> 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/6316.md). # 6316 / Perceptive Locomotion of Legged Robot Coupling Model Predictive Control and Terrain Mapping Title: Perceptive Locomotion of Legged Robot Coupling Model Predictive Control and Terrain Mapping Authors: Boyang Xing, Bo Su, Lei Jiang, Yufei Liu, Zhirui Wang, Jianxin Zhao, and Tianqi Qiu Abstract: Legged robots promise an advantage over traditional wheeled systems, however, most legged robots are still confined to structured and flat environments. In this paper, we present a motion planner for the perceptive rough-terrain locomotion with quadruped robots. One of the main reasons for this is the difficulty in planning complex whole-body motions while taking into account the terrain conditions. This problem is very high-dimensional as it considers the robots dynamics together with the terrain model in a suitable problem formulation. In this work, we propose a novel trajectory and foothold optimization method that plans dynamically both foothold locations and motions (coupled planning). It jointly optimizes body motion, step duration and foothold selection, considering the terrain topology. Our model predictive controller tracks compliantly trunk motions while avoiding slippage. We test our method and comparative evaluations over a set of terrains of progressively increasing difficulty. To this end, we present a novel pose optimization approach that enables the robot to climb over significant obstacles. We experimentally validate our approach with the quadrupedal robot Panda5 autonomously traversing obstacles such steps, inclines, and stairs. The locomotion planner re-plans the motion at every step to cope with disturbances and dynamic environments. 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/6316.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.