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Marek Musial

dblp:08/3908 · DBLP profile ↗
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4ranked-venue papers
1as first author
0since 2021 · last 2013
—ORCID · none

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

Artificial intelligence and machine learning · 3Systems, architecture and hardware · 2Computer networks · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 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
2 papers
Reinforcement learning · 60% Legged, aerial and field robots · 20% Robot navigation and mapping · 20%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 100%

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

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning › function approximation
state aggregation
0.212013
Construction of approximation spaces for reinforcement learning · J. Mach. Learn. Res. 2013
Robotics › Legged, aerial and field robots › aerial robots
autonomous helicopter
0.112005
Detection of Sensor Faults in Autonomous Helicopters · ICRA 2005
Robotics › Robot navigation and mapping › fault detection
sensor fault detection
0.112005
Detection of Sensor Faults in Autonomous Helicopters · ICRA 2005
Electronic design automation › hardware verification and test
fault diagnosis
0.012005
Detection of Sensor Faults in Autonomous Helicopters · ICRA 2005

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

approximation space construction · 0.2observer-based detection · 0.1
YearPublicationVenuePosition
2013 Construction of approximation spaces for reinforcement learning
Wendelin Böhmer, Steffen Grünewälder, Marek Musial, Klaus Obermayer
J. Mach. Learn. Res.4
2005 Detection of Sensor Faults in Autonomous Helicopters
abstract
This paper presents a sensor fault detection and diagnosis system for autonomous helicopters. The system has been tested with the MARVIN autonomous helicopter. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The effectiveness of the proposed approach is demonstrated by means of MARVIN experimental results.
Guillermo Heredia, Aníbal Ollero, Rajesh Mahtani, Manuel Béjar, Volker Remuß, Marek Musial
ICRA6
2004 Mechanical model and control of an autonomous small size helicopter with a stiff main rotor
abstract
The mathematical description of the kinematics and dynamics of a model helicopter is presented. Using the derived closed form solution for simplified dynamics, a controller for an autonomous model helicopter is designed. This controller is composed of two loops: the inner loop for controlling the orientation of the main rotor plain and the outer loop to control the mission parameters like coordinates of some reference point, orientation or cruise velocity. The performance of the proposed controller is demonstrated for the case of position control in a computer simulation.
Konstantin Kondak, Carsten Deeg, Günter Hommel, Marek Musial, Volker Remuß
IROS4
1997 On-Line Timed Protocol Trace Analysis Based on Uncertain State Descriptions
Marek Musial
FORTE1