Maintenance of power transmission lines, specifically the replacement of Stockbridge vibration dampers, is traditionally performed by skilled linemen due to the significant weights and fastening torques involved. While existing robotic solutions can perform these tasks, they rely on heavy infrastructure for deployment, adding significant logistical overhead. We present the first aerial robotic system capable of self-transporting to the conductor to perform end-to-end damper replacement. Our system features a specialized 5-DOF Cartesian manipulator integrated into a lightweight, wire-tensioned H-frame that minimizes flexibility under load. A key advantage of the proposed design is its versatility: without mechanical modification, the system can uninstall, retrieve, and install dampers of many sizes. The system has been validated in a real-world power line environment, and experimental results demonstrate that an operator can uninstall and install dampers ranging from 2 to 9.5 kg with an average precision of ±1.5 mm while overcoming breakaway torques exceeding 200 Nm.

Detecting and estimating distances to power lines is a challenge for both human UAV pilots and autonomous systems, which increases the risk of unintended collisions. We present a mmWave radar–based perception system that provides spherical sensing coverage around a small UAV for robust power line detection and avoidance. The system integrates multiple compact solid-state mmWave radar modules to synthesize an omnidirectional field of view while remaining lightweight. We characterize the sensing behavior of this omnidirectional radar arrangement in power line environments and develop a robust detection-and-avoidance algorithm tailored to that behavior. Field experiments on real power lines demonstrate reliable detection at ranges up to 10 m, successful avoidance maneuvers at flight speeds upwards of 10 m/s, and detection of wires as thin as 1.2 mm in diameter. These results indicate the approach’s suitability as an additional safety layer for both autonomous and manual UAV flight.

Autonomous drone flight within powerline corridors requires reliable onboard perception that can estimate and track the poses of conductors, even when individual cables temporarily leave the sensors' field of view. In this work, we present an onboard perception system for mid-span corridor powerline pose estimation and tracking, building on previous research that combines mmWave radar and RGB camera measurements with flight controller odometry. First, we introduce a transformation of the cable direction that enables consistent estimation of the global powerline orientation regardless of the drone's attitude by compensating for the mismatch between the camera and sensor planes. Second, we propose the Relative Cable Positions algorithm, which exploits the fixed geometric relationships between conductors to estimate the positions of cables outside the mmWave radar field of view based on measurements from cables that remain visible. The system is implemented onboard a drone and evaluated through real-world flight experiments conducted at a dedicated powerline test facility. The results show a clear reduction in position estimation error for conductors outside the radar field of view compared to odometry-only tracking under non-RTK conditions. Overall, the proposed methods improve the robustness of drone-based tracking and pose estimation of conductor geometry without relying on RTK, supporting safer and more reliable autonomous flight within mid-span powerline corridors.

This paper presents the development and experimental evaluation of an aerial robotic propulsion system designed for armor rod installation on overhead power transmission lines. Armor rods are metal helices that are twisted onto power lines to protect them from bending or abrasion, or to repair them to restore mechanical strength and conductivity. The proposed robot employs a compact bi-modal coaxial multi-rotor propulsion system that serves a dual purpose: enabling aerial self-transportation to the power line and generating the torque required for twisting armor rods onto the conductor. Prior to system integration, the torque required for armor rod twisting was experimentally characterized through manual measurements to inform propulsion system selection and configuration. The mechanical design ensures unobstructed rotation during twisting through a compact and symmetric rotor arrangement. Experimental validation demonstrates that the propulsion system alone can generate sufficient torque and rotational motion to achieve effective armor rod installation in elevated and difficult-to-access transmission environments.

Drones have recently become a widely used alternative to helicopters and cranes for high-voltage transmission line inspection and maintenance, offering improved safety and reduced costs. In order to operate on an energized high-voltage line, drones must be protected from electric sparks when making contact with the line. However, certain drone components remain susceptible to EMI noise even when protected by metal shields. This paper introduces a lab setup to measure spark noise from a 100 kV-AC line on these components. A mitigation solution was proposed to reduce the spark currents to a level where the shield is unnecessary. Flight tests were conducted in the lab under various voltage conditions (up to 350 kV AC with a tubular conductor and 500 kV AC at the transformer terminal) to demonstrate the applicability of the proposed solution and to evaluate the performance of drones with different sizes and shielding methods.

This paper proposes a method to robustify model predictive path integral (MPPI) control by directly taking into account the effects of parameter uncertainty into the controller formulation. Leveraging the recent notion of closed-loop state sensitivity, the proposed MPPI can consider the state sensitivity against parameter mismatch as a part of the system state, and consequently exploit this additional information to address the challenge of model mismatch in sampling-based model predictive control. Using an obstacle avoidance scenario, we demonstrate the use of our approach to control an aerial robot. We present an embedded implementation of our method, utilizing parallelization of computations on a GPU. Finally, we show the increased robustness of our approach over a standard MPPI controller through hardware-in-the-loop simulations and validate its embedded real-time properties.

