Mechanism Design of Passive Obstacle-Crossing Transmission Line Robot for Insulation Net Installation

High-voltage transmission line maintenance, particularly insulation net installation (commonly termed “sealing operations”), faces critical challenges including electrocution risks, harsh environments, and low manual efficiency. This study proposes a lightweight, highly adaptive robotic platform to address these issues. The platform integrates a locomotion module, obstacle-crossing mechanism, autonomous docking system, sensor network, and UAV-assisted deployment, enabling stable operation on 35–220 kV transmission lines (cable diameter: 10–30 mm). Key innovations include a passive obstacle-crossing mechanism utilizing three symmetrically distributed adaptive vanes, which mechanically adapt to cross spacers and other obstacles without active control, ensuring continuous wire contact to prevent falls. The locomotion module employs large-diameter driven wheels (34–42 mm radius) with high-friction coatings, achieving a climbing capacity of 10–15° slopes and a travel speed exceeding 20 m/min. A dual-motor drive system (rated torque: 0.4 N·m) enhances traction while minimizing energy consumption. For tool interaction, the 2-DOF robotic arm architecture guided by depth cameras ensures rapid tool attachment/detachment (<10 s), supported by a bidirectional screw-driven locking mechanism. UAV collaboration simplifies aerial deployment, reducing human intervention during ascent/descent. Kinematic and dynamic analyses validate the design: the robot’s drag capacity reaches 58.8 N (friction coefficient: 0.4) under a 15 kg payload, sufficient for towing standard insulation nets. Structural optimization achieves a 27% mass reduction compared to conventional models while maintaining rigidity. Field tests confirm adaptability to diverse line configurations (70–630 mm2 cross-sections) and environmental conditions. This work advances robotic automation in power grid maintenance, offering a safer, cost-effective alternative to manual operations with 40% efficiency improvement in repetitive tasks. Future research will focus on AI-enhanced decision-making for multi-robot coordination in complex grid networks.