11 Interfakultäre Einrichtungen

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/12

Browse

Filters

2021 - 20243

Settings

Institute: search.filters.UBSInstitut.Max-Planck-Institut für Intelligente Systeme

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Development of a bioinspired multimodal mobile robot platform
    (2024) Kim, HyunGyu; Sitti, Metin (Prof. Dr.)
  • Thumbnail Image
    ItemOpen Access
    Learning soft millirobot multimodal locomotion with sim‐to‐real transfer
    (2024) Demir, Sinan Ozgun; Tiryaki, Mehmet Efe; Karacakol, Alp Can; Sitti, Metin
    With wireless multimodal locomotion capabilities, magnetic soft millirobots have emerged as potential minimally invasive medical robotic platforms. Due to their diverse shape programming capability, they can generate various locomotion modes, and their locomotion can be adapted to different environments by controlling the external magnetic field signal. Existing adaptation methods, however, are based on hand‐tuned signals. Here, a learning‐based adaptive magnetic soft millirobot multimodal locomotion framework empowered by sim‐to‐real transfer is presented. Developing a data‐driven magnetic soft millirobot simulation environment, the periodic magnetic actuation signal is learned for a given soft millirobot in simulation. Then, the learned locomotion strategy is deployed to the real world using Bayesian optimization and Gaussian processes. Finally, automated domain recognition and locomotion adaptation for unknown environments using a Kullback‐Leibler divergence‐based probabilistic method are illustrated. This method can enable soft millirobot locomotion to quickly and continuously adapt to environmental changes and explore the actuation space for unanticipated solutions with minimum experimental cost.
  • Thumbnail Image
    ItemOpen Access
    Task space adaptation via the learning of gait controllers of magnetic soft millirobots
    (2021) Demir, Sinan O.; Culha, Utku; Karacakol, Alp C.; Pena-Francesch, Abdon; Trimpe, Sebastian; Sitti, Metin
    Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.