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Michael Brunner 0004

dblp:05/8924-4 · DBLP profile ↗
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6ranked-venue papers
4as first author
0since 2021 · last 2013
0000-0001-7917-0333ORCID · verified

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

Artificial intelligence and machine learning · 5 · 4 first-authorSystems, architecture and hardware · 1 · 1 first-authorComputer networks · 1

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 · 56% Planning, search and constraint satisfaction · 28% Legged, aerial and field robots · 17%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
graph search
0.212013
Hierarchical rough terrain motion planning using an optimal sampling-based method · ICRA 2013
Robotics › Motion planning and robot control
motion planning
0.212013
Hierarchical rough terrain motion planning using an optimal sampling-based method · ICRA 2013
Robotics › Motion planning and robot control › motion planning
sampling-based motion planning
0.212013
Hierarchical rough terrain motion planning using an optimal sampling-based method · ICRA 2013
Robotics › Legged, aerial and field robots
field robotics
0.012013
Hierarchical rough terrain motion planning using an optimal sampling-based method · ICRA 2013
Robotics › Legged, aerial and field robots
rough terrain locomotion
0.012013
Hierarchical rough terrain motion planning using an optimal sampling-based method · ICRA 2013

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

grid search · 0.2RRT* · 0.2
YearPublicationVenuePosition
2013 Hierarchical Roadmap Approach to Rough Terrain Motion Planning
Michael Brunner 0004, Bernd Brüggemann, Dirk Schulz 0001
ICAART (1)1
2013 Asynchronous Flooding Planner for Multi-Robot Navigation
Bernd Brüggemann, Michael Brunner 0004, Dirk Schulz 0001
ICINCO (2)2
2013 Hierarchical rough terrain motion planning using an optimal sampling-based method
abstract
Mobile robots with reconfigurable chassis are able to traverse unstructured outdoor environments with boulders or rubble, and overcome challenging structures in urban environments, like stairs or steps. Autonomously traversing rough terrain and such obstacles while ensuring the safety of the robot is a challenging task in mobile robotics. In this paper we introduce a two-phase motion planning algorithm for actively reconfigurable tracked robots. We first use the completeness of a graph search on a regular grid to quickly find an initial path in a low dimensional space, considering only the platform's operating limits instead of the complete state. We then take this initial path to focus the RRT* search in the continuous high-dimensional state space including the actuators of the robot. We do not rely on a detailed structure/terrain classification or use any predefined motion sequences. Hence, our planner can be applied to urban structures, like stairs, as well as rough unstructured environments. Simulation results prove our method to be effective in solving planning queries in such environments.
Michael Brunner 0004, Bernd Brüggemann, Dirk Schulz 0001
ICRA1
2013 Spatially constrained coordinated navigation for a multi-robot system
Bernd Brüggemann, Michael Brunner 0004, Dirk Schulz 0001
Ad Hoc Networks2
2012 Autonomously Traversing Obstacles - Metrics for Path Planning of Reconfigurable Robots on Rough Terrain
Michael Brunner 0004, Bernd Brüggemann, Dirk Schulz 0001
ICINCO (2)1
2010 Position Estimation of Mobile Robots Considering Characteristic Terrain Properties
Michael Brunner 0004, Dirk Schulz 0001, Armin B. Cremers
ICINCO (2)1