权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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，允许腿式机器人在森林等非结构化环境中进行自主探索和环境监测。一个中心主题是设计和部署一个统一的和综合的框架，利用冗余的板载感官形式和交叉集成的反馈控制和规划。该项目寻求在控制理论，计算机视觉，嵌入式系统和不确定性下的规划方面取得重大进展，这些领域通常是孤立研究的。通过将这些领域作为一个系统问题来研究，研究人员预计非结构化环境中的自治系统将取得重大进展。研究结果在Digit系列3D机器人和Mini Cheetah四足机器人上进行了演示。这项研究提升了在“野外”部署自主移动的机器人的技术水平。在越野和非结构化环境中，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，是不可能的，并对自主性产生不利影响。该项目开发了一个实时的多层密集的语义占用映射与一组扩展的地形标签和“步行能力”的指数相结合的集成运动规划器的自治系统。可通行性地图使步行机器人能够做出动态实时规划和反馈控制决策，根据步态特征进行调整，并根据周围地形和环境进行精确的足部放置。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使用计算线性代数来解决自主机器人研究中出现的大型和重要问题来激励学生。课程项目选自机器人技术的当代主题，例如从LiDAR点云构建地图，数据空间表示的机器学习以及移动的平台的反馈控制。此外，该项目还通过在底特律公立高中开展STEM领域的合作教育教学，向代表性不足的社区提供服务。该项目得到了机器人项目跨部门基础研究的支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同管理和资助该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。