This project is about modeling flexible hydraulic hoses attached to mining machines and has the goal of predicting hose states under different machine configurations in order to avoid damage during operations. We investigate different modeling techniques from computational physics and machine learning and evaluate their efficacy for preventing hose damage under realistic use-case scenarios for heavy duty mining machines.
This project is funded by the strategic innovation program Swedish Mining Innovation of Vinnova under grant number 2020-04467. The project started in April 2020 and is expected to run through August 2022. For more details, please get in touch with one of the main researchers involved in the project: Yuxuan Yang, Johannes A. Stork and Todor Stoyanov.
Recent Highlights
Learning differentiable dynamics models for shape control of deformable linear objects
Learning differentiable dynamics models for shape control of deformable linear objects Abstract Robots manipulating deformable linear objects (DLOs)—such as surgical sutures in medical robotics, or…
Particle Filters in Latent Space for Robust Deformable Linear Object Tracking
Abstract: Tracking of deformable linear objects (DLOs) is important for many robotic applications. However, achieving robust and accurate tracking is challenging due to the lack…
Learning to Propagate Interaction Effects for Modeling Deformable Linear Objects Dynamics
Learning to Propagate Interaction Effects for Modeling Deformable Linear Objects Dynamics Abstract Modeling dynamics of deformable linear objects (DLOs), such as cables, hoses, sutures, and…