Deborah C. Russell

dblp:33/5628 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 70% Human-AI interaction · 30%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction › automation
automation levels
0.112006
An investigation of real world control of robotic assets under communication latency · HRI 2006
Human-robot interaction › teleoperation
supervisory control
0.112006
An investigation of real world control of robotic assets under communication latency · HRI 2006
Human-robot interaction
teleoperation
0.112006
An investigation of real world control of robotic assets under communication latency · HRI 2006
YearPublicationVenuePosition
2006 An investigation of real world control of robotic assets under communication latency
abstract
Robots are already being used in a variety of applications, including the military battlefield. As robotic technology continues to advance, those applications will increase, as will the demands on the associated network communication links. Two experiments investigated the effects of communication latency on the control of a robot across four Levels Of Automation (LOAs), (1) full teleoperation, (2) guarded teleoperation, (3) autonomous obstacle avoidance, and (4) full autonomy. Latency parameters studied included latency duration, latency variability, and the "direction" in which the latency occurs, that is from user-to-robot or from robot-to-user. The results indicate that the higher the LOA, the better the performance in terms of both time and number of errors made, and also the more resistant to the degrading effects of latency. Subjective reports confirmed these findings. Implications of constant vs. variable-latency, user-to-robot vs. robot-to-user latency, and latency duration are also discussed.
Jason P. Luck, Patricia L. McDermott, Laurel Allender, Deborah C. Russell
HRI4