Computer Engineering Commons^™

Characterizing And Predicting Human Visual Perception Of Unmanned Aerial Vehicle Gestures, Paul Fletcher

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Unmanned Aerial Vehicles (UAVs) are being used in public domains and hazardous environments where effective communication strategies are critical. UAV gesture techniques have been shown to communicate meaning to human observers and may be ideal in contexts that require lightweight systems such as unmanned aerial flight, however, this work may be limited to an idealized range of viewer perspectives. As gesture is a visual communication technique it is necessary to consider how the perception of a robot gesture may suffer from obfuscation or self-occlusion from some viewpoints. This thesis presents the results of three online user-studies that examine participants’ ability …

Aerial Flight Paths For Communication, Alisha Bevins

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

This body of work presents an iterative process of refinement to understand naive perception of communication using the motion of an unmanned aerial vehicle (UAV). This includes what people believe the UAV is trying to communicate, and how they expect to respond through physical action or emotional response. Previous work in this area sought to communicate without clear definitions of the states attempting to be conveyed. In an attempt to present more concrete states and better understand specific motion perception, this work goes through multiple iterations of state elicitation and label assignment. The lessons learned in this work will be …

Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …

Autonomous Uavs For Near Earth Environmental Sensing, David J. Anthony

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Unmanned aerial vehicles (UAVs) are playing an increasing role in large scale environmental monitoring. Small UAVs are increasingly used to monitor agricultural fields, infrastructure projects, and disaster areas. The combination of their sensors, ease of use, and portability make them an ideal tool for collecting information on demand about geographic regions. These small UAVs do have several significant limitations. The UAVs have very limited autonomy and fly pre-determined flight paths far above the underlying terrain, limiting the spatial resolution of the collected data. Current battery technology severely limits their flight times, which in turn limits the temporal resolution of the …

On Problematic Robotic Thresholds, Adam K. Taylor

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Large configuration spaces present difficulties for developers validating large soft- ware systems and for users selecting the proper configuration to achieve the desired runtime behavior. Robot systems face the same challenges as they may have hundreds of configurable parameters. Our work focuses on co-robotic systems, those in which robots and humans work closely together to augment each other’s capabilities. We aim to leverage the user’s knowledge about a system to help determine configuration errors. To accomplish this, users mark runtime failures while observing the system in operation. A marked error indicates the robot “did something when it should not have” …

Invariant Inferring And Monitoring In Robotic Systems, Hengle Jiang

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

System monitoring can help to detect abnormalities and avoid failures. Crafting monitors for today’s robotic systems, however, can be very difficult due to the systems’ inherent complexity and its rich operating environment.

In this work we address this challenge through an approach that automatically infers system invariants and synthesizes those invariants into monitors. This approach is inspired by existing software engineering approaches for automated invariant inference, and it is novel in that it derives invariants by observing the messages passed between system nodes and the invariants types are tailored to match the spatial, time, temporal, and architectural attributes of robotic …