7 papers
Smooth Sampling-Based Model Predictive Control Using Deterministic Samples
Markus Walker, Marcel Reith-Braun, Tai Hoang +2
Sampling-based model predictive control (MPC) is effective for nonlinear systems but often produces non-smooth control inputs due to random sampling. To address this issue, we exte…
Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Markus Walker, Daniel Frisch, Uwe D. Hanebeck
Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance o…
Sampling-based Model Predictive Control Using Trust Regions
Markus Walker, Marcel Reith-Braun, Daniel Frisch +1
Sampling-based model predictive control (MPC) algorithms, such as model predictive path integral (MPPI), enable approximate, gradient-free solutions to optimal control problems by…
Newton-Flow Particle Filters based on Generalized Cramér Distance
Uwe D. Hanebeck
We propose a recursive particle filter for high-dimensional problems that inherently never degenerates. The state estimate is represented by deterministic low-discrepancy particle…
HapticGiant: A Novel Very Large Kinesthetic Haptic Interface with Hierarchical Force Control
Michael Fennel, Markus Walker, Dominik Pikos +1
Research in virtual reality and haptic technologies has consistently aimed to enhance immersion. While advanced head-mounted displays are now commercially available, kinesthetic ha…
Efficient Gaussian Mixture Filters based on Transition Density Approximation
OndÅej Straka, Uwe D. Hanebeck
Gaussian mixture filters for nonlinear systems usually rely on severe approximations when calculating mixtures in the prediction and filtering step. Thus, offline approximations of…