Analysis and Realization of a Dual-Nacelle Tiltrotor Aerial Vehicle

Unmanned aerial vehicles are a salient solution for rapid deployment in disaster relief, search and rescue, and warfare operations. In these scenarios, the agility, maneuverability and speed of the UAV are vital components towards saving human lives, successfully completing a mission, or stopping dangerous threats. Hence, a high speed, highly agile, and small footprint unmanned aerial vehicle capable of carrying minimal payloads would be the best suited design for completing the desired task. This thesis presents the design, analysis, and realization of a dual-nacelle tiltrotor unmanned aerial vehicle. The design of the dual-nacelle tiltrotor aerial vehicle utilizes two propellers for thrust with the ability to rotate the propellers about the sagittal plane to provide thrust vectoring. The dual-nacelle thrust vectoring of the aerial vehicle provides a slimmer profile, a smaller hover footprint, and allows for rapid aggressive maneuvers while maintaining a desired speed to quickly navigate through cluttered environments. The dynamic model of the dual-nacelle tiltrotor design was derived using the Newton-Euler method and a nonlinear PD controller was developed for spatial trajectory tracking. The dynamic model and nonlinear PD controller were implemented in Matlab Simulink using SimMechanics. The simulation verified the ability of the controlled tiltrotor to track a helical trajectory. To study the scalability of the design, two prototypes were developed: a micro scale tiltrotor prototype, 50mm wide and weighing 30g, and a large scale tiltrotor prototype, 0.5m wide and weighing 2.8kg. The micro scale tiltrotor has a 1.6:1 thrust to weight ratio with an estimated flight time of 6 mins in hover. The large scale tiltrotor has a 2.3:1 thrust to weight ratio with an estimated flight time of 4 mins in hover. A detailed realization of the tiltrotor prototypes is provided with discussions on mechanical design, fabrication, hardware selection, and software implementation. Both tiltrotor prototypes successfully demonstrated hovering, altitude, and yaw maneuvering while tethered and remotely controlled. The developed prototypes provide a framework for further research and development of control strategies for the aggressive maneuvering of underactuated tiltrotor aerial vehicles

Assessing Educational Needs of FIRST Robotics

This project is aimed at assessing the educational needs of students new to the FIRST Robotics Competition (FRC) and developing a set of requirements for an educational website. Using data collected by surveying students and mentors from the FRC community, this project provides recommendations for an online robotics learning resource designed to improve the retention rates of the competition through a support system for FRC Rookie teams

Realization of a Self-Reconfigurable Modular Robot

This project realized a self-reconfigurable modular robot for search & rescue applications. The module was designed to move independently and connect with other modules. A single module was roughly 3x3x6” and weighed 2lbs. The module had three degrees of freedom, giving it individual mobility and high system configurability. The small module size and untethered operation necessitated an innovative design and strategic placement of the microcontroller, wireless communication, motors & control systems, sensors, and battery. An external magnetic connection mechanism using electrically switchable permanent magnets was designed, allowing the modules to connect and disconnect repeatedly

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.Comment: 10 pages, 4 figure

Recruitment Constraints in Singapore's Fluted Giant Clam (Tridacna squamosa) Populations - A Dispersal Model Approach

10.1371/journal.pone.0058819PLoS ONE83

Progress in Contacting a-Si:H/c-Si Heterojunction Solar Cells and its Application to Interdigitaded Back Contact Structure

ID = 457International audienc