E
Elizabeth A. Croft
Researcher at Monash University
Publications - 202
Citations - 7527
Elizabeth A. Croft is an academic researcher from Monash University. The author has contributed to research in topics: Robot & Human–robot interaction. The author has an hindex of 34, co-authored 191 publications receiving 5888 citations. Previous affiliations of Elizabeth A. Croft include University of British Columbia & University of Batna.
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Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots
TL;DR: A literature review has been performed on the measurements of five key concepts in HRI: anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety, distilled into five consistent questionnaires using semantic differential scales.
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Jerk-bounded manipulator trajectory planning: design for real-time applications
S. Macfarlane,Elizabeth A. Croft +1 more
TL;DR: An online method for obtaining smooth, jerk-bounded trajectories has been developed and implemented and a method for blending these straight-line trajectories over a series of way points is also discussed.
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Smooth and time-optimal trajectory planning for industrial manipulators along specified paths
TL;DR: In this paper, a method for determining smooth and time-optimal path constrained trajectories for robotic manipulators is presented, where the desired smoothness of the trajectory is imposed through limits on the torque rates.
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Feed optimization for five-axis CNC machine tools with drive constraints
TL;DR: In this paper, a feed scheduling algorithm for CNC systems is presented to minimize the machining time for five-axis contour machining of sculptured surfaces, where the variation of the feed along the tool-path is expressed in a cubic B-spline form.
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Affective State Estimation for Human–Robot Interaction
Dana Kulic,Elizabeth A. Croft +1 more
TL;DR: The implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus, and the results of the HMM affective estimation are compared to a previously implemented fuzzy inference engine.