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Maximilian Mehl

dblp:319/6961 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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 · 77% Probabilistic and Bayesian machine learning · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian filtering › kalman filtering
unscented kalman filter
0.212024
Adaptive State Estimation with Constant-Curvature Dynamics Using Force-Torque Sensors with Application to a Soft Pneumatic Actuator · ICRA 2024

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

unscented kalman filter · 0.8cosserat rod model · 0.8
YearPublicationVenuePosition
2024 Adaptive State Estimation with Constant-Curvature Dynamics Using Force-Torque Sensors with Application to a Soft Pneumatic Actuator
abstract
Using compliant materials leads to continuum robots undergoing large deformations. Their nonlinear behavior motivates the use of model-based controllers. They require state estimation as an essential step to be deployed. Available sensors are usually realized by introducing rigid bodies to the soft robot or inserting soft sensors made of materials different from the robot itself. Both approaches result in changes in the system’s dynamics. Optical measurements are problematic, especially in confined spaces. This can be avoided when the sensor is located at the robot's base. This paper studies the state estimation of a pneumatically actuated soft robot using the measured forces and torques at its base. For the first time, this is done using an unscented Kalman filter without restraining the dynamics to a planar or quasi-static motion while applying it to a real system. Real-time capability is achieved with our implementation. The state estimation is tested in a Cosserat rod simulation and on the physical system. The position is estimated with an accuracy of three to five millimeters for a 130 millimeter long pneumatic robot.
Maximilian Mehl, Max Bartholdt, Simon F. G. Ehlers, Thomas Seel, Moritz Schappler
ICRA1