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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是实现各种电动汽车的自动和自主充电（例如，电气化机器人、汽车和卡车以及无人机）。 例子包括用于农业和物流的自动化系统，港口的电动汽车和电动卡车。尽管电动汽车取得了巨大的进步和普及，但目前缺乏足够的、具有成本效益的、自主的和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。拟议中的技术增加了自主移动的机器人和无人机在农业和物流等行业的部署，这些行业目前正面临劳动力问题。 该技术可能会提高商用电动汽车车队的部署率，有助于减少温室气体排放。SBIR第一阶段项目旨在解决电动汽车充电过程自动化的关键问题，即在恶劣天气条件下的精确定位和对接。用于对接的传统定位方法（例如，红外线或基于视觉）具有诸如在不太理想的环境条件下精度不足和性能受限的局限性。该团队提出了一种高精度的自动对接解决方案，可以在杂乱的情况下消除可能损害视线（LOS）的物体。所提出的研究和开发的目标是：1）建立多个谱带中的时间平均的多径信号特性可以识别已知地图内的位置或在电动车辆接近充电器期间的位置; 2）建立适合于Monte-通过一些测量基准对充电器附近的室内环境或区域进行Carlo建模和模拟，并使用此类模拟进行验证成功;以及3）开发基于宽带信号的高精度应答器。该奖项反映了NSF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。