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Matthew Rice

dblp:58/10812 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 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
Multi-agent systems · 87% Motion planning and robot control · 13%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems › distributed constraint optimization
distributed assignment
0.312017
A Distributed Version of the Hungarian Method for Multirobot Assignment · IEEE Trans. Robotics 2017
Knowledge, reasoning and agents › Multi-agent systems › task allocation
multi-robot task allocation
0.312017
A Distributed Version of the Hungarian Method for Multirobot Assignment · IEEE Trans. Robotics 2017
Robotics › Motion planning and robot control › motion planning
multi-robot motion planning
0.112017
A Distributed Version of the Hungarian Method for Multirobot Assignment · IEEE Trans. Robotics 2017

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

peer-to-peer communication · 0.3distributed hungarian method · 0.3
YearPublicationVenuePosition
2023 The Lena Singer Project: Simulating the Learning Experience of a Singer
Matthew Rice, Simon Colton
ICCC1
2017 A Distributed Version of the Hungarian Method for Multirobot Assignment
abstract
In this paper, we propose a distributed version of the Hungarian method to solve the well-known assignment problem. In the context of multirobot applications, all robots cooperatively compute a common assignment that optimizes a given global criterion (e.g., the total distance traveled) within a finite set of local computations and communications over a peer-to-peer network. As a motivating application, we consider a class of multirobot routing problems with “spatiotemporal” constraints, i.e., spatial targets that require servicing at particular time instants. As a means of demonstrating the theory developed in this paper, the robots cooperatively find online suboptimal routes by applying an iterative version of the proposed algorithm in a distributed and dynamic setting. As a concrete experimental test bed, we provide an interactive “multirobot orchestral” framework, in which a team of robots cooperatively plays a piece of music on a so-called orchestral floor.
Smriti Chopra, Giuseppe Notarstefano, Matthew Rice, Magnus Egerstedt
IEEE Trans. Robotics3