This paper shows how different types of novel aerial robots with new functionalities can cooperate in the inspection and maintenance (I&M) of power lines, one of the largest and most essential civil infrastructures in any country. This study relies on the results from the AERIAL-CORE research and innovation project. The paper describes an I&M validation scenario and evaluation metrics for three linked operation domains: 1) long-range inspection for the detection of possible damages on power lines in a post-storm scenario, 2) aerial manipulation for the installation of devices on power lines, and 3) aerial co-working to help human operators in their activities at height. It presents the demonstration of ten different aerial robots in a real scenario with 10 km of power lines. The platforms include morphing-wing and VTOL (vertical take-off and landing) UAVs (unmanned aerial vehicles), multi-rotors, and aerial manipulators. These platforms, custom-developed or commercially available, are evaluated in the three application domains, describing the new functionalities implemented for each case. The paper ends with guidelines, design principles, and lessons learned for future developments derived from the final demonstration of the project.

The use of drones for inspecting and maintaining high-voltage transmission lines has become increasingly popular, replacing traditional methods like helicopters and cranes. However, the potential difference between the drone and the high-voltage cable results in the occurrence of electric sparks striking the drone during its interaction with the power cable to perform tasks. This paper models the arcing phenomenon to predict spark current levels and repetition rates at any power line voltages and distances. Lab experiments at voltages up to 100 kV AC were conducted to validate the model, showing an accuracy range of 3.6% to 23% when considering the peak currents. The results were used to evaluate the interference to a carbon fiber drone in simulation and minimal shield was proposed to reduce the noise. All the proposed models are made available as open-source. Additionally, equivalent lab tests using at least two times lower voltages were introduced to replicate the arcing conditions of real overhead power lines.

Using drones to inspect overhead transmission lines has progressively gained popularity due to safety reasons, ease of deployment, and reasonable costs compared to the traditional approach of using helicopters. However, the short flying time is the challenging problem that deters drones from being used in long-range inspection missions. Recharging the battery from the magnetic field around the cable is a promising solution that enables drones to operate automatically and endlessly without human intervention. In this article, a magnetic gripper was proposed to take advantage of the magnetic field from the line to hold the drone on the power line and recharge the battery at the same time. A magnetic manipulating circuit with different operating modes was developed to maintain the grip regardless of the power line's current level. The system was tested in the lab and on a drone with an energized transmission line.

We present a fully autonomous self-recharging drone system capable of long-duration sustained operations near powerlines. The drone is equipped with a robust onboard perception and navigation system that enables it to locate powerlines and approach them for landing. A passively actuated gripping mechanism grasps the powerline cable during landing after which a control circuit regulates the magnetic field inside a split-core current transformer to provide sufficient holding force as well as battery recharging. The system is evaluated in an active outdoor three-phase powerline environment. We demonstrate multiple contiguous hours of fully autonomous uninterrupted drone operations composed of several cycles of flying, landing, recharging, and takeoff, validating the capability of extended, essentially unlimited, operational endurance.

Large-scale infrastructures are prone to deterioration due to age, environmental influences, and heavy usage. Ensuring their safety through regular inspections and maintenance is crucial to prevent incidents that can significantly affect public safety and the environment. This is especially pertinent in the context of electrical power networks, which, while essential for energy provision, can also be sources of forest fires. Intelligent drones have the potential to revolutionize inspection and maintenance, eliminating the risks for human operators, increasing productivity, reducing inspection time, and improving data collection quality. However, most of the current methods and technologies in aerial robotics have been trialed primarily in indoor testbeds or outdoor settings under strictly controlled conditions, always within the line of sight of human operators. Additionally, these methods and technologies have typically been evaluated in isolation, lacking comprehensive integration. This paper introduces the first autonomous system that combines various innovative aerial robots. This system is designed for extended-range inspections beyond the visual line of sight, features aerial manipulators for maintenance tasks, and includes support mechanisms for human operators working at elevated heights. The paper further discusses the successful validation of this system on numerous electrical power lines, with aerial robots executing flights over 10 kilometers away from their ground control stations.

This work proposes a novel drone system designed to autonomously track and follow power lines and reconstruct them in 3D with a point cloud representation based on mmWave radar measurements. The system is composed of a GNSS-enabled quadrotor UAV equipped with a combined mmWave radar sensor and onboard compute module payload and has been designed to be small, lightweight, and low-cost. MmWave radar sensors offer great range and sensitivity in the task of power line detection with a high level of sparsity in the produced data when compared to traditional sensors such as LiDARs. The proposed system overcomes the radar sensor's shortcomings by building up a point cloud representing the power line environment as the drone moves around in it. The built-up point cloud is analyzed using the onboard computer to detect the cables in the power line environment and to produce pose-estimates of each line. The system has been tested in a variety of scenarios and has been shown to be able to accurately detect and track power lines in varying weather conditions. A video demonstration of the system can be viewed here: https://www.youtube.com/watch?v=MORFX3CFygk.

