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Samuel J. McDonald

dblp:195/6877 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author

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 · 50% Haptics and multimodal interaction · 50%
Artificial intelligence
1 paper
Robot navigation and mapping · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Haptics and multimodal interaction
haptic feedback
0.312017
Haptic Shape-Based Management of Robot Teams in Cordon and Patrol · HRI 2017
Human-robot interaction › multi-robot systems
multi-robot management
0.312017
Haptic Shape-Based Management of Robot Teams in Cordon and Patrol · HRI 2017

Methods — techniques the papers use, named apart from their topics

user study · 0.6
YearPublicationVenuePosition
2017 Haptic Shape-Based Management of Robot Teams in Cordon and Patrol
abstract
There is a growing need to develop effective interaction methods that enable a single operator to manage a team of multiple robots. This paper presents a novel approach that involves treating the team as a moldable volume, in which deformations of the volume correspond to changes in team shape. The team possesses a level of autonomy that allows the team to travel to and surround buildings of interest in a patrol and cordon scenario. During surround mode, the operator explores or manipulates the team shape to create desired formations around a building. A spacing interaction method also allows the operator to adjust how robots are spaced within the current shape. Separate haptic feedback is developed for each method to allow the operator to "feel" the shape or spacing manipulation. During travel mode, the operator chooses desired travel locations and receives feedback to help identify how and where the team travels. Results from a user study suggest that haptic feedback significantly improves operator performance in a reconnaissance task when task demand is higher, but may slightly increase operator workload. In the context of the experimental setup, these results suggest that haptic feedback may contribute to heads-up control of a team of autonomous robots. There were no significant differences in levels of situation awareness due to haptic feedback in this study.
Samuel J. McDonald, Mark B. Colton, C. Kristopher Alder, Michael A. Goodrich
HRI1