Upcoming Doctoral Dissertations at the Schaefer School

"Underwater robotics is an under-researched field due to various barriers to entry, including the harsh environment, expensive hardware, and complex control challenges. Nevertheless, underwater robots hold significant value due to their ability to perform surveying and intervention tasks in environments that are hazardous and inaccessible to humans. This dissertation presents four novel contributions aimed at lowering these barriers through the development of low-cost and open-source underwater robot manipulator designs, along with advancements in modeling, planning, and control." Read more...

"In today’s rapidly growing urban environments, ensuring the resilience and efficiency of traffic systems is crucial not only for smooth daily mobility but also for effective responses during extreme events. Disruptions caused by natural disasters, large-scale incidents, and localized events can lead to significant congestion and severely affect emergency response operations. Consequently, timely and reliable detection of such disruptions is vital for effective traffic management. This dissertation introduces innovative methodologies aimed at enhancing traffic monitoring and anomaly detection in urban networks during these events, along with a targeted approach to improving the timely detection of incidents on freeways. " Read more...

"The systems engineering and product development community can benefit from a methodology that can significantly reduce the likelihood of engineering rework when decisions are made within the context of moderate to high information uncertainty. This condition is predominant in the concept design stage and early lifecycle development stages when data is scarce, model fidelity is low, and stakeholder needs and requirements are expected to change. Often product design solutions under these conditions turn out to be inadequate in that they do not meet customer needs, product performance and/or product costs. Conceptually, set-based design (SBD) can address these challenges but unfortunately the time it takes to obtain a solution set that achieves high operational performance is too long which prevents the complete application of SBD. This research developed a novel SBD framework that increases the likelihood that a design engineer develops a set of design alternatives that achieve effective operational performance with resource constraints under epistemic uncertainty in less time and fewer iterations than previous methods. The SBD framework was developed around addressing three key areas: 1) iterate between distributed design teams due to conflict between shared attributes during the integrate by intersection and establish feasibility before commitment principles of the set-based concurrent engineering (SBCE) process, 2) iterate when information and requirements change, and 3) iterate due to misclassification." Read more...

"Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) systems encounter considerable challenges in demanding environments, making advancements in this area crucial. Integrating a single camera with an Inertial Measurement Unit (IMU) in Visual-Inertial Navigation Systems (VINS) offers a cost-effective, low-power solution suitable for robot perception and AR/VR applications. The camera captures rich environmental details, while the IMU measures acceleration and angular velocity, enhancing system resilience in fast-motion or low-texture scenarios. This sensor fusion allows for complementary benefits, though achieving precise and dense 3D reconstruction in challenging conditions remains an unresolved issue." Read more...

"Camera pose estimation aims to localize an observer w.r.t. a known geometric reference and has a range of applications from autonomous navigation to virtual/augmented reality. Motivated by the ubiquity of digital cameras deploying rolling shutter (RS) hardware, pose estimation modules have been extended from the pinhole camera model to incorporate the dynamic 1D capture characteristics of these sensors. However, absolute pose estimation from single RS scanline inputs has been hitherto ignored in the literature. This omission may be attributed to the limited geometric context available for instantaneous RS capture and stringent latency requirements involved in real-time system operation. " Read more...

"Understanding complex neuronal firing patterns and interactions between neural circuits at different brain regions is essential for uncovering the mechanisms of brain function and dysfunctions. Electrophysiological Source Imaging (ESI) refers to reconstructing underlying cortical and subcortical electrical activities from electroencephalography (EEG) or magnetoencephalography (MEG) recordings. ESI is crucial for both neuroscience research and clinical applications, serving as an essential tool for capturing brain source signals with high temporal resolution. However, solving the ESI inverse problem remains challenging due to its illposed nature. To obtain a unique solution, traditional algorithms emphasize incorporating predesigned neurophysiological priors to restrict the solution space, while deep learning frameworks aim to directly learn the mapping from scalp EEG/MEG measurements to underlying brain sources in a data-driven manner, eliminating the need for handcrafted priors. Building on this, this dissertation proposes several ESI algorithms designed to capture the complexities of brain activity by leveraging sparse and graph-structured EEG/MEG signals, especially in cases involving extended source activities." Read more...

"Typically, operation tasks within the power system field follow a predict-then-optimize framework, in which machine learning (ML) methods are first trained to predict key parameters and then optimization models use these predictions as inputs to determine optimal operational decisions. For instance, renewable energy availability is predicted to serve as inputs for day-ahead operation models. The ultimate goal of such a predict-then-optimize process is to achieve the best operation economics associated with the optimal operation tasks, e.g., minimum operation cost or maximum operation revenue." Read more...