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

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.