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

该项目以变革性的方式扩充了Chrono计算机模拟平台。Chrono的目的是通过模拟预测机电系统之间的相互作用，它们在其中运行的环境，以及它们可能与之交互的人类。这个开源的模拟平台将成为一个社区共享的虚拟调查工具，用于探索相互竞争的工程设计，并测试那些太危险、太难或太昂贵而无法通过物理实验验证的假设。CHERO已经并将继续用于多个领域和学科，例如，大地力学、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。具体地说，它被用于设计2023年的Viper月球车；被美国陆军专家用于评估其轮式和履带式车辆设计；在美国和德国用于风力涡轮机行业；并参与设计欧洲的波能转换解决方案。项目完成后，Chrono将成为Gazebo的模拟引擎，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；支持生物机器人和现场机器人社区的研究；并协助由大学和公司组成的财团在汽车研究中心的框架下开展广泛的汽车研究领域的工作。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；在机器人学中固定两门新的自动车辆控制和模拟课程；通过威斯康星大学麦迪逊分校的一个住宅项目扩大对计算的参与，该项目吸引了农村高中的教师和学生。高质量的数据推动了创新和发现。这个项目的核心是寻求增加这些以模拟为出处的数据的份额。在这一背景下，一个由40名研究人员组成的多学科团队对一个基于物理的模拟框架进行了扩充和验证，该框架支持自主代理(AA)的研究。AA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AA进行双向交互。该项目使Chrono能够快速且廉价地生成机器学习训练数据；促进评估权衡的竞争设计的比较；并通过在角落案例场景的模拟中进行测试来衡量候选设计的健壮性。这些任务是通过升级和扩展Chrono以利用最新的计算动力学创新来完成的，例如，更快的索引3微分代数方程求解器；解决摩擦接触问题的新方法；通过非线性有限元分析处理软机器人中的柔体动力学的实时求解器；用于地形动力学应用的同类最佳模拟器；依赖实时编译来生成既针对问题又针对硬件优化的可执行文件；使用混合数据表示法以简约地存储状态信息的新方法；这一奖项反映了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 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.