Due to the variation of sunlight conditions resulting in uneven lightness in images, the details of target objects tend to hide in the dark or bright regions, which is adverse to following image processing. To reliably land on the power line under the change of lightness, the flying-walking power line inspection robot (FPLIR) needs reliable detection for the power line. In this paper, a machine vision-based detection method of power line is proposed to adapt different lightness. Firstly, a visual system of the FPLIR is designed to collect and process power line images. Secondly, the multi-scale retinex (MSR) algorithm is used to reduce the influence of lightness. Then, the local binary pattern (LBP) map of power line image is generated by the LBP operator and is divided into many blocks. An LBP histogram vector is calculated for every block, then the first-order entropy and second-order entropy of every histogram vector are calculated. Finally, the first-order entropy, the second-order entropy, and the edge density of power line image are used as the feature vector of fuzzy c-means (FCM) to obtain the power line region. The experimental result shows that the accuracy of the proposed method is 82.6%, which is 9.3% more than the method without image enhancement. Thus, the proposed method can effectively detect power line, improving the robustness and accuracy of power line detection (PLD) during the FPLIR landing.
Power line inspection robot can replace manual inspection, reduce the burden of manpower, and collect more information about the state of power line. When power line inspection robot walks along the power line, it is prone to slipping of driving wheel, which causes some problems, e.g., inaccurate position recognition, unstable image acquisition. To solve the slipping problem on power line, a walking mechanism of a flying-walking power line inspection robot (FPLIR) is designed in this paper, working principle of the walking mechanism is elaborated, and its mathematical model is established. To determine the walking state of the robot on the power line, a slipping identification method is proposed by defining a slipping degree to evaluate the slipping state. Considering that the factors causing slipping have a high degree of uncertainty and cannot be described by a very precise model, we design an adaptive neural fuzzy controller using ANFIS for the walking mechanism. A rigid-flexible coupling model is established using ADAMS software to simulate and verify the walking process of the FPLIR along the power line, and effect of the controller is tested based on the evaluation of the slipping degree. These simulation results show that the proposed adaptive neural fuzzy controller can effectively restrain slipping and improve the stability of the FPLIR inspection. The walking efficiency of the FPLIR is increased 35% using the proposed controller in the simulation. This study provides important reference of direction and technology for the subsequent slipping control research of inspection robot.
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