany, September 2020. DOI: 10.1109/MFI49285.2020.9235261
J. von Tiesenhausen, U. Artan, J. A. Marshall, and Q. Li. Hand gesture-based control of a front-end loader. In Proceedings of the 33rd IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), London, ON, May 2020. DOI: 10.1109/CCECE47787.2020.9255828
J. Marshall. Mining robotics. Invited chapter in the Springer Encyclopedia of Robotics. Published online April 12, 2020. DOI: 10.1007/978-3-642-41610-1_35-1
J. Mitchell and J. A. Marshall. Towards a novel auto-rotating UAV platform for cavity surveying. In Tunnelling and Underground Space Technology, vol. 97, March 2020. DOI: 10.1016/j.tust.2019.103260
H. Fernando, J. A. Marshall, and J. Larsson. Iterative learning-based admittance control for autonomous excavation. In Journal of Intelligent & Robotic Systems, vol. 96, no. 3-4, December 2019. DOI: 10.1007/s10846-019-00994-3
L. Dekker, J. A. Marshall, and J. Larsson. Experiments in feedback linearized iterative learning-based path following for center-articulated industrial vehicles. In Journal of Field Robotics, vol. 36, no. 5, August 2019. DOI: 10.1002/rob.21864
R. Hewitt, E. Boukas, M. Azkarate, M. Pagnamenta, J. A. Marshall, A. Gasteratos, and G. Visentin. The Katwijk beach planetary rover dataset. In The International Journal of Robotics Research, vol. 37, no. 1, pp. 3-12, January 2018. DOI: 10.1177/0278364917737153
L. G. Dekker, J. A. Marshall, and J. Larsson. Industrial-scale autonomous wheeled-vehicle path following by combining iterative learning control with feedback linearization. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, BC, September 2017. DOI: 10.1109/IROS.2017.8206089
H. Fernando, J. A. Marshall, H. Almqvist, and J. Larsson. Towards controlling bucket fill factor in robotic excavation by learning admittance control setpoints. In Proceedings of the 11th Conference on Field and Service Robotics (FSR 2017), Zürich, Switzerland, September 2017. DOI: 10.1007/978-3-319-67361-5
A. A. Dobson, J. A. Marshall, and J. Larsson. Admittance control for robotic loading: Design and experiments with a 1-tonne loader and a 14-tonne load-haul-dump machine. Invited paper in the special issue on Field and Service Robotics of the Journal of Field Robotics, vol. 34, no. 1, pp. 123-150, January 2017. DOI: 10.1002/rob.21654
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
J. A. Marshall, A. Bonchis, E. M. Nebot, and S. Scheding. Robotics in Mining. Invited Chapter 59, Part F, in the Springer Handbook of Robotics, 2nd edition, 2016. DOI: 10.1007/978-3-319-32552-1_59
E. Boukas, R. A. Hewitt, M. Pagnamenta, R. Nelen, M. Azkarate, J. A. Marshall, A. Gasteratos, and G. Visentin. HDPR: A mobile testbed for current and future rover technologies. In Proceedings of the 14th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2016), Beijing, China, June 2016.
M. J. Gallant and J. A. Marshall. The LiDAR Compass: Extremely lightweight heading estimation with axis maps. In Robotics and Autonomous Systems, vol. 82, pp. 35-45, August 2016. DOI: 10.1016/j.robot.2016.04.005
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. DOI: 10.1109/ICRA.2016.7487236
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. DOI: 10.1109/TRO.2015.2506162
E. Deretey, M. T. Ahmed, J. A. Marshall, and M. Greenspan. Visual indoor positioning using a single camera. In Proceedings of the 6th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015), pp. 1-9, Banff, AB, October 2015. DOI: 10.1109/IPIN.2015.7346756
R. Hewitt and J. A. Marshall. Towards intensity-augmented SLAM with LiDAR and ToF sensors. In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, September 2015. DOI: 10.1109/IROS.2015.7353634
A. A. Dobson, J. A. Marshall, and J. Larsson. Admittance control for robotic loading: Underground field trials with an LHD. In Proceedings of the 10th Conference on Field and Service Robotics (FSR), Toronto, ON, June 2015. Conference best paper award! DOI: 10.1007/978-3-319-27702-8_32
M. T. Ahmed, M. Mohamad, J. A. Marshall, and M. Greenspan. Registration of noisy point clouds using virtual interest points. In Proceedings of the 12th Conference on Computer and Robot Vision (CRV), Halifax, NS, June 2015. Best vision paper award! DOI: 10.1109/CRV.2015.12
Thomas Sears, PEng
Jefferson Silveira (with Dr. Sidney Givigi)
Sina Qahremani (with Prof. Keyvan Hashtrudi-Zaad)
Johan von Tiesenhausen (with Prof. Qingguo Li)
Emily Taylor
Francesco Maratto
M. Riley Cooper
Kai Wang (with Dr. Sidney Givigi)
Jude Diab (with Prof. Brian Surgenor)
Prof. Marshall is currently looking for two graduate students (Master's- or, preferably, PhD-level) to support a new collaborative project with MacLean Engineering on the development of a fully autonomous grader for civil and mining applications. This project is planned to start in May 2024.
If you are an exceptional student and interested in research, you are welcome to send inquiries to Joshua Marshall and complete this form. You will not receive a response unless you are under consideration for a position. If you are a prospective graduate student then you are also strongly encouraged to apply (early) for scholarship funding through NSERC, OGS, and others.
If you are an international student (i.e., not a Canadian citizen or landed immigrant): Due to the large number of applications that we receive, we kindly ask that you first apply online to the Queen’s School of Graduate Studies before we consider your application to join our research group. This allows us to more easily understand your qualifications.