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Lara Bergmann

Publications and source records attributed to Lara Bergmann.

3 recordsLinked to original sources

From Transportation to Manipulation: Transforming Magnetic Levitation to Magnetic Robotics

Magnetic Levitation (MagLev) systems fundamentally increase the flexibility of in-machine material flow in industrial automation. Therefore, these systems enable dynamic throughput optimization, which is especially beneficial for high-mix low-volume manufacturing. Until now, MagLev installations have been used primarily for in-machine transport, while their potential for manipulation is largely unexplored. This paper introduces the 6D-Platform MagBot, a low-cost six degrees of freedom parallel kinematic that couples two movers into a composite robotic platform. Experiments show that the 6D-Platform MagBot achieves sub-millimeter positioning accuracy and supports fully autonomous pick up and drop off via a docking station, allowing rapid and repeatable reconfiguration of the machine. Relative to a single mover, the proposed platform substantially expands the reachable workspace, payload, and functional dexterity. By unifying transportation and manipulation, this work advances Magnetic Levitation towards Magnetic Robotics, enabling manufacturing solutions that are more agile, efficient, and adaptable.

cs.RO

MagBotSim: Physics-Based Simulation and Reinforcement Learning Environments for Magnetic Robotics

Magnetic levitation is about to revolutionize in-machine material flow in industrial automation. Such systems are flexibly configurable and can include a large number of independently actuated shuttles (movers) that dynamically rebalance production capacity. Beyond their capabilities for dynamic transportation, these systems possess the inherent yet unexploited potential to perform manipulation. By merging the fields of transportation and manipulation into a coordinated swarm of magnetic robots (MagBots), we enable manufacturing systems to achieve significantly higher efficiency, adaptability, and compactness. To support the development of intelligent algorithms for magnetic levitation systems, we introduce MagBotSim (Magnetic Robotics Simulation): a physics-based simulation for magnetic levitation systems. By framing magnetic levitation systems as robot swarms and providing a dedicated simulation, this work lays the foundation for next generation manufacturing systems powered by Magnetic Robotics. MagBotSim's documentation, videos, experiments, and code are available at: https://ubi-coro.github.io/MagBotSim/

cs.RO

Precision-Focused Reinforcement Learning Model for Robotic Object Pushing

Non-prehensile manipulation, such as pushing objects to a desired target position, is an important skill for robots to assist humans in everyday situations. However, the task is challenging due to the large variety of objects with different and sometimes unknown physical properties, such as shape, size, mass, and friction. This can lead to the object overshooting its target position, requiring fast corrective movements of the robot around the object, especially in cases where objects need to be precisely pushed. In this paper, we improve the state-of-the-art by introducing a new memory-based vision-proprioception RL model to push objects more precisely to target positions using fewer corrective movements.

cs.RO