Vegetation encroachment around overhead lines poses significant risks to electricity reliability, public safety, and operational efficiency. UK Power Networks currently relies on manual tree cutting operations, which are labour-intensive, high-risk, and costly – particularly in rural and hard-to-access areas. This project aims to address these challenges through the design, prototype, and evaluation of drone-based tree cutting solutions that improve the safety, efficiency, and scalability of vegetation management around HV overhead lines. The key objectives being to reduce Customer Interruptions (CIs) and Customer Minutes Lost (CMLs), minimise safety risks to personnel and the public, and lower operational costs, whilst ensuring compliance with regulatory and environmental standards.
Benefits
The benefits identified through this project will demonstrate the technology’s suitability for effective vegetation management. The solution has the potential to reduce vegetation management costs and time, while improving safety by minimising the need for personnel to manually operate power tools or work at height or in hazardous terrains.
Although the project’s scope is limited to prototype development, its potential future deployment is expected to enhance vegetation management and, in turn, reduce CIs, CMLs and safety risks.
Learnings
Outcomes
To date, the Aero Prune project has delivered several outcomes that have advanced UK Power Networks’ understanding of how drone-based tree cutting solutions could be deployed safely and effectively on the electricity network. These outcomes have been generated through concept development, system design, early prototyping, and initial testing activities.
1. Development of a viable drone-enabled vegetation management concept
The project has established a clearly defined technical and operational concept for a drone-based pruning solution. This includes the specification of the aerial platform, cutting mechanism, sensing and control approach, and the constraints required for safe operation in proximity to live network assets. Early-stage engineering design work has validated the feasibility of mounting pruning equipment on a drone platform while maintaining stability and control.
2. Prototype development and initial performance validation
The project has progressed into the build and early testing of prototype systems. Initial prototype iterations have enabled controlled testing of key components, including cutting performance, flight stability under load, and operator control mechanisms.
3. Improved understanding of deployment constraints and network applicability
The project has generated valuable insight into where and how the solution could realistically be applied on the network.
Lessons Learnt
The project has confirmed that drone‑based vegetation cutting presents a fundamentally different technical challenge compared to existing drone inspection use cases. Integrating a cutting tool introduces additional forces, payload constraints and stability considerations which must be carefully managed. Early laboratory testing has demonstrated that cutting can be achieved within controlled conditions, but further work is required to validate performance in real‑world environments.
The project also identified practical operational constraints that will influence future deployment. These include limitations on drone weight to remain within permitted limits, battery endurance, and the need for suitably trained personnel with both drone and tree‑cutting competencies.
From a delivery perspective, the project demonstrated the importance of access to appropriate testing facilities. Progress has been affected by constraints such as weather conditions and limited availability of outdoor test sites, reinforcing the need for controlled environments to de‑risk testing and accelerate development.
Finally, the parallel approach (retrofitted commercial drone vs bespoke design) has been valuable in understanding the trade‑off between near‑term ease of deployment and long‑term optimisation for DNO requirements.
The Aero Prune project has been effective in advancing understanding of an emerging innovation area. While the method remains at an early stage of maturity (TRL 3), the learning generated provides a clear pathway for further development and supports informed decision‑making on future trials and potential deployment.
