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.

这个小型企业创新研究阶段I项目的更广泛的影响/商业潜力是使自动移动机器人（AMRS）能够以安全且具有社会符合社会性/可接受的方式在不拥挤的空间中运行。一个关键的潜在结果是开发基于深度强化学习（DRL）的避免碰撞方法。该方法将能够处理密集的人群并进行优化，以在紧凑和效率的嵌入式处理器上运行。这样的能力将提高基于学习的导航方法的商业潜力和采用，这些方法表现出极好的避免碰撞和噪音处理能力。该技术可以通过在机场，零售，医疗保健和酒店业中部署AMR来释放商业机会，在该机场，环境高度密集和动态。 The airport industry may derive postive impacts from AMRs that can navigation in complex, indoor environments where global positioning systems (GPS) are not allowed by providing contactless delivery of food, bedrooms, 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在稀疏场景中符合社会统一。初步研究表明，基于深的加强学习（DRL）的方法可以使用不准确的，不确定的感知数据来计算无碰撞的机器人速度。拟议的基于DRL的模型将被实施为优化的神经元网络，可在功率和具有成本效益的嵌入式处理器上。这项技术中的主要技术障碍是：在模拟中训练的DRL模型在现实世界环境（称为SIM到真实的差距）中可能表现不佳，与公司当前的DRL模型相比，全面训练的DRL模型可能具有一定的性能降级，这是由于在嵌入式过程中使用嵌入式误差而在嵌入式过程中运行的参数较低，因此在嵌入式过程中运行的是较低的参数，并且是在嵌入式过程中运行的。闭塞。第一阶段的关键目标是应对这些挑战。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是宝贵的支持。