2 papers
eess.SY2024
Domain-decoupled Physics-informed Neural Networks with Closed-form Gradients for Fast Model Learning of Dynamical Systems
Henrik Krauss, Tim-Lukas Habich, Max Bartholdt +2
Physics-informed neural networks (PINNs) are trained using physical equations and can also incorporate unmodeled effects by learning from data. PINNs for control (PINCs) of dynamic…
cs.RO2023
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…