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Andrew Rogers

dblp:166/3597 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-7634-2977ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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.

Artificial intelligence
1 paper
Robot navigation and mapping · 33% Multi-agent systems · 33% Motion planning and robot control · 33%
Computer networks
1 paper
Internet of things and sensor networks · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › motion control
closed-loop motion control
0.612022
Closed-Loop Motion Control of Robotic Swarms - A Tether-Based Strategy · IEEE Trans. Robotics 2022
Knowledge, reasoning and agents › Multi-agent systems › swarm robotics
swarm control
0.612022
Closed-Loop Motion Control of Robotic Swarms - A Tether-Based Strategy · IEEE Trans. Robotics 2022
Internet of things and sensor networks
mobile sensor networks
0.212022
Closed-Loop Motion Control of Robotic Swarms - A Tether-Based Strategy · IEEE Trans. Robotics 2022

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

wireless tether · 1.1closed-loop control · 1.1
YearPublicationVenuePosition
2022 Closed-Loop Motion Control of Robotic Swarms - A Tether-Based Strategy
abstract
Swarm robots can achieve effective task execution via closed-loop motion control. However, such a goal can only be realized through accurate localization of the swarm. Past approaches have focused on addressing this issue using external sensors, static sensor networks, or through active localization—requirements that may restrict the motion of the swarm or may not be achievable in practice. We present a tether-based strategy that achieves closed-loop swarm-motion control by using a secondary team of mobile sensors. These sensors form a wireless tether that allows the swarm to indirectly sense a home base or a landmark, and to compensate for the accumulated motion errors via a closed-loop control strategy. The proposed strategy is the first to use a tether of mobile sensors that can dynamically reshape and reconnect to various points in the environment to achieve closed-loop motion control. The novelty of the strategy is in its ability to adapt to any swarm motion considered, and to be applied to swarms with limited sensing capabilities and knowledge of their environment. The performance of the proposed strategy was validated through extensive experiments.
Kasra Eshaghi, Andrew Rogers, Goldie Nejat, Beno Benhabib
IEEE Trans. Robotics2