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

该项目开发了一个开源系统，允许腿部机器人在非结构化环境中进行自主探索和环境监测，例如在森林中。一个中心主题是设计和部署一个统一和集成的框架，该框架利用机载感觉模式的冗余，并与反馈控制和规划交叉整合。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，这些领域经常被孤立地研究。通过将这些领域作为一个系统问题进行研究，研究人员预计非结构化环境中的自主系统将取得重大进展。研究结果在数字系列3D两足动物和迷你猎豹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机器人的最先进水平。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--根据收集的传感器测量数据注册到已知的高清晰度地图--是不可能的，并对自主性产生了不利影响。该项目开发了一种实时多层密集语义占有率地图，带有一组扩展的地形标签和一个与自主系统集成的运动规划器相结合的“步行能力”指数。可穿越地图使行走机器人能够做出动态的实时规划和反馈控制决策，根据周围的地形和环境调整步态特征和精确的脚位置。主要研究人员正在共同开发一门大学新生课程，通过教授实习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重大问题来启发学生。课程项目选自机器人学的当代主题，例如，从激光雷达点云构建地图，用于数据空间表示的机器学习，以及移动平台的反馈控制。此外，该项目通过在底特律公立高中的STEM领域的合作教育教学，致力于向未被充分代表的社区提供服务。该项目由跨部门机器人基础研究计划支持，该计划由工程指导委员会(ENG)和计算机和信息科学与工程指导委员会(CEISE)联合管理和资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.