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Wind turbines need annual inspections to investigate their states which may have damages, such as cracks, erosion, bonding defects, cavities and delamination. Wind blades inspection, however, is a difficult process which needs specialized equipment and well-trained technicians to perform it manually. In addition, most approaches to inspect require pre-installed infrastructures like ropes or other platforms, so they are not appropriate for a close investigation and has a low preference. To overcome these problems, the need for a wall-climbing robot has emerged. In this paper, we suggest a MAV (Micro Aerial Vehicle) type wall-climbing robot that has four rotors to make thrust force for tlying and four wheels for wall-climbing so that it can tly, stick, and move on a vertical and non-tlat surface. The overall inspection process has two parts; macro and micro inspections. The main concept was verified throughout simulations.

Visually servoed paired structured light system (ViSP) has been found to be useful in estimating 6-DOF relative displacement. The system is composed of two screens facing each other, each with one or two lasers, a 2-DOF manipulator and a camera. The displacement between two sides is estimated by observing positions of the projected laser beams and rotation angles of the manipulators. To apply the system to massive structures, the whole area should be partitioned and each ViSP module is placed in each partition in a cascaded manner. The estimated displacement between adjoining ViSPs is combined with the next partition so that the entire movement of the structure can be estimated. The multiple ViSPs, however, have a major problem that the error is propagated through the partitions. Therefore, a displacement estimation error back-propagation (DEEP) method which uses Newton–Raphson or gradient descent formulation inspired by the error back-propagation algorithm is proposed. In this method, the estimated displacement from the ViSP is updated using the error back-propagated from a fixed position. To validate the performance of the proposed method, various simulations and experiments have been performed. The results show that the proposed method significantly reduces the propagation error throughout the multiple modules.

Summary In previous studies, a visually servoed paired structured light system (ViSP) was proposed to estimate structural displacement with high accuracy at low cost. Although the performance of the system has been verified from various simulations and experimental tests, it has a limitation that high‐speed dynamic displacement cannot be measured properly due to the relatively low sampling rate. To speed up ViSP, in this paper, an inertial measurement unit (IMU) with a high sampling rate is concurrently used with ViSP in a Kalman filtering framework. By combining measured accelerations and angular velocities from the IMU with the estimated 6‐DOF displacement from ViSP, high‐speed structural movement can be monitored with high accuracy. In the proposed data fusion method, named ViSP‐IMU, random biases of the IMU are calibrated by using the estimated displacement results from ViSP‐IMU. We perform experimental tests and verify the efficacy of the proposed algorithm as a promising structural displacement estimation method which guarantees high accuracy and a high sampling rate. Also, the proposed bias compensation method can effectively reduce the displacement estimation error by minimizing the difference between estimated displacements from the IMU and ViSP‐IMU. Copyright © 2016 John Wiley & Sons, Ltd.

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