CAREER: Interactive Decision-making and Resilient Planning for Safe Legged Locomotion and Navigation.

职业：交互式决策和弹性规划，实现安全的腿部运动和导航。

• 批准号: 2144309
• 负责人: Ye Zhao
• 金额: $ 59.54万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144309&HistoricalAwards=false
• 关键词: CAREER; Interactive; Decision; making; Resilient

This Faculty Early Career Development (CAREER) project aims to bridge fundamental gaps between state-of-the-art robot task and motion planning and the ultimate goals of safe and autonomous robot locomotion. This work will resolve major computational hurdles that have hindered the use of symbolic planning and decision-making methodologies on the highly nonlinear, hybrid, and under-actuated legged systems, reducing their ability to reliably operate in unstructured environments, efficiently collaborate with robot teammates, and safely interact with humans. Specific long-standing challenges to be addressed are robustness to contact terrain uncertainty, locomotion task generalization, navigation safety coupled with complex locomotion dynamics, and proactive interaction with pedestrians to avoid collisions. This project will move legged robotic systems from the research laboratory, and into challenging application domains such as disaster first responders, surveillance in civil and mechanical infrastructures, and planting in agricultural environments. The research is complemented by education activities to (i) integrate undergraduate students into a new experiment-focused research program through the multi-semester Vertically Integrated Program (VIP) at Georgia Institute of Technology; (ii) host high school students from Atlanta minority-serving public schools to perform research and enhance underrepresented population participation in the greater Atlanta area; and (iii) broaden the societal impact by disseminating the integrated robotics research through an interactive workshop. This project will develop a novel task and motion planning framework for bipedal locomotion interacting with complex environments. In particular, it will retain and augment the strengths of full-body-dynamics-aware trajectory optimization (TO) with the complementary capabilities of symbolic planning and policy learning for environment abstraction, formal task specification design, and robust decision-making. This project has a large focus on experimental evaluations to enable transformative new legged navigation functionalities in real-world environments. The research outcome will enable (i) safe navigation in partially observable environments and proactive interaction with pedestrians while incorporating low-level locomotion dynamics constraints, (ii) versatile contact behavior planning and robustness reasoning through risk-sensitive TO and adversarially regularized policy learning, and (iii) real-time locomotion failure recovery capability via behavior trees to address unexpected environment interventions. This project will advance the state of the art in safe and autonomous navigation of legged systems and pave the road for future studies of heterogeneous robot teaming.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.

这个教师早期职业发展（Career）项目旨在弥合最先进的机器人任务和运动规划与安全和自主机器人运动的最终目标之间的根本差距。这项工作将解决主要的计算障碍，这些障碍阻碍了在高度非线性、混合和欠驱动的腿系统中使用符号规划和决策方法，降低了它们在非结构化环境中可靠运行的能力，有效地与机器人队友协作，并安全地与人类互动。需要解决的具体长期挑战是对接触地形不确定性的鲁棒性，运动任务泛化，复杂运动动力学的导航安全性，以及与行人的主动交互以避免碰撞。该项目将把有腿机器人系统从研究实验室转移到具有挑战性的应用领域，如灾难第一响应者，民用和机械基础设施的监视，以及农业环境中的种植。本研究辅以以下教育活动：(i)通过乔治亚理工学院的多学期垂直整合项目（VIP），将本科生融入一个新的以实验为重点的研究项目；（ii）接待来自亚特兰大少数族裔公立学校的高中生进行研究，并加强大亚特兰大地区未被充分代表的人口的参与；（三）通过互动研讨会传播集成机器人研究，扩大社会影响。该项目将开发一种新的任务和运动规划框架，用于两足运动与复杂环境的相互作用。特别是，它将保留并增强全身动态感知轨迹优化（TO）的优势，并具有符号规划和环境抽象、正式任务规范设计和稳健决策的策略学习的补充能力。该项目主要侧重于实验评估，以在现实环境中实现变革性的新腿部导航功能。研究成果将实现(i)在部分可观察环境中安全导航，并在结合低水平运动动力学约束的情况下与行人进行主动交互，（ii）通过风险敏感TO和对抗正则化策略学习实现通用接触行为规划和鲁棒性推理，以及（iii）通过行为树解决意外环境干预的实时运动故障恢复能力。该项目将推动腿式系统安全和自主导航技术的发展，并为未来异构机器人团队的研究铺平道路。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Abstraction-Based Planning for Uncertainty-Aware Legged Navigation

• DOI: 10.1109/ojcsys.2023.3296000
• 发表时间: 2023
• 期刊: IEEE Open Journal of Control Systems
• 作者: Jesse Jiang;S. Coogan;Ye Zhao

GPF-BG: A Hierarchical Vision-Based Planning Framework for Safe Quadrupedal Navigation

• DOI: 10.1109/icra48891.2023.10160804
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Shiyu Feng;Ziyi Zhou;Justin S. Smith;M. Asselmeier;Ye Zhao;P. Vela

Safe Hierarchical Navigation in Crowded Dynamic Uncertain Environments

• DOI: 10.1109/cdc51059.2022.9992674
• 发表时间: 2022-12
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC)
• 作者: Hongyi Chen;Shiyu Feng;Ye Zhao;Changliu Liu;P. Vela

Leveraging Heterogeneous Capabilities in Multi-Agent Systems for Environmental Conflict Resolution

利用多智能体系统中的异构功能解决环境冲突

• DOI: 10.1109/ssrr56537.2022.10018728
• 发表时间: 2022
• 期刊: and Rescue Robotics (SSRR
• 作者: Cao, Michael E.;Ni, Xinpei;Warnke, Jonas;Han, Yunhai;Coogan, Samuel;Zhao, Ye
• 通讯作者: Zhao, Ye