A Novel Autonomous Landing Method for Flying–Walking Power Line Inspection Robots Based on Prior Structure Data

Abstract

Hybrid inspection robots have been attracting increasing interest in recent years, and are suitable for inspecting long-distance overhead power transmission lines (OPTLs), combining the advantages of flying robots (e.g., UAVs) and climbing robots (e.g., multiple-arm robots). Due to the complex work conditions (e.g., power line slopes, complex backgrounds, wind interference), landing on OPTL is one of the most difficult challenges faced by hybrid inspection robots. To address this problem, this study proposes a novel autonomous landing method for a developed flying–walking power line inspection robot (FPLIR) based on prior structure data. The proposed method includes three main steps: (1) A color image of the target power line is segmented using a real-time semantic segmentation network, fusing the depth image to estimate the position of the power line. (2) The safe landing area (SLA) is determined using prior structure data, applying the trajectory planning method with geometric constraints to generate the dynamic landing trajectory. (3) The landing trajectory is tracked using real-time model predictive control (MPC), controlling FPLIR to land on the OPTL. The feasibility of the proposed method was verified in the ROS Gazebo environment. The RMSE values of the position along three axes were 0.1205,0.0976 and 0.0953, respectively, while the RMSE values of the velocity along these axes were 0.0426, 0.0345 and 0.0781. Additionally, experiments in a real environment using FPLIR were performed to verify the validity of the proposed method. The experimental results showed that the errors of position and velocity for the FPLIR landing on the lines were 6.18×10−2 m and 2.16×10−2 m/s. The simulation results as well as the experimental findings both satisfy the practical requirements. The proposed method provides a foundation for the intelligent inspection of OPTL in the future.

Keywords

• FPLIR
• autonomous landing
• prior structure data
• model predictive control
• trajectory planning