NGRNMaterial prepared by Oleksandra Artemenko, NGRN Analyst, based on research by the University of Southern Denmark
The development and integration of advanced drone charging technologies have redefined the limits of their operational efficiency and autonomy, particularly in combat conditions. One such breakthrough is autonomous systems capable of drawing energy directly from power lines, enabling drones to perform extended missions without external infrastructure. This innovation is rooted in the convergence of mechanical design, electromagnetic energy harvesting, and sophisticated computational systems.
Technological component
At the core of this approach lies the deployment of a robust capture system designed to latch onto overhead power lines. This mechanism relies on a two-conductor current transformer that is passively activated as the drone ascends towards the power line. The gripper transitions from an open to a closed state as it captures the cable, with the design ensuring minimal actuation force. This passive triggering system eliminates the need for additional sensors to verify contact, as the gripper itself serves as a functional indicator of successful attachment. Its operation is further optimized by a magnetic control circuit, which modulates the magnetic field within the core, balancing two goals—secure attachment and efficient energy transfer. The upward movement of the drone as it approaches the power line shifts the transformer into a closed state, forming a strong connection. This is achieved via a cable guidance system that accurately directs the power line into the gripper, even under adverse conditions such as wind or cable oscillations. The mechanical design is optimized to minimize the force required to close the gripper, allowing for efficient deployment on drones with lower thrust-to-weight ratios.
Drone components
Once the gripper secures the power line, the system transitions to the energy harvesting phase. The split-core transformer not only provides reliable attachment but also serves as an energy harvester. It harnesses the electromagnetic field generated by the alternating current of the power line to inductively transfer energy to the drone’s battery. The magnetic control circuit within the gripper regulates this process, dynamically adjusting to the power line’s current level and the drone battery’s state. For instance, when the power line current is low, the system increases holding force using direct current (DC) from the drone’s battery to ensure stability. When the grid current is sufficiently high, the system activates charging mode, prioritizing battery replenishment. Additionally, in scenarios where the battery is fully charged, the system uses the AC magnetic field to maintain grip without consuming energy from the drone’s battery.
Gripper mechanism components
The efficiency of such energy transfer is enhanced by the implementation of advanced control algorithms. The Magnetic Management Circuit (MMC) plays a central role, coordinating various operational modes of the gripper to ensure optimal energy utilization. The MMC dynamically adjusts the magnetizing current responsible for maintaining the gripper’s hold on the power line. It also manages transitions between modes, such as switching from holding to charging or quickly releasing the grip when the drone needs to take off. Such precise control is achieved through real-time data exchange between the MMC and the drone’s onboard computational system, which monitors parameters such as grid current, battery voltage, and charging power.
The computational foundation underlying this system is equally sophisticated. At its core lies a perception system that combines an mmWave radar with high-resolution RGB cameras. This sensor combination enables the drone to detect and track power lines with exceptional precision. The radar provides a three-dimensional representation of the power line’s position, while the camera captures visual details. These data are processed using advanced algorithms, including Kalman filters, which integrate odometry estimates from the drone’s onboard system. The result is an accurate real-time map of the power line environment, allowing the drone to perform complex maneuvers such as aligning with the power line, landing, and takeoff.
These perception capabilities feed into the drone’s autonomy system, responsible for high-level mission planning and execution. The autonomy system breaks down complex tasks into discrete flight primitives, such as “FlyToCable” or “LandOnCable.” These primitives are executed using trajectory planning algorithms that ensure smooth and efficient movements. The algorithms account for environmental factors such as wind, cable oscillations, and obstacles, enabling the drone to operate reliably in diverse conditions. The autonomy system also integrates with the drone’s power management subsystem, coordinating the initiation and termination of charging operations based on the battery state and mission requirements.
Testing
The efficiency of this integrated system was rigorously tested under real-world conditions at Odense Airport in Denmark by the Department of Mechanical and Electrical Engineering. Experimental trials demonstrated the system’s ability to sustain continuous operation over extended periods. The integrated system was controlled via a WiFi connection to a laptop with a GUI interface. The charging process could be started and stopped with the press of a button, representing the only operator intervention during testing. During outdoor trials using three-phase power lines, the 4.3 kg drone completed multiple inspection and charging cycles, each lasting several hours. A key factor in the system’s success was its adaptability to varying current levels in the power line. Charging power was shown to vary with current strength, ranging from 15 W at 100 A to 181 W at 1000 A. This variability allows the system to optimize charging time, minimizing downtime and maximizing operational efficiency. In practice, charging 55% of the drone’s battery capacity could take as little as 28 minutes at high current levels (100 A to 1000 A) or up to 346 minutes (5.8 hours) at lower currents. The longest continuous test lasted over two hours and included five charging cycles.
