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

该项目以变革性的方式增强了Chrono计算机仿真平台。Chrono的目的是通过模拟来预测机电系统、它们所处的环境以及它们可能与之互动的人类之间的相互作用。这个开源仿真平台将成为一个社区共享的虚拟调查工具，用于探索竞争的工程设计，并测试那些通过物理实验验证过于危险、困难或昂贵的假设。Chrono已经并将继续在多个领域和学科中使用，例如，地形力学，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;还有计算机科学。具体来说，它被用来设计2023年的“蝰蛇”月球车；依靠美国陆军专家评估其轮式和履带式车辆设计；用于美国和德国的风力涡轮机行业；并参与了欧洲波浪能转换解决方案的设计。项目完成后，Chrono将成为Gazebo中的仿真引擎，广泛应用于机器人研究；在美国最大的驾驶模拟器上操作；授权生物机器人和野外机器人社区的研究；并协助在汽车研究中心的保护伞下由大学和公司组成的财团开展的广泛的汽车研究工作。这个项目的教育影响是三重的：以多学科的方式培养本科生、研究生和博士后，强调高级计算技能的发展；开设自主车辆控制和机器人仿真两门新课程；通过威斯康星大学麦迪逊分校校园内的一个住宅项目，让来自农村高中的教师和学生参与进来，扩大人们对计算机的参与。高质量的数据推动了创新和发现。其核心是，该项目旨在增加以模拟为来源的数据的份额。在此背景下，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模拟框架，该框架可以为自主代理（autonomous agents, aa）的研究提供支持。AAs在复杂和非结构化的动态环境中运行，并可能与人类或其他AAs进行双向交互。该项目使Chrono能够快速、低成本地生成机器学习训练数据；促进竞争设计的比较，以评估权衡；并通过模拟角落案例场景的测试来衡量候选设计的稳健性。这些任务是通过升级和扩展Chrono来实现的，以利用最近的计算动力学创新，例如，更快的索引3微分代数方程求解器；一种求解摩擦接触问题的新方法基于非线性有限元分析的软机器人柔性体动力学实时求解器一流的地面动力学应用模拟器；依赖即时编译来生成问题优化和硬件优化的可执行文件；一种使用混合数据表示简化状态信息存储的新方法以及一个可扩展的多代理框架，该框架可以通过互联网实现地理分布的人机交互的实时模拟。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。

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.