> For the complete documentation index, see [llms.txt](https://2022.istvs.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://2022.istvs.org/papers/4409.md). # 4409 / Design of Self-Driving Bulldozer System Title: Design of Self-Driving Bulldozer System Authors: Junhua Yang, Biao Zhang, Haokai Tang, Binghua Shen, Linlin Ou, Xinyi Yu, Yuanjing Feng, Yu Feng, and Libo Zhou Abstract: To improve the efficiency of manual operations in the coal bunker, a completely automatic system based on multi-sensor perceptual positioning and map-based clearance planning is proposed. Firstly, the perceptual positioning link combines semantic segmentation to improve the traditional feature extraction and restricts the loop keyframe by using brute force matching, which improves the localization ability of simultaneous localization and mapping (SLAM) in the coal bunker. Compared with the incremental map maintenance strategy, a neighboring strategy is proposed to update the map in the dynamic environment of the coal bunker. Secondly, in response to the requirements of the coal bunker cleaning operation, based on the SLAM positioning results, we formulate behavior planning combined with a hierarchical finite state machine (FSM) and a grid map-based motion planning. The proposed clearing plan combines the geometric characteristics of crawler rakes, reduces the error of trajectory tracking, and improves the smoothness of trajectory curvature. The system can realize accurate real-time positioning in the bunker environment, and successfully complete the requirements of cleaning. In addition, a high simulation environment based on the unreal engine is built to verify the effectiveness and robustness of the system. Order the full paper: ISTVS members: receive three papers per year as part of your membership via the ISTVS Member Portal: --- # 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://2022.istvs.org/papers/4409.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.