Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnaires

A significant challenge to measuring human-automation trust is the amount of construct proliferation, models, and questionnaires with highly variable validation. However, all agree that trust is a crucial element of technological acceptance, continued usage, fluency, and teamwork. Herein, we synthesize a consensus model for trust in human-automation interaction by performing a meta-analysis of validated and reliable trust survey instruments. To accomplish this objective, this work identifies the most frequently cited and best-validated human-automation and human-robot trust questionnaires, as well as the most well-established factors, which form the dimensions and antecedents of such trust. To reduce both confusion and construct proliferation, we provide a detailed mapping of terminology between questionnaires. Furthermore, we perform a meta-analysis of the regression models that emerged from those experiments which used multi-factorial survey instruments. Based on this meta-analysis, we demonstrate a convergent experimentally validated model of human-automation trust. This convergent model establishes an integrated framework for future research. It identifies the current boundaries of trust measurement and where further investigation is necessary. We close by discussing choosing and designing an appropriate trust survey instrument. By comparing, mapping, and analyzing well-constructed trust survey instruments, a consensus structure of trust in human-automation interaction is identified. Doing so discloses a more complete basis for measuring trust emerges that is widely applicable. It integrates the academic idea of trust with the colloquial, common-sense one. Given the increasingly recognized importance of trust, especially in human-automation interaction, this work leaves us better positioned to understand and measure it.Comment: 44 pages, 6 figures. Submitted, in part, to ACM Transactions on Human-Robot Interaction (THRI

Learning to Predict Intent from Gaze During Robotic Hand-Eye Coordination

Effective human-aware robots should anticipate their user’s intentions. During hand-eye coordination tasks, gaze often precedes hand motion and can serve as a powerful predictor for intent. However, cooperative tasks where a semi-autonomous robot serves as an extension of the human hand have rarely been studied in the context of hand-eye coordination. We hypothesize that accounting for anticipatory eye movements in addition to the movements of the robot will improve intent estimation. This research compares the application of various machine learning methods to intent prediction from gaze tracking data during robotic hand-eye coordination tasks. We found that with proper feature selection, accuracies exceeding 94% and AUC greater than 91% are achievable with several classification algorithms but that anticipatory gaze data did not improve intent prediction

Modular Aneutronic Fusion Engine

NASA’s JUNO mission will arrive at Jupiter in July 2016, after nearly five years in space. Since operational costs tend to rise with mission time, minimizing such times becomes a top priority. We present the conceptual design for a 10 MW aneutronic fusion engine with high exhaust velocities that would reduce transit time for a Jupiter mission to eighteen months and enable more challenging exploration missions in the solar system and beyond. 1

Direct Fusion Drive For Advanced Space Missions

Center) are collaborating on a breakthrough concept, the Direct Fusion Drive (DFD), a D – 3 He fueled fusionpowered rocket engine. The design is based on the Princeton Field-Reversed Configuration Reactor (PFRC) concept for a compact, clean, steady-state fusion reactor for power levels between 1 and 10 MW. Larger power levels can be accommodated using multiple engines. This technology enables missions such as human Mars orbiters, nuclear fusion powered space stations and high power robotic missions to the outer planets. This paper reviews the theory behind the design of compact and clean (aneutronic) fusion-powered rocket engines. The reactor employs a field-reversed magnetic field configuration (FRC) for plasma confinement, a novel radio-frequency (RF) method for plasma heating, and a unique method for control of propellant flow, producing a thrust of 30 N at a specific impulse of 2 × 10 4 s. The FRC has higher β (plasma pressure/magnetic energy density) than other magnetic plasma-confinement devices and a relatively simple linear solenoid magnet layout that permits control of propellant flow parameters. Higher β reduces the mass of the superconducting solenoidal coils needed for achieving the higher plasma temperatures required for aneutronic fusion reactions. Waste heat generated from the plasma’s bremsstrahlung and synchrotron radiation can be recycled to power the RF heating and other on-board systems. The unique characteristics of this reactor –with non-equilibrium plasma conditions –will produce five-fold lower levels of neutrons than from equilibrium conditions. Additionally, the reactor design permits a novel method for extractin

IAC-12,C4,7-C3.5,10 Modular Aneutronic Fusion Engine

A compact aneutronic fusion engine will enable more challenging exploration missions in the solar system. This engine uses a deuterium-helium-3 reaction to produce fusion energy by employing a novel field-reversed magnetic field configuration (FRC). The FRC has a simple linear solenoidal coil configuration yet generates higher plasma pressures for a given magnetic field than other designs. Waste heat generated from bremsstrahlung and synchrotron radiation is recycled to maintain the fusion temperature. The charged reaction products, augmented by additional propellant, are exhausted through a magnetic nozzle. As an example, we present a mission to deploy the James Webb Space Telescope from LEO to an L2 halo orbit using a one MW compact aneutronic fusion rocket engine. The engine produces 20 N of thrust with an exhaust velocity of 55 km/s and has a specific power of 0.77 kW/kg. I