Offroad Robotics
Offroad Robotics is a multidisciplinary research group at Queen’s University. Our researchers have expertise in mining, mechanical, and electrical & computer engineering, and are passionate about field robotics, mechatronics, and systems control.
This community includes research outputs produced by faculty and students. Submitting works to QSpace may enable compliance with the Tri-Agency Open Access Policy on Publications.
When you submit your work to QSpace, you retain copyright and grant the Library a non-exclusive license to distribute and preserve. Works are open access unless restricted by the creator.
Collections in this community
Recent Submissions
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Mapping waves with an uncrewed surface vessel via Gaussian process regression
(IEEE, 2023-05-29)Mobile robots are well suited for environmental surveys because they can travel to any area of interest and react to observations without the need for pre-existing infras- tructure or significant setup time. However, vehicle ... -
Towards unsupervised filtering of millimetre-wave radar returns for autonomous vehicle road following
(IEEE, 2023-03-01)Path planning and localization in low-light and inclement weather conditions are critical problems facing autonomous vehicle systems. Our proposed method applies a single modality, millimetre-wave radar perception system ... -
Mapping of spatiotemporal scalar fields by mobile robots using Gaussian process regression
(IEEE, 2022-10)Spatiotemporal maps are data-driven estimates of time changing phenomena. For environmental science, rather than collect data from an array of static sensors, a mobile sensor platform could reduce setup time and cost, ... -
The robot revolution is here: How it’s changing jobs and businesses in Canada
(The Conversation, 2021-02-23)The future of automated labour may not spell the end of human employment. -
Detection of conductive lane markers using mmWave FMCW automotive radar
(IEEE, 2021-09-23)Localization of vehicles in inclement weather conditions, including snow and heavy rain, is a significant issue plaguing autonomous vehicle systems. Our work takes a step towards tackling this problem by leveraging existing ... -
Towards efficient learning-based model predictive control via feedback linearization and Gaussian process regression
(IEEE, 2021-09)This paper presents a learning-based Model Pre- dictive Control (MPC) methodology incorporating nonlinear predictions with robotics applications in mind. In particular, MPC is combined with feedback linearization for ... -
Automatic material classification via proprioceptive sensing and wavelet analysis during excavation
(IEEE, 2021-07-12)This paper presents an excavation material classification methodology that uses wavelet analysis and unsupervised learning on acceleration measurements. The technique was validated by using acceleration data that were ... -
Multi-resolution and multi-domain analysis of off-road datasets for autonomous driving
(IEEE, 2021-07-05)For use in off-road autonomous driving applications, we propose and study the use of multi-resolution local binary pat- tern texture descriptors to improve overall semantic segmentation performance and reduce class imbalance ... -
A self-supervised near-to-far approach for terrain-adaptive off-road autonomous driving
(IEEE, 2021-07-05)For use in off-road autonomous driving applications, we propose and study the use of multi-resolution local binary pattern texture descriptors to improve overall semantic segmentation performance and reduce class imbalance ... -
Experiments in feedback linearized iterative learning‐based path following for center‐articulated industrial vehicles
(John Wiley & Sons, Inc., 2019-02-13)This paper describes the design, industrial application, and field testing of a technique for autonomous wheeled‐vehicle path following that uses iterative learning control (ILC) in a feedback linearized space. One advantage ... -
What Lies Beneath: Material Classification for Autonomous Excavators using Proprioceptive Force Sensing and Machine Learning
(Elsevier B.V., 2020-11)The ability of robotic excavators to acquire meaningful knowledge about materials during digging can augment their autonomous functionality, as well as optimize downstream operations in construction and mining. Some material ... -
Towards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysis
(IEEE, 2020-09)In this paper, we describe a method for classifying rock piles characterized by different size distributions by using accelerometer data and wavelet analysis. Size distribution (frag-mentation) estimates are used in the ... -
Towards a novel auto-rotating LiDAR platform for cavity surveying
(Elsevier, 2020-01-09)This paper presents the conceptual design, construction, and preliminary testing of a novel auto-rotating platform for cavity surveying. Cavity surveying involves generating a 3D model of an opening from acquired point ... -
Hand gesture-based control of a front-end loader
(IEEE, 2020-05)In this paper, we present the design and use of an instrumented glove consisting of a 9-DOF inertial measurement unit (IMU) and resistive flex sensors. The glove is used as a unique human-machine interface to control a ... -
Mining robotics
(Springer, Berlin, Heidelberg, 2020-04-12)Mining robotics refers to the full automation, semiautomation, or remote control of mining equipment, including both fixed and mobile machines, toward improving the feasibility, safety, productivity, and efficiency of ... -
Iterative Learning-Based Admittance Control for Autonomous Excavation
(Springer Netherlands, 2019-02-07)This paper presents the development and field validation of an iterative learning-based admittance control algorithm for autonomous excavation in fragmented rock using robotic wheel loaders. An admittance control strategy ... -
Robotics in Mining
(Springer, 2016)This chapter presents an overview of the state of the art in mining robotics, from surface to underground applications, and beyond. Mining is the practice of extracting resources for utilitarian purposes. Today, the ... -
Industrial-Scale Autonomous Wheeled-Vehicle Path Following by Combining Iterative Learning Control with Feedback Linearization
(2017-09-30)Abstract— This paper presents a path following method for autonomous wheeled vehicles that combines iterative learning control (ILC) with nonlinear feedback linearization (FBL) to provide anticipatory control action ... -
Towards Controlling Bucket Fill Factor in Robotic Excavation by Learning Admittance Control Setpoints
(2017-09-30)This paper investigates the extension of an admittance control scheme to- ward learning and adaptation of its setpoints to achieve controllable bucket fill factor for robotic excavation of fragmented rock. A previously ... -
Mobile LiDAR-Based Convergence Detection in Underground Tunnel Environments
(2017-04-06)This paper presents a mobile LiDAR-based method for remotely identifying convergence (i.e., naturally occurring deformation) in excavated underground tunnel environments. A mobile LiDAR system is used to collect and generate ...
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