Visual Spacecraft Relative Motion Control using Higher Order Geometric Moments

This thesis studies passive visual relative control for satellites. The three main problems studied are how to keep the camera pointing at the center of the target, how to move the satellite so the camera can look at the target from a perpendicular orientation or maintain a fixed orientation, and how to maintain a nominal distance from the target. Visual target tracking within the image uses a statistical pressure snake algorithm, which tracks the outer contour of a target and allows geometric moments to be calculated. The attitude control is based off of tracking the center of the visual target, which is the first moment. The attitude control can treat the inertial target angular velocities as disturbances in the control for slow, smooth reference motions and still be stable. The skewness coefficient g3, which is a non-dimensional form of the third moment, can be used as a measure of perpendicularity when certain information is known about the target. A 640x480 pixel resolution camera is used to obtain better skewness and perpendicularity information than the human eye. A distance control does not need to assume the absolute distance, but can maintain a nominal distance by using the image inertia, or second moment information

Estimation of Gravitation Parameters of Saturnian Moons Using Cassini Attitude Control Flight Data

A major science objective of the Cassini mission is to study Saturnian satellites. The gravitational properties of each Saturnian moon is of interest not only to scientists but also to attitude control engineers. When the Cassini spacecraft flies close to a moon, a gravity gradient torque is exerted on the spacecraft due to the mass of the moon. The gravity gradient torque will alter the spin rates of the reaction wheels (RWA). The change of each reaction wheel's spin rate might lead to overspeed issues or operating the wheel bearings in an undesirable boundary lubrication condition. Hence, it is imperative to understand how the gravity gradient torque caused by a moon will affect the reaction wheels in order to protect the health of the hardware. The attitude control telemetry from low-altitude flybys of Saturn's moons can be used to estimate the gravitational parameter of the moon or the distance between the centers of mass of Cassini and the moon. Flight data from several low altitude flybys of three Saturnian moons, Dione, Rhea, and Enceladus, were used to estimate the gravitational parameters of these moons. Results are compared with values given in the literature

Humans teaching intelligent agents with verbal instruction

The widespread integration of robotics into everyday life requires significant improvement in the underlying machine learning (ML) agents to make them more accessible, customizable, and intuitive for ordinary individuals to interact with. As part of a larger field of interactive machine learning (IML), this dissertation aims to create intelligent agents that can easily be taught by individuals with no specialized training, using an intuitive teaching method such as critique, demonstrations, or explanations. It is imperative for researchers to be aware of how design decisions affect the human’s experience because individuals who experience frustration while interacting with a robot are unlikely to continue or repeat the interaction in the future. Instead of asking how to train a person to use software, this research asks how to design software agents so they can be easily trained by people. When creating a robotic system, designers must make numerous decisions concerning the mobility, morphology, intelligence, and interaction of the robot. This dissertation focuses on the design of the interaction between a human and intelligent agent, specifically an agent that learns from a human’s verbal instructions. Most research concerning interaction algorithms aims to improve the traditional ML metrics of the agent, such as cumulative reward and training time, while neglecting the human experience. My work demonstrates that decisions made during the design of interaction algorithms impact the human’s satisfaction with the ML agent. I propose a series of design recommendations that researchers should consider when creating IML algorithms. This dissertation makes the following contributions to the field of Interactive Machine Learning: (1) design recommendations for IML algorithms to allow researchers to create algorithms with a positive human-agent interaction; (2) two new IML algorithms to foster a pleasant user-experience; (3) a 3-step design and verification process for IML algorithms using human factors; and (4) new methods for the application of NLP tools to IML.Ph.D