I-Corps: Advanced simulation system for end-to-end autonomy validation in robot vehicle systems

I-Corps：用于机器人车辆系统端到端自主验证的高级仿真系统

• 批准号: 2331047
• 负责人: Susan Fussell
• 金额: $ 5万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331047&HistoricalAwards=false
• 关键词: Corps; Advanced; simulation; system; end

The broader impact/commercial potential of this I-Corps project is in simulation technology that can be used for validation and optimization of any autonomous robot vehicle system. This technology’s most significant benefit is for robots (Uncrewed Air, Ground, Marine Vehicles) in sectors involving challenging operation environments including defense, space, transportation, delivery, construction, energy, mining, and agriculture. Unlocking rapid and reliable deployment at scale is key for the manufacturers and service providers active in these sectors and the limitations of their current method for test and validation can become a significant obstacle for deployment at scale. Current processes for validation include field tests, lab experiments or using disintegrated software tools to test different parts of the system in isolation. Lack of large-scale end-to-end test data that would enable them to rapidly validate the autonomy stack of these robots can result in an autonomy that cannot be relied upon eventually leading to increased operation inefficiencies, manual labor, risks and costs in development and resources.This I-Corps project is based on the development of an advanced modular simulation system to validate, enhance, and optimize the autonomy stack in autonomous robots end-to-end. It provides scalable testbeds for robot autonomy through a connected modular data-driven simulation where the robots’ software, hardware, and artificial intelligence can be tested through all phases of operation in multi-fidelity virtual spaces and challenged in different realistic scenarios. Using a novel approach in multi-modal data collection, through a performant rendering mechanism and a hybrid modular design for underlying engines, the simulator generates large-scale test data to validate and optimize the components in autonomy stack for single or multi-robot scenarios before deployment thus mitigating risk of deployment and accelerating validation. The underlying modular engines provide physically accurate sensor and object modeling, data-driven large-scale performant environment modeling, software and hardware in-the-loop testing, human feedback integration and predictive vision modeling for integrated test and training of machine learning models and other software and hardware components in the autonomy stack. By accelerating validation by a factor of 100 to 100,000, this technology can enable built-in resiliency through reliable autonomy stacks that are optimized and resilient against interference or sources of errors.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 I-Corps 项目更广泛的影响/商业潜力在于模拟技术，可用于验证和优化任何自主机器人车辆系统。该技术最显着的好处是适用于涉及具有挑战性操作环境的领域的机器人（无人驾驶空中、地面、海上车辆），包括国防、太空、运输、交付、建筑、能源、采矿和农业。对于活跃在这些领域的制造商和服务提供商来说，实现快速、可靠的大规模部署至关重要，而他们当前的测试和验证方法的局限性可能会成为大规模部署的重大障碍。当前的验证过程包括现场测试、实验室实验或使用分解的软件工具来单独测试系统的不同部分。缺乏大规模的端到端测试数据使他们能够快速验证这些机器人的自主堆栈，可能会导致无法依赖自主性，最终导致操作效率低下、体力劳动、开发和资源方面的风险和成本增加。这个I-Corps项目基于先进的模块化仿真系统的开发，以端到端地验证、增强和优化自主机器人的自主堆栈。它通过连接的模块化数据驱动模拟为机器人自主提供可扩展的测试平台，其中机器人的软件、硬件和人工智能可以在多保真虚拟空间中的所有操作阶段进行测试，并在不同的现实场景中接受挑战。该模拟器采用多模态数据收集的新颖方法，通过高性能渲染机制和底层引擎的混合模块化设计，生成大规模测试数据，在部署之前针对单机器人或多机器人场景验证和优化自主堆栈中的组件，从而降低部署风险并加速验证。底层模块化引擎提供物理精确的传感器和对象建模、数据驱动的大规模高性能环境建模、软件和硬件在环测试、人类反馈集成和预测视觉建模，用于机器学习模型以及自主堆栈中其他软件和硬件组件的集成测试和训练。通过将验证速度加快 100 至 100,000 倍，该技术可以通过可靠的自主堆栈实现内置弹性，这些堆栈经过优化并具有抵御干扰或错误来源的弹性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。