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

James Gloyd

dblp:416/5215 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 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
Motion planning and robot control · 61% Multi-agent systems · 39%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
motion planning
0.912025
Multi-Agent Collective Construction of General Modular Structures · ICRA 2025
Robotics › Motion planning and robot control › motion planning
multi-robot motion planning
0.912025
Multi-Agent Collective Construction of General Modular Structures · ICRA 2025
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.312025
Multi-Agent Collective Construction of General Modular Structures · ICRA 2025

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

decoupled planning · 0.9deadlock avoidance · 0.9collision avoidance · 0.9
YearPublicationVenuePosition
2025 Multi-Agent Collective Construction of General Modular Structures
abstract
We present an algorithmic framework for a multi-robot modular assembly system. Motivated by the prospects of in-space assembly, we focus on the NASA Automated Reconfigurable Mission Adaptive Digital Assembly Systems (AR-MADAS) framework, in which multiple types of robots work together in a team to build large structures. Unlike with other multi-robot construction systems, the geometry of structures that ARMADAS robots can build is not limited to the class of histogram shapes. To address the intractability of path planning for a robot system with the exponentially growing number of dimensions, we present a decoupled planning approach, where the assembly and path planning is performed iteratively by one robot team at a time. We present a number of data structures which help us avoid collisions and deadlocks in the resulting robot schedule.
Irina Kostitsyna, James Gloyd, Kenneth C. Cheung
ICRA2