ItemTouchless control of heavy equipment using low-cost hand gesture recognition(IEEE, 2022-03-01) Khaleghi, Leyla; Artan, Unal; Etemad, Ali; Marshall, JoshuaHuman-machine interaction using remote hand gestures is becoming increasingly prevalent across various industries. However, their potential application to heavy construction equipment is often overlooked. This paper presents a robust and inexpensive hand gesture recognition system that was implemented and tested on a robotic 1-tonne wheel loader. The system uses an RGB camera paired with a laptop to process, in real time, hand gestures to control the loader. We first design 4 unique gestures for controlling the loader and then collect 26000 images to train and test a neural network for hand gesture recognition. Our system uses robust landmark detection using an off-the-shelf system prior to gesture recognition. We successfully controlled the loader to excavate in a rock pile by using the proposed hand gesture recognition system. ItemMapping waves with an uncrewed surface vessel via Gaussian process regression(IEEE, 2023-05-29) Sears, Thomas M. C.; Cooper, M. Riley; Marshall, Joshua A.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 motion constraints limit where and when measurements occur. This is challenging for a single vehicle observing a time-varying phenomenon, such as coastal waves, but the ability to generate a spatiotemporal map would have immediate scientific and engineering applications. In this paper, an uncrewed surface vessel (USV) was used to measure waves on the coast of Lake Ontario, Canada. Data were collected from a low-cost inertial measurement system onboard the USV and processed in an offline Gaussian process regression (GPR) workflow to create a spatiotemporal wave model. Frequency analysis of raw sensor data was used to best select and design kernel functions, and to initialize hyperparameters. The relative speed of the waves limited the ability to make complete wave reconstructions, but GPR captured the dominant periodic components of the waves despite irregularities in the signals. After optimization, the hyperparameters indicate a dominant signal with a wave period of 0.87 s, which concurs with ground truth estimates. ItemTowards unsupervised filtering of millimetre-wave radar returns for autonomous vehicle road following(IEEE, 2023-03-01) Sacoransky, Dean; Marshall, Joshua A.; Hashtrudi-Zaad, KeyvanPath 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 for the detection of roadside retro-reflectors. Radar-based perception tasks can be challenging to perform due to the sparse and noisy nature of radar data. We propose the use of an unsupervised learning approach for filtering radar point clouds through Density-Based Spatial Clustering of Applica- tions with Noise (DBSCAN). The DBSCAN algorithm segments retro-reflector points from noise points, thus providing the autonomous vehicle with a predicted path for the road ahead. We tested the approach via indoor experiments that make use of Continental’s ARS 408 radar, a mobile Husky A2000 robot, and a Vicon motion capture system for ground truth validation. The experimental results of the proposed system demonstrated a classification accuracy of 84.13 % and F1 score of 83.71 %. ItemMapping of spatiotemporal scalar fields by mobile robots using Gaussian process regression(IEEE, 2022-10) Sears, Thomas M C; Marshall, Joshua ASpatiotemporal 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, maintain flexibility to be deployed to any area of interest, and provide active feedback during observations. While promising, mapping is challenging with mobile sensors because vehicle constraints limit not only where, but also when observations can be made. By assuming spatial and temporal correlations in the data through kernel functions, this paper uses Gaussian process regression (GPR) to generate a maximum likelihood estimate of the phenomenon while also tracking the estimate uncertainty. Spatiotemporal mapping by GPR is simulated for a single fixed- path mobile robot observing a latent spatiotemporal scalar field. The learned spatiotemporal map captures the structure of the latent scalar field with the largest uncertainties in areas the robot never visited. ItemThe robot revolution is here: How it’s changing jobs and businesses in Canada(The Conversation, 2021-02-23) Marshall, JoshuaThe future of automated labour may not spell the end of human employment.