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

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是在恶劣天气条件下实现各种电动汽车（例如电动机器人、汽车和卡车以及无人机）的自动和自主充电。例子包括用于农业和物流的自主系统、港口的电动汽车和电动卡车。尽管电动汽车取得了巨大的进步和普及，但目前仍缺乏足够的、具有成本效益的、自动的、可用的充电基础设施。这种需求和可用性之间的差距在卡车运输等高功率应用中更为严重。拟议中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，这些行业目前正遭受劳动力问题的困扰。这项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一期项目提出解决电动汽车充电过程自动化的关键问题，即恶劣天气条件下的精确定位与对接。传统的对接定位方法（例如，红外或基于视觉的）具有局限性，例如精度不足和在非最佳环境条件下的有限性能。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移除可能损害视线（LOS）的物体。提出的研究和开发目标是：1)建立时间平均，多频谱带的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时的位置；2)开发适合蒙特卡罗建模和模拟室内环境或区域附近的充电器的模型，通过一些测量基准，并使用这种模拟验证成功；3)研制了基于宽带信号的高精度转发器。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。

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.