Learning-Aided Integrated Control and Semantic Perception Architecture for Legged Robot Locomotion and Navigation in the Wild

用于腿式机器人野外运动和导航的学习辅助集成控制和语义感知架构

基本信息

批准号：
2118818
负责人：
Jessy Grizzle
金额：
$ 98.64万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118818&HistoricalAwards=false
关键词：
Learning Aided Integrated Control Semantic

项目摘要

This project develops an open-source system to allow legged robots to perform autonomous exploration and environmental monitoring in unstructured environments, such as in a forest. A central theme is the design and deployment of a unified and integrated framework that exploits the redundancy of onboard sensory modalities and cross-integrates with feedback control and planning. The project seeks major advances in control theory, computer vision, embedded systems, and planning under uncertainty, areas that are often studied in isolation. By studying these areas as a systems problem, the researchers expect significant advances for autonomous systems in unstructured environments. The results of the research are demonstrated on a Digit-series 3D biped and a Mini Cheetah quadruped robot. The research elevates the state of the art in deploying autonomous mobile robots “in the wild.” In off-road and unstructured settings, the main technique currently employed by the autonomous vehicle industry, registering into known high-definition maps on the basis of collected sensor measurements, is not possible and adversely affects autonomy. The project develops a real-time multi-layer dense semantic occupancy mapping with an extended set of terrain labels and a “walk-ability” index combined with an integrated motion planner for an autonomous system. The traversability map enables a walking robot to make dynamic real-time planning and feedback control decisions, adjusting for gait characteristics and for precise foot placement based on the surrounding terrain and environment.The principal investigators are co-developing a freshman college course to inspire students by teaching how practicing engineers employ computational linear algebra to solve large and important problems as arise in the study of autonomous robots. Course projects are selected from contemporary topics in robotics such as, for example, map building from LiDAR point clouds, machine learning for spatial representation of data, and feedback control of mobile platforms. In addition, the project engages in outreach to underrepresented communities through collaborative educational instruction in STEM fields in Detroit public high schools.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目开发了一个开源系统，允许腿式机器人在非结构化环境（例如森林）中进行自主探索和环境监测，其中心主题是设计和部署一个统一的集成框架，利用机载传感器的冗余。研究人员通过将这些领域作为系统问题进行研究，寻求控制理论、计算机视觉、嵌入式系统和不确定性规划方面的重大进展。预计取得重大进展该研究的结果在 Digit 系列 3D 双足机器人和 Mini Cheetah 四足机器人上进行了演示，该研究提升了“野外”部署自主移动机器人的技术水平。道路和非结构化环境是自动驾驶汽车行业目前采用的主要技术，即根据收集的传感器测量结果注册到已知的高清地图中，这是不可能的，并且会对自动驾驶产生不利影响。多层密集语义占用映射，具有扩展的地形标签集和“步行能力”指数，与自主系统的集成运动规划器相结合。可遍历性地图使步行机器人能够进行动态实时规划和反馈控制。决定，根据周围的地形和环境调整步态特征和精确的足部位置。主要研究人员正在共同开发一门大学新生课程，通过教授实践工程师如何利用计算线性代数来解决出现的大型和重要问题来启发学生在自治研究中课程项目选自机器人技术的当代主题，例如从激光雷达点云构建地图、数据空间表示的机器学习以及移动平台的反馈控制。此外，该项目还致力于向代表性不足的社区进行推广。通过底特律公立高中 STEM 领域的协作教育指导。该项目得到了机器人学跨部门基础研究项目的支持，该项目由工程部 (ENG) 以及计算机和信息科学与工程部共同管理和资助（CISE）。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。