23 citations · 102 across the 18 of their papers we have counts for
18 papers
An Adaptive Control Architecture for Slope and Terrain Compensation in Autonomous Navigation in Mediterranean Greenhouses
Fernando Cañadas-Aránega, Dirk Wollherr, José L. Guzmán +2
The ability to move stably over terrain with varying slopes and textures is essential for mobile agricultural robots operating in complex and dynamic environments such as greenhous…
Optimal Control for Indoor Vertical Farms Based on Crop Growth
Annalena Daniels, Michael Fink, Marion Leibold +2
Vertical farming allows for year-round cultivation of a variety of crops, overcoming environmental limitations and ensuring food security. This closed and highly controlled system…
Comparison of Dynamic Tomato Growth Models for Optimal Control in Greenhouses
Michael Fink, Annalena Daniels, Cheng Qian +3
As global demand for efficiency in agriculture rises, there is a growing interest in high-precision farming practices. Particularly greenhouses play a critical role in ensuring a y…
An Online Adaptation Strategy for Direct Data-driven Control
Johannes Teutsch, Sebastian Ellmaier, Sebastian Kerz +2
The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unkn…
Offline Uncertainty Sampling in Data-driven Stochastic MPC
Johannes Teutsch, Sebastian Kerz, Tim Brüdigam +2
In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The…
Disturbance Estimation for High-Degree-of-Freedom Euler-Lagrangian Systems Using Sliding Mode Observer without Matching Conditions
Zengjie Zhang, Dirk Wollherr
This paper proposes a novel observer-based disturbance estimation method for high degree-of-freedom Euler-Lagrangian systems using an unknown input-output (UIO) sliding mode observ…
Safe Stochastic Model Predictive Control
Tim Brüdigam, Robert Jacumet, Dirk Wollherr +1
Combining efficient and safe control for safety-critical systems is challenging. Robust methods may be overly conservative, whereas probabilistic controllers require a trade-off be…
MS2MP: A Min-Sum Message Passing Algorithm for Motion Planning
Salman Bari, Volker Gabler, Dirk Wollherr
Gaussian Process (GP) formulation of continuoustime trajectory offers a fast solution to the motion planning problem via probabilistic inference on factor graph. However, often the…
Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing
Salman Bari, Xiagong Wang, Ahmad Schoha Haidari +1
Factor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor gra…
Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery
Regine Hartwig, Daniel Ostler, Jean-Claude Rosenthal +3
We propose a novel method to tackle the visual-inertial localization problem for constrained camera movements. We use residuals from the different modalities to jointly optimize a…
MITI: SLAM Benchmark for Laparoscopic Surgery
Regine Hartwig, Daniel Ostler, Jean-Claude Rosenthal +3
We propose a new benchmark for evaluating stereoscopic visual-inertial computer vision algorithms (SLAM/ SfM/ 3D Reconstruction/ Visual-Inertial Odometry) for minimally invasive su…
Model Predictive Control with Models of Different Granularity and a Non-uniformly Spaced Prediction Horizon
Tim Brüdigam, Daniel Prader, Dirk Wollherr +1
Horizon length and model accuracy are defining factors when designing a Model Predictive Controller. While long horizons and detailed models have a positive effect on control perfo…
Collision Avoidance with Stochastic Model Predictive Control for Systems with a Twofold Uncertainty Structure
Tim Brüdigam, Jie Zhan, Dirk Wollherr +1
Model Predictive Control (MPC) has shown to be a successful method for many applications that require control. Especially in the presence of prediction uncertainty, various types o…
Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing
Tim Brüdigam, Alexandre Capone, Sandra Hirche +2
A fundamental aspect of racing is overtaking other race cars. Whereas previous research on autonomous racing has majorly focused on lap-time optimization, here, we propose a method…
Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version
Tim Brüdigam, Michael Olbrich, Dirk Wollherr +1
Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding ve…
Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments
Tim Brüdigam, Fulvio di Luzio, Lucia Pallottino +2
Stochastic Model Predictive Control has proved to be an efficient method to plan trajectories in uncertain environments, e.g., for autonomous vehicles. Chance constraints ensure th…
Minimization of Constraint Violation Probability in Model Predictive Control
Tim Brüdigam, Victor Gaßmann, Dirk Wollherr +1
While Robust Model Predictive Control considers the worst-case system uncertainty, Stochastic Model Predictive Control, using chance constraints, provides less conservative solutio…
Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity
Tim Brüdigam, Johannes Teutsch, Dirk Wollherr +1
Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive C…