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Moritz Diehl

7 accepted papers

2024

Model Predictive Control for Frenet-Cartesian Trajectory Tracking of a Tricycle Kinematic Automated Guided Vehicle

IROS 2024poster

This work proposes an optimal control scheme for a trajectory-tracking Automated Guided Vehicle considering motion and collision constraints in a warehouse environment. We outline how the simpler obstacle avoidance constraints in the Cartesian Coordinate Frame (CCF) can be retained, while projecting…

Cited by 1SourceScholar
2024

Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots

IROS 2024poster

Flexible robots may overcome some of the industry’s major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and…

Cited by 2SourcecodeScholar
2022

Position and Orientation Tunnel-Following NMPC of Robot Manipulators Based on Symbolic Linearization in Sequential Convex Quadratic Programming

RA-L 2022

The tunnel-following nonlinear model predictive control (NMPC) scheme allows to exploit acceptable deviations around a path reference. This is done by using convex-over-nonlinear functions as objective and constraints in the underlying optimal control problem (OCP). The convex-over-nonlinear structu

Cited by 18SourceScholar
2021

Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation

AISTATS 2021poster

We propose kernel distributionally robust optimization (Kernel DRO) using insights from the robust optimization theory and functional analysis. Our method uses reproducing kernel Hilbert spaces (RKHS) to construct a wide range of convex ambiguity sets, which can be generalized to sets based on integ…

2020

An NMPC Approach using Convex Inner Approximations for Online Motion Planning with Guaranteed Collision Avoidance

ICRA 2020poster

Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remain subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a…

Cited by 40SourceScholar
2020

Transferring Optimality Across Data Distributions via Homotopy Methods

ICLR 2020poster

Homotopy methods, also known as continuation methods, are a powerful mathematical tool to efficiently solve various problems in numerical analysis, including complex non-convex optimization problems where no or only little prior knowledge regarding the localization of the solutions is available. In…

Cited by 2SourceScholar
2018

A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control

IROS 2018poster

This paper introduces a family of iterative algorithms for unconstrained nonlinear optimal control. We generalize the well-known iLQR algorithm to different multiple shooting variants, combining advantages like straightforward initialization and a closed-loop forward integration. All algorithms have…

Cited by 117SourceScholar