SBIR Phase I: Precision Docking for Automated Charging of Unmanned Platforms and Electric Vehicles

SBIR第一期：无人平台和电动汽车自动充电精准对接

• 批准号: 2230483
• 负责人: Fred Daneshgaran
• 金额: $ 27.4万
• 依托单位: INNOTECH SYSTEMS LLC
• 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: 2023-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230483&HistoricalAwards=false
• 关键词: SBIR; Phase; Precision; Docking; Automated

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable automated and autonomous charging of a wide array of electric vehicles (e.g., electrified robots, cars and trucks, and drones) in adverse weather conditions. Examples include autonomous systems used in farming and logistics, electric vehicles in ports, and electric trucks. Despite tremendous progress and the proliferation of electric vehicles, an adequate, cost-effective, autonomous, and available charging infrastructure is currently lacking. This gap between need and availability is far worse in high power applications such as trucking. The proposed technology increases the deployment of autonomous mobile robots and drones in industries like agriculture and logistics which are currently suffering from labor issues. The technology may increase the deployment rate of commercial electric vehicle fleets that can contribute to reducing greenhouse gasses.This SBIR Phase I project proposes to solve a key problem in the automation of the charging process for electric vehicles, namely precision localization and docking in adverse weather conditions. The conventional methods of localization for docking (e.g., infrared or vision-based) have limitations such as insufficient precision and limited performance in less-than-optimal environmental conditions. This team presents a high precision automated docking solution in the presence of clutter and removes objects that are potentially harming the Line of Sight (LOS). The goals of the proposed research and development are: 1) establishing that time averaged, multi-path signal characteristics in multiple spectral bands can identify locations within a known map or during a close proximity approach of the electric vehicle to the charger; 2) developing models suitable for Monte-Carlo modeling and simulation of an indoor environment or region in proximity of a charger benchmarked by some measurements and using such simulations for verification success; and 3) developing a high precision transponder based on wideband signaling.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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是实现在恶劣天气条件下对各种电动汽车（例如电动机器人、汽车和卡车以及无人机）进行自动和自主充电。 例子包括农业和物流中使用的自主系统、港口的电动汽车和电动卡车。尽管电动汽车取得了巨大进步并且数量激增，但目前仍缺乏充足的、具有成本效益的、自主且可用的充电基础设施。在卡车运输等高功率应用中，需求和可用性之间的差距要严重得多。拟议的技术增加了自主移动机器人和无人机在目前面临劳动力问题的农业和物流等行业的部署。 该技术可能会提高商用电动汽车车队的部署率，从而有助于减少温室气体排放。SBIR一期项目旨在解决电动汽车充电过程自动化的一个关键问题，即恶劣天气条件下的精准定位和对接。传统的对接定位方法（例如基于红外或基于视觉的）具有局限性，例如在不太理想的环境条件下精度不足和性能有限。该团队在杂乱的情况下提出了高精度自动对接解决方案，并移除了可能损害视线 (LOS) 的物体。拟议研究和开发的目标是：1）建立多个频谱带中的时间平均多路径信号特征，可以识别已知地图内或电动汽车靠近充电器期间的位置； 2) 开发适合蒙特卡罗建模和模拟室内环境或充电器附近区域的模型，通过一些测量作为基准，并使用此类模拟来验证成功； 3) 开发基于宽带信号的高精度转发器。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。