Abstract: Power line inspection is a critical task that requires regular monitoring and maintenance to ensure the reliability and safety of electrical distribution infrastructure. With the advancements in robotics, artificial intelligence (AI), and unmanned aerial vehicles (UAVs), integrating robotic manipulators with drones and automating their maneuvers has emerged as a promising solution for power line inspection. This paper proposes a quadcopter design and implementation with a gripper mechanism to dock automatically on a power line using AI-enabled camera feedback. The machine learning model implemented onboard will detect the power line, align the drone to it, and activate the gripper for automated perching. The drone also includes a light weight three degree of freedom (DoF) robotic manipulator with an additional camera incorporated into it for AI-assisted power line inspection. The insulator fault detection can be carried out with a deep learning model. Power line inspection begins with the take-off of the drone from the ground and its perch on the power line. After disarming the drone, the manipulator comes into action. The arm is lifted through a controlled manipulator action to focus the camera on the insulators. The video of the insulators will be shared with a server through wireless means. A custom-trained deep-learning model in the server will identify the faulty insulators.
Keywords: Power line inspection, UAVs, manipulator, degree of freedom, payload capability, deep learning.
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DOI:
10.17148/IJARCCE.2025.14390
