SBIR Phase I: Enabling Safe, High-Speed Autonomous Mobile Robots in Warehouse Environments

SBIR 第一阶段：在仓库环境中实现安全、高速自主移动机器人

• 批准号: 2026137
• 负责人: John Spletzer
• 金额: $ 25.57万
• 依托单位: BOX ROBOTICS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026137&HistoricalAwards=false
• 关键词: SBIR; Phase; Enabling; Safe; Speed

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the development of an autonomous mobile robot (AMR) software stack designed to safely optimize material movement throughput in warehouses. Currently, AMRs in warehouses operate at less than half the speed of their human-driven counterparts due to the robot’s limited perception capabilities. A consequence of this decision is a dramatic reduction in the vehicle’s potential throughput, translating to a smaller facility fleet size with associated cost and material savings. Improved AMR operation will address warehouse labor shortages and improve safety. According to the Occupational Safety and Health Administration (OSHA), there are roughly 85-90 forklift fatalities each year, and over 7,000 injuries requiring days away from work. By automating forklift trucks with the proposed software stack, these safety incidents will be significantly reduced. This project will develop software associated with safer AMRs based on systems used in autonomous cars. This Small Business Innovation Research (SBIR) Phase I project is developing a software stack for autonomous mobile robots (AMRs) in warehouses to improve AMR agility and spatial awareness to human-like levels. The proposed innovation takes inspiration from advances in self-driving cars through the use of high-definition (HD) mapping and three-dimensional (3D) perception. These HD maps will leverage the latest advances in 3D LiDAR systems and deep learning approaches for object detection, enabling operation at higher speeds. This project will enable safe increases in AMR speeds from 2.0 to 3.0 m/s. To ensure vehicle safety is not compromised, systems will be tested to and meet the American National Standards Institute/Industrial Truck Standards Development Foundation (ANSI/ITSDF) B56.5 safety standard for object detection requirements.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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将是开发一个自主移动机器人（AMR）软件堆栈，旨在安全优化仓库中的物料移动吞吐量。目前，由于机器人有限的感知能力，仓库中的amr的运行速度还不到人类驱动的amr的一半。这一决定的结果是车辆的潜在吞吐量大幅降低，转化为更小的设施车队规模，节约相关成本和材料。改进AMR操作将解决仓库劳动力短缺问题并提高安全性。根据职业安全与健康管理局（OSHA）的数据，每年大约有85-90起叉车死亡事故，超过7000人受伤，需要离开工作岗位。通过使用所提出的软件栈实现叉车的自动化，这些安全事故将大大减少。该项目将基于自动驾驶汽车中使用的系统开发与更安全的amr相关的软件。这项小企业创新研究（SBIR）第一阶段项目正在为仓库中的自主移动机器人（AMR）开发软件堆栈，以提高AMR的敏捷性和空间感知能力，达到类似人类的水平。这一创新方案的灵感来自于使用高清地图和三维感知技术的自动驾驶汽车。这些高清地图将利用3D激光雷达系统和深度学习方法的最新进展进行目标检测，从而实现更高的运行速度。该项目将使AMR速度从2.0米/秒安全提高到3.0米/秒。为了确保车辆安全不受影响，系统将进行测试，以满足美国国家标准协会/工业卡车标准发展基金会（ANSI/ITSDF） B56.5安全标准的目标检测要求。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。