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Einar Berglund

dblp:39/3322 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none

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

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2

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
Robot navigation and mapping · 83% Legged, aerial and field robots · 10% Motion planning and robot control · 8%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › localization
inertial navigation
0.112008
Model-aided inertial navigation for underwater vehicles · ICRA 2008
Robotics › Robot navigation and mapping
localization
0.112008
Model-aided inertial navigation for underwater vehicles · ICRA 2008
Robotics › Robot navigation and mapping
state estimation
0.112008
Model-aided inertial navigation for underwater vehicles · ICRA 2008
Robotics › Legged, aerial and field robots
underwater robotics
0.022008
Model-aided inertial navigation for underwater vehicles · ICRA 2008
Exponential Stabilization of a Nonholonomic Underwater Vehicle with Constant Desired Configuration · ICRA 1994
Robotics › Motion planning and robot control › robot control › stabilization control
nonholonomic system stabilization
0.011994
Exponential Stabilization of a Nonholonomic Underwater Vehicle with Constant Desired Configuration · ICRA 1994
Robotics › Motion planning and robot control
robot control
0.011994
Exponential Stabilization of a Nonholonomic Underwater Vehicle with Constant Desired Configuration · ICRA 1994

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

vehicle dynamics model · 0.1sea current estimation · 0.1kinematic model transformation · 0.0feedback control law · 0.0
YearPublicationVenuePosition
2008 Model-aided inertial navigation for underwater vehicles
abstract
This paper reports the development and experimental evaluation of a complete model-aided inertial navigation system (INS) for underwater vehicles. The navigation system is novel in that accurate knowledge of the vehicle dynamics is utilized for aiding the INS, and the performance is evaluated using real data collected by an autonomous underwater vehicle (AUV). Together with real-time sea current estimation, the output from a kinetic vehicle model describing the dynamics is integrated in the navigation system. The presented experimental results verify that with merely an addition of software and no added instrumentation, it is possible to significantly improve the accuracy and robustness of an INS by utilizing the physical insight provided by a kinetic vehicle model. To the best of our knowledge, this paper is the first report on the implementation and experimental evaluation of a complete model-aided INS for underwater vehicle navigation. The proposed approach shows promise to improve underwater navigation capabilities both for systems lacking disparate velocity measurements, typically from a Doppler velocity log (DVL), and for systems where the need for redundancy and integrity is important, e.g. during sensor dropouts or failures, or in case of emergency navigation. The reported methodology is applicable to a large group of submersibles, as well as land and aerial crafts and robots.
Øyvind Hegrenæs, Einar Berglund, Oddvar Hallingstad
ICRA2
1994 Exponential Stabilization of a Nonholonomic Underwater Vehicle with Constant Desired Configuration
abstract
Presents a feedback control law which gives exponential stabilization of a nonholonomic underwater vehicle to a constant desired configuration. This is achieved by transforming the kinematic model into a chained form, and applying a control scheme for exponential stabilization of chained form systems. The nonholonomic vehicle under consideration is described by a kinematic model with three angular velocities and one translational velocity component as inputs. The kinematic model of the underwater vehicle is given in SE(3) by a position vector and a rotation matrix. Due to the difference in topology, the transformation into chained form is not global, but has singularities for large angular deviations. This problem is discussed, and conditions for avoiding the singularities are derived.>
Olav Egeland, Einar Berglund, Ole Jakob Sørdalen
ICRA2