24 citations · 63 across the 14 of their papers we have counts for
16 papers
Nonlinear Two-Track Model of a Semitrailer with Experimental Validation of Lateral and Vertical Tire Forces
Simon F. G. Ehlers, Zygimantas Ziaukas, Jan-Philipp Kobler +3
As part of the automation of commercial vehicles, the number of assistance systems in this field is continuously increasing. The semitrailer plays an important role for the vehicle…
Safe Collision and Clamping Reaction for Parallel Robots During Human-Robot Collaboration
Aran Mohammad, Moritz Schappler, Tim-Lukas Habich +1
Parallel robots (PRs) offer the potential for safe human-robot collaboration because of their low moving masses. Due to the in-parallel kinematic chains, the risk of contact in the…
Collision Isolation and Identification Using Proprioceptive Sensing for Parallel Robots to Enable Human-Robot Collaboration
Aran Mohammad, Moritz Schappler, Tobias Ortmaier
Parallel robots (PRs) allow for higher speeds in human-robot collaboration due to their lower moving masses but are more prone to unintended contact. For a safe reaction, knowledge…
Towards Human-Robot Collaboration with Parallel Robots by Kinetostatic Analysis, Impedance Control and Contact Detection
Aran Mohammad, Moritz Schappler, Tobias Ortmaier
Parallel robots provide the potential to be leveraged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving mass…
Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging
Max-Heinrich Laves, Sontje Ihler, Jacob F. Fast +2
The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variat…
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
Max-Heinrich Laves, Malte Tölle, Tobias Ortmaier
Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate an…