This technology has far-reaching implications. By eliminating the need for ground charging stations or manual battery replacement, the system significantly enhances the flexibility and scalability of drone operations. It is particularly well-suited for use in remote or hard-to-reach areas where traditional charging infrastructure is impractical. Potential use cases include infrastructure inspection, environmental monitoring, weather response, and military reconnaissance. In each of these scenarios, the ability to sustain operations without external support provides a clear advantage, reducing costs and logistical complexity.
Integration of Artificial Intelligence
With AI managing the process, drones can continue performing tasks in the field without interrupting operations to return to base. Additionally, this autonomy enables a swarm of drones to coordinate their actions in real-time: AI analyzes the charge level of each drone, directing them to the nearest power lines for recharging, thereby maintaining operational continuity and expanding territorial coverage.
AI can also oversee energy conversion, regulating the output voltage from power lines and adjusting the conversion rate to prevent overloads. The AI-driven power management system (PMS) ensures not only safe charging but also the distribution of energy across the drone’s various components, from sensors to communication systems. The PMS continuously monitors the battery charge level from the power lines and the state of energy conversion components. Upon reaching full battery capacity, AI automatically reduces input power or stops charging altogether to prevent overcharging, which could negatively impact battery longevity. Additionally, the PMS ensures optimal energy allocation among the drone’s critical systems, allowing uninterrupted operation even during charging.
Charging from power lines requires precise drone positioning near the lines, as excessive proximity to the lines could result in electrical discharges or component damage. AI controls this process by processing data from GPS, cameras, LIDAR, and inertial measurement units (IMUs), enabling the drone to accurately determine its position and maintain stable hovering near the power lines. Furthermore, computer vision systems provide automatic identification of power lines, which is critical for the safety and stability of the charging process. Such precise positioning is essential not only for optimal energy collection but also for drones equipped with specialized gripping mechanisms for charging. When physically attached to the power lines, the drone employs insulated clamps, which necessitate advanced insulation systems to protect against high-voltage discharges.
As electromagnetic fields around power lines can fluctuate due to external factors such as changes in grid load or weather conditions, AI oversees the dynamic adjustment of the energy harvesting process. By processing electromagnetic field parameters in real time, AI can alter the energy collection rate or reposition the drone to a more optimal location to maintain a stable charging cycle. If the AI detects a voltage drop or an increase in the field, it can adapt charging parameters to prevent component damage or overheating, which could cause system failure. AI monitors the temperature of critical elements, such as inductive coils, voltage converters, and the battery, activating cooling systems as necessary to reduce the risk of overheating. If the temperature reaches a critical threshold, the AI halts the charging process until temperatures normalize, safeguarding the drone’s operational capability.
Batteries must not only have high energy density to support extended flights but also be compatible with high-voltage energy harvesting. Lithium-ion (Li-Ion) and lithium-polymer (Li-Po) batteries are suitable for such drones due to their high energy density and fast charging capabilities. Li-Ion batteries provide stable power output, making them ideal for drones conducting long-term surveillance or reconnaissance missions. They can maintain consistent output over extended periods but are sensitive to overcharging and overheating. To prevent damage, a battery management system (BMS) controlled by AI regulates the charging process and continuously monitors parameters such as voltage, current, and temperature. Li-Po batteries are more flexible in design and can be manufactured in various shapes and sizes, which is advantageous for compact drones. They can endure high discharge currents, making them suitable for drones requiring high energy bursts during maneuvers or combat operations. However, they are also prone to thermal runaway during charging. To mitigate this, AI regulates battery temperature during charging, reducing current or activating cooling systems as needed.
Forecasts
Future developments in this field are likely to focus on further improving system reliability and expanding its capabilities. Among the promising directions are enhancing the resilience of the gripper and energy harvesting system to adverse weather conditions, such as heavy rain or extreme temperatures. Researchers are also exploring the possibility of integrating additional features, such as high-resolution visualization and spatial sensing, to broaden the scope of potential applications. Additionally, advancements in battery technology and energy management algorithms could further enhance the system’s efficiency and performance.