FollowPV - Developing autonomous unmanned aerial vehicles with spatial awareness for improved image quality from solar farm inspections

FollowPV - 开发具有空间感知能力的自主无人机，以提高太阳能发电场检查的图像质量

• 批准号: 98378
• 金额: $ 35.71万
• 依托单位: ABOVE SURVEYING LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=98378
• 关键词: FollowPV; Developing; autonomous; unmanned; aerial

The FollowPV project plans to develop a 'self-driving' (semi-automated) drone system for inspecting solar farms. Our device will allow a drone to follow rows of solar panels in the same way that a 'self-driving' car is able to keep in lane. However, unlike a car, a drone is not connected to the road by wheels. Therefore, our device must also enable the drone to follow the rise and fall of solar panels over uneven terrain.Solar farms are critical to the UK's energy supply and to reducing emissions, so they need to be inspected regularly for defective components. We use drones with specialist cameras to inspect entire solar farms in a single visit, which is more efficient than inspecting panels on-foot. This reduces maintenance costs of solar farms allowing operation at optimum condition, which helps keep down the cost of electricity to the consumer.However, some defects are only visible very close-up, yet reveal early systemic degenerative problems for the future. Current drones are not accurate enough to fly very close to solar panels, and therefore manual inspections are sometimes still needed. These are very time-consuming, expensive, and involve health and safety risk.To use a drone to capture this ultra-high detail imagery, we want to fly much closer to the panels (within 5m). However, in the same way that 'sat nav' is not accurate enough to control the steering wheel of a self-driving car, then GPS is not accurate enough to control a drone so near to the solar panels. To do this accurately, the drone (like the car) needs to be able to 'see' its environment, and to understand and use this information to make tiny control adjustments. This requires special sensors on the drone, and onboard artificial intelligence (AI), which can rapidly process and make in-flight corrections.Loughborough University (LU) and the University of Essex (UoE) already have expertise in utilising drone technology with this capability for use in 'smart agriculture' (e.g. crop disease monitoring), but similar technology can be applied to solar farms.In our proposed partnership, the expertise of LU and UoE in drone automation will be combined with _Above_'s expertise in solar farm inspection and worldwide network of international customers and commercial partners. Ultimately, our desire with this project is to ensure that the UK and the world's solar plants are working as efficiently as possible, thus reducing our reliance on fossil fuels.

后续项目计划开发一个“自动驾驶”（半自动化的无人机系统，用于检查太阳能农场。我们的设备将允许无人机以与“自动驾驶”汽车能够在车道上保持相同的方式遵循一排太阳能电池板。但是，与汽车不同，无人机与车轮没有连接到道路。因此，我们的设备还必须使无人机在不平坦的地形上遵循太阳能电池板的上升和下降。野性农场对于英国的能源供应和减少排放至关重要，因此需要定期检查它们是否有缺陷的组件。我们使用带有专业摄像头的无人机在一次访问中检查整个太阳能农场，这比在脚上检查面板更有效。这降低了太阳能农场的维护成本，允许在最佳状态下运行，这有助于降低电力成本。当前的无人机不够准确，无法非常接近太阳能电池板，因此有时仍需要进行手动检查。这些非常耗时，昂贵，并且涉及健康和安全风险。要使用无人机捕获这种超高的细节图像，我们希望将其靠近面板（5m以内）。但是，就像“ SAT NAV”不够准确的方式以控制自动驾驶汽车的方向盘一样，GP的准确性不足以控制靠近太阳能电池板的无人机。为了准确地执行此操作，无人机（像汽车一样）需要能够“看到”其环境，并了解和使用此信息来进行微小的控制调整。这需要在无人机和机上人工智能（AI）上进行特殊传感器，该传感器可以快速处理并进行机上更正。用于用于“智能农业”（例如农作物疾病监测），但可以应用于太阳能农场。在我们提出的合作伙伴关系中，Lu和Uoe在无人机自动化中的专业知识将与_above_在Solar Farm检验和全球检验中的专业知识相结合国际客户和商业合作伙伴网络。最终，我们对这个项目的愿望是确保英国和世界太阳能工厂尽可能有效地工作，从而减少我们对化石燃料的依赖。