Collaborative Research: Frameworks: Simulating Autonomous Agents and the Human-Autonomous Agent Interaction

协作研究：框架：模拟自主代理和人机交互

• 批准号: 2209791
• 负责人: Dan Negrut
• 金额: $ 187.52万
• 依托单位: University of Wisconsin-Madison
• 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: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209791&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Simulating; Autonomous

This project augments the Chrono computer simulation platform in transformative ways. Chrono's purpose is to predict through simulation the interplay between mechatronic systems, the environment they operate in, and humans with whom they might interact. The open-source simulation platform is slated to become a community-shared virtual investigation tool used to probe competing engineering designs and test hypotheses that would be too dangerous, difficult, or costly to verify through physical experiments. Chrono has been and will continue to be used in multiple fields and disciplines, e.g., terramechanics, astrophysics; soft matter physics; biomechanics; mechanical engineering; civil engineering; industrial engineering; and computer science. Specifically, it is used to engineer the 2023 VIPER lunar rover; relied upon by US Army experts in evaluating its wheeled and tracked vehicle designs; used in the US and Germany in the wind turbine industry; and involved in designing wave energy conversion solutions in Europe. Upon project completion, Chrono will become a simulation engine in Gazebo, which is widely used in robotics research; operate on the largest driving simulator in the US; empower research in the bio-robotics and field-robotics communities; and assist efforts in the broad area of automotive research carried out by a consortium of universities and companies under the umbrella of the Automotive Research Center. The educational impact of this project is threefold: training undergraduate, graduate, and post-doctoral students in a multi-disciplinary fashion that emphasizes advanced computing skills development; anchoring two new courses in autonomous vehicle control and simulation in robotics; and broadening participation in computing through a residential program on the campus of the University of Wisconsin-Madison that engages teachers and students from rural high-schools. Innovation and discovery are fueled by quality data. At its core, this project seeks to increase the share of this data that has simulation as its provenance. In this context, a multi-disciplinary team of 40 researchers augments and validates a physics-based simulation framework that empowers research in autonomous agents (AAs). The AAs operate in complex and unstructured dynamic environments and might engage in two-way interaction with humans or other AAs. This project enables Chrono to generate machine learning training data quickly and inexpensively; facilitates comparison of competing designs for assessing trade-offs; and gauges candidate design robustness via testing in simulation of corner-case scenarios. These tasks are accomplished by upgrading and extending Chrono to leverage recent computational dynamics innovations, e.g., a faster index 3 differential algebraic equations solver; a new approach to solving frictional contact problems; a real-time solver for handling flexible-body dynamics in soft robotics via nonlinear finite element analysis; a best-in-class simulator for terradynamics applications; reliance on just-in-time compiling for producing executables that are both problem- and hardware-optimized; a novel way for using mixed data representations for parsimonious storing of state information; and a scalable multi-agent framework that enables geographically-distributed, over the Internet, real-time simulation of human-AA interaction.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.

该项目以变革的方式增强了Chrono计算机模拟平台。Chrono的目的是通过仿真预测机电系统之间的相互作用，它们所处的环境以及它们可能与之交互的人类。开源仿真平台将成为一个社区共享的虚拟调查工具，用于探测竞争性的工程设计和测试假设，这些假设太危险、太困难或太昂贵，无法通过物理实验进行验证。Chrono已经并将继续用于多个领域和学科，例如，地球力学，天体物理学;软物质物理学;生物力学;机械工程;土木工程;工业工程;和计算机科学具体而言，它被用于设计2023 VIPER月球车;美国陆军专家在评估其轮式和履带式车辆设计时依赖它;在美国和德国用于风力涡轮机行业;并参与设计欧洲的波浪能转换解决方案。项目完成后，Chrono将成为Gazebo的仿真引擎，该引擎广泛用于机器人研究;在美国最大的驾驶模拟器上运行;为生物机器人和现场机器人社区的研究提供支持;并协助汽车研究中心下属的大学和公司联盟在汽车研究领域开展的广泛工作。该项目的教育影响有三个方面：以强调高级计算技能发展的多学科方式培训本科生，研究生和博士后学生;锚定两门自动驾驶车辆控制和机器人模拟的新课程;通过威斯康星大学麦迪逊分校校园内的住宿计划扩大对计算的参与，该计划吸引了来自农村高中的教师和学生。创新和发现由高质量的数据推动。该项目的核心是增加以模拟为来源的数据的份额。在这种情况下，一个由40名研究人员组成的多学科团队增强并验证了一个基于物理的仿真框架，该框架为自主代理（AAs）的研究提供了支持。AA在复杂和非结构化的动态环境中运行，并且可能与人类或其他AA进行双向交互。该项目使Chrono能够快速、廉价地生成机器学习训练数据;便于比较竞争设计以评估权衡;并通过模拟极端情况来衡量候选设计的鲁棒性。这些任务是通过升级和扩展Chrono来完成的，以利用最近的计算动力学创新，例如，一个更快的索引3微分代数方程求解器;一种解决摩擦接触问题的新方法;一个通过非线性有限元分析处理软机器人中柔性体动力学的实时求解器;一个同类最佳的陆地动力学应用模拟器;依赖于即时编译来生成问题和硬件优化的可执行文件;一种使用混合数据表示来节省存储状态信息的新方法;以及一个可扩展的多代理框架，它使地理上分布的，通过互联网，实时模拟人体-该奖项反映了美国国家科学基金会的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

On the Use of Half-Implicit Numerical Integration in Multibody Dynamics

半隐式数值积分在多体动力学中的应用

• DOI: 10.1115/1.4056183
• 发表时间: 2023
• 期刊: Journal of Computational and Nonlinear Dynamics
• 影响因子: 2
• 作者: Fang, Luning;Kissel, Alexandra;Zhang, Ruochun;Negrut, Dan
• 通讯作者: Negrut, Dan

Implementation implications on the performance of ANCF simulations

• DOI: 10.1016/j.ijnonlinmec.2022.104328
• 发表时间: 2022-12
• 期刊: International Journal of Non-Linear Mechanics
• 影响因子: 3.2
• 作者: Michael Taylor;R. Serban;D. Negrut

An efficiency comparison of different ANCF implementations

• DOI: 10.1016/j.ijnonlinmec.2022.104308
• 发表时间: 2022-11
• 期刊: International Journal of Non-Linear Mechanics
• 影响因子: 3.2
• 作者: Michael Taylor;R. Serban;D. Negrut