> For the complete documentation index, see [llms.txt](https://2025.istvs.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://2025.istvs.org/submissions/papers/8964.md). # 8964 - APPLICATION OF LSTM NETWORKS TO REAL-TIME PREDICTION OF A WHEEL TRAVELING PHENOMENA {% hint style="info" %} Paper presented at ISTVS 2025 | 55th Conference of the International Society for Terrain-Vehicle Systems {% endhint %} **Authors:** *Haruki Endo, Shingo Ozaki, Kai Xing* **Keywords:** Wheel; Trafficability; Simulation; Deep learning; LSTM network **Abstract:** Lunar exploration, which began in the 1960s, is still ongoing and various space exploration programs are planned. In these space exploration programs, it is important to analyze and predict the trafficability of exploration rovers on the lunar and Martian surfaces, and to develop and operate on these ground conditions. Therefore, in this study, we focused on the real-time analysis in operational phase of exploration rovers. We applied a Long Short-term Memory (LSTM)1) network, an artificial regression neural network architecture used in the field of deep learning, to single-wheel traveling phenomena with the aim of applying it to the real-time prediction in future exploration rover operations. Specifically, numerical simulations were carried out using an extended terramechanics model that takes terrain surface deformation into account, and training and prediction are carried out on several LSTM models. The results show the possibility of real-time prediction of trafficability of a wheel on soft ground due to the characteristics of LSTM networks that can handle time-series data. Furthermore, it is suggested that the proposed method may be applicable to the prediction of precursor phenomena of wheel stuck. 1. Gonzalez, J ; Yu, W. Non-linear system modeling using LSTM neural networks. IFAC-Pap. 2018, 51, 485-489. --- # 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://2025.istvs.org/submissions/papers/8964.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.