> For the complete documentation index, see [llms.txt](https://2024.istvs.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://2024.istvs.org/submissions/papers/9028.md). # 9028 / Uncertainty Quantification For Wheeled Locomotion Machine Learning Predictions On Soft Soil ## Authors Vladyslav Fediukov, Felix Dietrich, Fabian Buse, and Jana Huhne {% hint style="info" %} Paper presented at ISTVS 2024 | 21st International and 12th Asia-Pacific Regional Conference of the ISTVS\ Keywords: Terramechanics; Rover Locomotion; Uncertainty Quantification; Multi-Fidelity; Machine Learning\ {% endhint %} ## Abstract In predicting locomotion on soft-soil, we have to deal with a consistent uncertainty surrounding this process, from the input noise to the uncertainties produced by an approximation. Available data comprises limited sets of experimental data and various numerical approximations. Machine learning models, gaining recognition in the terramechanics community, need to work with these limited data set. By deploying probabilistic frameworks, like Gaussian processes, for our tasks, we can implicitly work with the resulting uncertainties. Accurate uncertainty quantification and further analysis can provide more robustness and understanding of terramechanical machine learning models. In our work, we concentrate on the uncertainty's propagation, uncertainty calibration, and uncertainty decoupling for a wheel locomotion prediction. Our machine learning models work in a multi-fidelity framework using experimental data from the DLR’s TROLL testbed and numerical simulations using TerRA and SCM, approximating the high-fidelity target function in the training process. The experimental setup involves runs with various velocities and movement scenarios, including tilting, steering, for- and backward, as well as up- and downhill movements. Complete analysis of uncertainties will give engineers and operators a more in-depth understanding of the reliability of ongoing simulations and predictions. Moreover, uncertainties quantification can help us improve our data generation and modeling process, allowing us to make a self-improving model. *** Full paper purchase: \ ISTVS members receive three complimentary papers per year: --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://2024.istvs.org/submissions/papers/9028.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.