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项目的更广泛的影响/商业潜力是模拟技术，可用于验证和优化任何自动驾驶机器人车辆系统。这项技术最重要的好处是涉及挑战操作环境的部门的机器人（未拔出的空气，地面，海洋车辆），包括国防，太空，运输，交通，交付，建筑，建筑，能源，采矿和同意。对于活跃于这些领域的制造商和服务提供商而言，快速和可靠的部署是关键，其当前测试和验证方法的局限性可能成为大规模部署的重要障碍。当前验证过程包括现场测试，实验室实验或使用瓦解的软件工具隔离测试系统的不同部分。缺乏大规模的端到端测试数据，这些数据将使它们能够快速验证这些机器人的自主性堆栈可能会导致自主权通过连接的模块化数据驱动的模拟为机器人自主权提供可扩展的测试床，以使机器人的软件，硬件，硬件和人工智能可以通过操作中的所有阶段来进行多个风格的阶段，从而在多方面挑战。使用多模式数据收集中的一种新方法，通过表现效果渲染机制和针对基础引擎的混合模块化设计，模拟器生成大规模测试数据，以验证和优化单个或多机器人场景中的自主堆组件，以便在部署前部署前部署和加速验证的风险。潜在的模块发动机提供了物理上准确的传感器和对象建模，数据驱动的大规模性能环境建模，软件和硬件内部测试，人体反馈集成以及对机器学习模型的集成测试以及自动堆栈中的其他软件和硬件组件的集成测试以及培训的预测视觉建模。通过将验证加速100至100,000，该技术可以通过可靠的自主堆栈来实现内置的弹性，这些堆栈对干扰或错误的干扰或源头进行了优化和韧性。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估CRITERIA CRITERIA CRITERIA。