Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Michael L. Chen

dblp:47/3216 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 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.

Artificial intelligence
1 paper
Robot manipulation · 62% Motion planning and robot control · 19% Reinforcement learning · 19%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › assembly
multi-robot assembly
0.012000
Visually Guided Coordination for Distributed Precision Assembly · ICRA 2000
Machine learning › Reinforcement learning › multi-agent reinforcement learning
multi-agent communication
0.012000
Visually Guided Coordination for Distributed Precision Assembly · ICRA 2000
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing
0.012000
Visually Guided Coordination for Distributed Precision Assembly · ICRA 2000

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

visual servoing · 0.0low-latency protocols · 0.0
YearPublicationVenuePosition
2000 Visually Guided Coordination for Distributed Precision Assembly
abstract
We document our initial efforts to instantiate visually-guided cooperative behaviors between robotic agents in the minifactory environment. The minifactory incorporates high-precision, 2-DOF robotic agents to perform micron-level precision 4-DOF assembly tasks. Here we utilize two minifactory agents to perform visual servoing. We present a detailed description of the control and communication systems used to coordinate the agents. To provide a suitable communications infrastructure, we describe the development of a new interagent communication system which uses low-latency protocols carried by a commercial 100 Mb Ethernet network. Finally, we conclude by presenting experimental results front our first coordinated multi-agent task, the visually guided positioning of a small medical device.
Michael L. Chen, Shinji Kume, Alfred A. Rizzi, Ralph L. Hollis
ICRA1