Carnegie Mellon University
Ergodic Coverage and Active Search in Constrained Environments
In this thesis, we explore sampling-based trajectory optimization applied to search for objects of interest in constrained environments (e.g., a UAV searching for a target in the presence of obstacles). We consider two search scenarios: in the first scenario, accurate prior information distribution of the possible locations of the objects of interest is available, thus [...]
The 2018 Robotics Institute Semi-formal
Robotics Institute members and a guest are invited to join us for our annual semi-formal! Join us for an evening of music, fun, food and friends! Food and beverage will include: hot hors d'oeuvres, stations for carving, pasta, fruit, cheese, coffee and dessert and hosted non-alcoholic beverages. Cash bar. We will have a dj for [...]
Carnegie Mellon University
Analysis of Spatio-Temporally Varying Features in Optical Coherence Tomographic (OCT) and Ultrasound (US) Image Sequences
Abstract: Optical Coherence Tomography (OCT) and Ultrasound (US) are non-ionizing and non-invasive imaging modalities that are clinically used to visualize anatomical structures in the body. OCT has been widely adopted in clinical practice due to its micron-scale resolution to visualize in-vivo structures of the eye. Ultra-High Frequency Ultrasound (UHFUS) can capture images at a depth [...]
Carnegie Mellon University
Planning for Energy-Efficient Coverage and Exploratory Deviation by Robots in Rivers
Abstract: Manual collection of environmental data over a large area can be a time-consuming, costly, and even dangerous process, making it a perfect candidate for automation with mobile robots. Despite this clear suitability and numerous advances in robotics resulting in decreased costs, improved reliability, and increased ease of use, the problem of powering autonomous robots [...]
Carnegie Mellon University
Learning to Learn for Small Sample Visual Recognition
Abstract: Understanding how humans and machines recognize novel visual concepts from few examples remains a fundamental challenge. Humans are remarkably able to grasp a new concept and make meaningful generalization from just few examples. By contrast, state-of-the-art machine learning techniques and visual recognition systems typically require thousands of training examples and often break down if [...]
Composable Benchmarks for Safe Motion Planning on Roads
Abstract Numerical experiments for motion planning of road vehicles require numerous components: vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. Providing a description of the numerical experiment precise enough to reproduce it might require several pages of information. Thus, only key aspects are [...]
Carnegie Mellon University
Understanding Machine Vision through Human Vision
Abstract: Recent success in machine vision has been largely driven by advanced computer vision methods, most commonly known as deep learning based methods. While we have seen tremendous performance improvements in machine visual tasks, such as object categorization and segmentation, there remain two major issues in deep learning. Firstly, deep networks have been largely unable [...]
Model Predictive Path Following for Wheeled Mobile Robots
Abstract: The navigation success of a wheeled mobile robotic mission is directly correlated to the degree of accuracy to which the robot can follow a given path. This, in turn, is largely affected by two factors: a) the environment and b) the intrinsic properties of the robot – its design, actuation mechanism etc. In the [...]