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Benjamin Holmgren

dblp:289/0556 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · none

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

Theory of computation · 2 · 2 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.

Theoretical computer science
1 paper
Computational geometry · 67% Approximation and online algorithms · 33%
Artificial intelligence
2 papers
Motion planning and robot control · 87% Multi-agent systems · 13%

Topics — the 6 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational geometry › motion planning
coordinated motion planning
1.012026
Near-Optimal Min-Sum Multi-Robot Motion Planning in a Planar Polygonal Environment · SODA 2026
Computational geometry
motion planning
1.012026
Near-Optimal Min-Sum Multi-Robot Motion Planning in a Planar Polygonal Environment · SODA 2026
Robotics › Motion planning and robot control
motion planning
0.912025
Optimal Motion Planning for Two Square Robots in a Rectilinear Environment · SoCG 2025
Robotics › Motion planning and robot control › motion planning
optimal motion planning
0.912025
Optimal Motion Planning for Two Square Robots in a Rectilinear Environment · SoCG 2025
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.312026
Near-Optimal Min-Sum Multi-Robot Motion Planning in a Planar Polygonal Environment · SODA 2026
Robotics › Motion planning and robot control › motion planning
multi-robot motion planning
0.312025
Optimal Motion Planning for Two Square Robots in a Rectilinear Environment · SoCG 2025

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

geometric shortest paths · 2.0approximation algorithm · 2.0computational geometry · 0.9
YearPublicationVenuePosition
2026 Near-Optimal Min-Sum Multi-Robot Motion Planning in a Planar Polygonal Environment
abstract
Let \(\mathscr{W} \subset \mathbb{R}^2\) be a planar polygonal environment with n vertices, and let \([k] = \{1, \ldots, k\}\) denote \(k\) unit-square robots translating in \(\mathscr{W}\). Given source and target placements \(s_1, t_1, \ldots, s_k, t_k, \in \mathscr{W}\) for each robot, we wish to compute a collision-free motion plan \(\boldsymbol \pi\), i.e., a coordinated motion for each robot \(i\) along a continuous path from \(s_i\) to \(t_i\), so that robot \(i\) does not leave \(\mathscr{W}\) or collide with any other robot \(j\). Moreover, we additionally require that \(\boldsymbol \pi\) minimizes the sum of the path lengths; this variant is known as min-sum motion planning.
Pankaj K. Agarwal, Benjamin Holmgren, Alex Steiger
SODA2
2025 Optimal Motion Planning for Two Square Robots in a Rectilinear Environment
Pankaj K. Agarwal, Mark de Berg, Benjamin Holmgren, Alex Steiger, Martijn Struijs
SoCG3