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

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是在恶劣天气条件下实现对各种电动汽车(例如电动机器人、汽车和卡车以及无人机)的自动化和自主充电。例子包括农业和物流中使用的自动驾驶系统，港口中的电动汽车，以及电动卡车。尽管取得了巨大的进步和电动汽车的激增，但目前缺乏足够、经济高效、自主和可用的充电基础设施。在卡车运输等高功率应用中，这种需求和可用性之间的差距要严重得多。这项拟议的技术增加了自主移动机器人和无人机在农业和物流等行业的部署，这些行业目前正受到劳工问题的困扰。该技术可能会提高商用电动汽车车队的部署速度，有助于减少温室气体排放。这项SBIR第一阶段项目提出解决电动汽车充电过程自动化的一个关键问题，即在恶劣天气条件下的精确定位和对接。传统的对接定位方法(例如，红外线或基于视觉的)具有局限性，如在非最佳环境条件下精度不足和性能有限。该团队提出了一种高精度的自动对接解决方案，可在杂物存在的情况下移除可能损害视线(LOS)的物体。拟议研究和开发的目标是：1)确定多个频谱段的时间平均、多路径信号特征可以识别已知地图内的位置或电动汽车接近充电器时的位置；2)开发适合于蒙特卡罗建模和模拟充电器附近室内环境或区域的模型，并使用此类模拟成功验证；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。

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.