Markus Giftthaler


Last Name

Giftthaler

First Name

Markus

ORCID

0000-0002-1839-9449

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2017 - 20196
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Publications 1 - 6 of 6
  • Automatic differentiation of rigid body dynamics for optimal control and estimation
    Item type: Conference Paper
    Giftthaler, Markus; Neunert, Michael; Stäuble, Markus; et al. (2017)
    Advanced Robotics
  • The control toolbox — An open-source C++ library for robotics, optimal and model predictive control
    Item type: Conference Paper
    Giftthaler, Markus; Neunert, Michael; Stäuble, Markus; et al. (2018)
    2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)
  • Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds
    Item type: Journal Article
    Neunert, Michael; Stäuble, Markus; Giftthaler, Markus; et al. (2018)
    IEEE Robotics and Automation Letters
  • A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control
    Item type: Conference Paper
    Giftthaler, Markus; Neunert, Michael; Stäuble, Markus; et al. (2018)
    2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
    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 similar computational complexity, i.e. linear complexity in the time horizon, and can be derived in the same computational framework. We compare the full-step variants of our algorithms and present several simulation examples, including a high-dimensional underactuated robot subject to contact switches. Simulation results show that our multiple shooting algorithms can achieve faster convergence, better local contraction rates and much shorter runtimes than classical iLQR, which makes them a superior choice for nonlinear model predictive control applications.
  • Digital in situ fabrication - Challenges and opportunities for robotic in situ fabrication in architecture, construction, and beyond
    Item type: Review Article
    Buchli, Jonas; Giftthaler, Markus; Kumar, Nitish; et al. (2018)
    Cement and Concrete Research
  • Mobile robotic fabrication beyond factory conditions: case study Mesh Mould wall of the DFAB HOUSE
    Item type: Journal Article
    Dörfler, Kathrin; Hack, Norman; Sandy, Timothy; et al. (2019)
    Construction Robotics
    The development of novel robotic fabrication technologies in architecture concentrates largely on integrating stationary industrial-type robots into off-site prefabrication processes. By contrast, few enabling robotic technologies exist today that allow robotic fabrication processes to be mobile and implemented directly on building sites. While mobile in situ fabrication offers a large range of architectural potentials, its realization requires to address fundamental challenges. First, the production of large-scale and potentially monolithic structures on-site requires an advanced robotic fabrication system that can fulfill the material, structural- and architectural-related demands associated with it. Second, the poorly structured nature of building sites requires mobile robotic systems to be equipped with advanced sensing and control solutions to contend with uncertain conditions found on-site. The research discussed in this paper addresses both of these subjects. It applies a novel construction system for non-standard reinforced concrete structures, termed Mesh Mould, to explore the fabrication of large-scale and monolithic building structures using a mobile robot on site. It further investigates sensor-integrated adaptive fabrication strategies to achieve the accurate fabrication of such a large-scale structure, and this is done despite prevalent uncertainties related to the building site environment, the mobile robotic system, and the material behavior during fabrication. The results of this research were realized in a slender, doubly curved, reinforced concrete wall at the DFAB HOUSE at NEST. This research demonstrator provides the unique opportunity to present robotic in situ fabrication not merely as a future possibility, but as a reality applied to a tangible construction project.
Publications 1 - 6 of 6