Autonomous robots require the ability to perceive their environment. This must be done in a power-efficient manner to allow them to operate for an extended duration of time. Convolutional neural networks (CNN) are typically used to process image data but they require large amounts of processing power to deploy. CNNs can be efficiently implemented on an FPGA achieving low power consumption. In this work, we present a framework for implementing CNNs on an MPSoC that can be used in robotics applications. A method for automatic image labelling is used to create a dataset for training the neural network. The model is trained using TensorFlow and the weights are automatically exported and programmed onto the FPGA. An example application is developed to showcase the proposed framework. The application achieves a 428% increase in performance and a 432% increase in power efficiency when using hardware acceleration compared to running the application on a CPU.

Recent research has pushed the applications of UAVs into domains such as infrastructure inspection and interaction. For UAVs to be able to safely and efficiently perform autonomous operations near the target infrastructure, they need to be aware of their surroundings while exposing navigation API to the application software. For powerline inspection UAVs, this yields a requirement for knowledge of the powerline cable positions and a set of actions facilitating specific flight operations in this environment. This work presents a hardware/software system solving these requirements. A framework is shown which allows application software to autonomously fly the UAV to any of the perceived cables, to fly the UAV along a cable, and to land on and takeoff from a cable. The system relies on an abstract representation of the identified and tracked cables, while solving the flight maneuvers using an MPC based trajectory planning routine. The system is tested in a real powerline environment featuring four cables stretched between two pylons. A GUI application is developed for triggering the actions remotely from a ground control station while providing a visual representation of the perceived cables and planned trajectories.

Recently, due to the advantages of drones, many electrical companies have employed drones for inspecting overhead transmission lines instead of helicopters. However, the limited operating time is still the big issue preventing drones from being used for monitoring on a large scale. The magnetic field around cables is a potential power source enabling drones to recharge when inspecting high-voltage power lines, eliminating the need to return to the home base for charging. In this paper, the Transfer Window Alignment method was combined with Perturb and Observe algorithm and Silicon Steel core to enhance the harvested power. The experiment showed that extra 58.6% power could be harvested compared to the traditional approach. Regardless of the current fluctuation on the power lines, the proposed method also allows the charging circuit to automatically locate the maximum power point without sensing the primary current.

Drone grasping on power lines for recharging is challenging since it requires the gripper to be lightweight, carried by a drone, and efficient for a firm grasp. A deep understanding of the power line nature and its magnetic characteristic helps ease such challenges and bring new knowledge to gripper design. In this work, a novel adaptive, lightweight, and fail-safe magnetic gripper with a recharging feature is presented. The gripper exploits the radiated magnetic field of the lines for charging and holding the drone and can easily detach from the line. The gripper design has been validated in the lab and on a quadcopter with a real power line.

Inspection of critical infrastructure with drones is experiencing an increasing uptake in the industry driven by a demand for reduced cost, time, and risk for inspectors. Early deployments of drone inspection services involve manual drone operations with a pilot and do not obtain the technological benefits concerning autonomy, coordination, and cooperation. In this paper, we study the design needed to handle the complexity of an Unmanned Aerial System (UAS) to support autonomous inspection of safety-critical infrastructure. We apply a constructive research approach to link innovation needs with concepts, designs, and validations that include simulation and demonstration of key design parts. Our design approach addresses the complexity of the UAS and provides a selection of technology components for drone and ground control hardware and software including algorithms for autonomous operation and interaction with cloud services. The paper presents a drone perception system with accelerated onboard computing, communication technologies of the UAS, as well as algorithms for swarm membership, formation flying, object detection, and fault detection with artificial intelligence. We find that the design of a cooperative drone swarm and its integration into a custom-built UAS for infrastructure inspection is highly feasible given the current state of the art in electronic components, software, and communication technology.

Autonomously recharging UAVs from existing infrastructure has enormous potential for various applications, such as infrastructure inspection, surveillance, and search and rescue. While it is an active area of research, most related work focuses on alternating current (AC) infrastructure while very little work has been done on investigating the potential of recharging UAVs from direct current (DC) infrastructure. This work proposes a UAV system designed to autonomously recharge from existing DC infrastructure. Two onboard powerline grippers and a motorized cable drum enable the UAV to perform a two-stage landing on railway DC lines where a wire is connected between them through the UAV for recharging. Light-weight electronics designed to be carried by the UAV are developed to harvest energy from up to 3kV DC railway lines. The recharge mission is autonomously executed using fully onboard and real-time perception and trajectory planning and tracking algorithms. The potential of the system is shown in lab setting validation, with hardware-in-the-loop simulation, and partly in a real overhead powerline environment, verifying the functionality of the sub-components.

Non-functional properties, such as energy, time, and security (ETS) are becoming increasingly important in Cyber-Physical Systems (CPS) programming. This article describes TeamPlay, a research project funded under the EU Horizon 2020 programme between January 2018 and June 2021. TeamPlay aimed to provide the system designer with a toolchain for developing embedded applications where ETS properties are first-class citizens, allowing the developer to reflect directly on energy, time and security properties at the source code level. In this paper we give an overview of the TeamPlay methodology, introduce the challenges and solutions of our approach and summarise the results achieved. Overall, applying our TeamPlay methodology led to an improvement of up to 18% performance and 52% energy usage over traditional approaches.