SBIR Phase I: Dense, Socially-Compliant, Autonomous Delivery Robot

SBIR 第一阶段：密集、符合社会规范的自主送货机器人

• 批准号: 2136783
• 负责人: Utsav Patel
• 金额: $ 25.54万
• 依托单位: INCEPTION ROBOTICS, LLC
• 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: 2022-03-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136783&HistoricalAwards=false
• 关键词: SBIR; Phase; Dense; Socially; Compliant

The broader impact/commercial potential of this Small Business Innovation Research Phase I project is to enable autonomous mobile robots (AMRs) to operate in densely crowded spaces in a safe and socially compliant/acceptable manner. A key potential outcome is the development of a collision avoidance method based on Deep Reinforcement Learning (DRL). This method would be capable of handling dense crowds and optimized to run on compact and power-efficient embedded processors. Such abilities would increase the commercial potential and adoption of learning-based navigation methods that have demonstrated excellent collision avoidance and noise handling capabilities. The technology may unlock commercial opportunities by deploying AMRs in the airport, retail, healthcare, and hospitality industries, where the environments are highly dense and dynamic. The airport industry may derive postive impacts from AMRs that can navigate in complex, indoor environments where global positioning systems (GPS) are not allowed by providing contactless deliveries of food, beverages, and other retail products to travelers at the gate.This Small Business Innovation Research (SBIR) Phase I project investigates a hybrid collision avoidance approach enabling autonomous mobile robots (AMRs) to operate safely in dense crowds, while being socially-compliant in sparse scenarios. Preliminary research has shown that Deep Reinforcement Learning (DRL)-based approaches can compute collision-free robot velocities with inaccurate, uncertain perception data. The proposed DRL-based model will be implemented as an optimized neural network that works on power and cost-efficient embedded processors. The key technical hurdles in this technology are: the DRL model trained in simulation may not perform well in real-world environments (known as sim-to-real gap), the fully-trained DRL model may have some performance degradation compared to the company’s current DRL models due to the lower number of parameters used to run on embedded processors, and the localization modules could compute erroneous locations when the AMR is navigating through a dense crowd due to occlusions. The key objectives of Phase I are to address these challenges.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.

该小型企业创新研究第一阶段项目的更广泛影响/商业潜力是使自主移动机器人（AMR）能够以安全且符合社会要求/可接受的方式在拥挤的空间中运行，一个关键的潜在结果是发生碰撞。基于深度强化学习（DRL）的回避方法该方法将能够处理密集的人群，并经过优化以在紧凑且节能的嵌入式处理器上运行，这种能力将增加基于学习的导航方法的商业潜力和采用。证明了该技术可以通过在机场、零售、医疗保健和酒店业等环境高度密集和动态的领域部署 AMR 来释放商业机会。机场行业可以从能够导航的 AMR 中获得积极影响。在不允许使用全球定位系统 (GPS) 的复杂室内环境中，通过提供食品、饮料和其他零售产品的非接触式交付到门口。这个小型企业创新研究 (SBIR) 第一阶段项目研究了混合防撞技术实现自主移动的方法初步研究表明，基于深度强化学习（DRL）的方法可以使用不准确、不确定的感知数据计算无碰撞机器人速度。基于模型的模型将被实现为一个优化的神经网络，该网络适用于功率和成本效益高的嵌入式处理器。该技术的关键技术障碍是：在模拟中训练的 DRL 模型可能在现实环境中表现不佳。 （称为模拟与真实差距），与公司当前的 DRL 模型相比，经过充分训练的 DRL 模型可能会出现一些性能下降，因为用于在嵌入式处理器上运行的参数数量较少，并且本地化模块可能会计算错误第一阶段的主要目标是解决这些挑战。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力和能力进行评估，被认为值得支持。更广泛的影响审查标准。