Motion Planning for Vibration Reduction of a Railway Bridge Maintenance Robot with a Redundant Manipulator

Motivated by the potential applications of maintenance and inspection tasks for railway bridges, we have developed a biped climbing robot. The biped climbing robot can climb on the steel guardrail of the railway bridge with two electromagnetic feet and implement the maintenance and inspection tasks by a redundant manipulator with 7 degrees of freedom. To reduce the vibration of the manipulator caused by the low rigidity of the guardrail and the discontinuous trajectories of joints, a motion planning algorithm for vibration reduction is proposed in this paper. A geometric path accounting for obstacle avoidance and the manipulator’s center of gravity is determined by the gradient projection method with a singularity-robust inverse. Then, a piecewise quintic polynomial S shape curve with a smooth jerk (derivative of joint angular acceleration) profile is used to interpolate the sequence of joint angular position knots that are transformed from the via-points in the obstacle-avoidance path. The parameters of the quintic polynomial S-curve are determined by a nonlinear programming problem in which the objective function is to minimize the maximus of the torque exerted by the manipulator on the guardrail throughout the jerk-continuous trajectory. Finally, a series of simulation experiments are conducted to validate the effectiveness of the proposed algorithm. The simulation results show that the tracking errors of the trajectory with the proposed optimization algorithm are significantly smaller than the tracking errors of the trajectory without optimization. The absolute values of mean deviation of the tracking errors of the three coordinate axes decreased by at least 48.3% compared to the trajectory without vibration-reduction in the triangle working path and linear working path trajectory following simulations. The analysis results prove that the proposed algorithm can effectively reduce the vibration of the end effector of the manipulator.

AMCD: an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection

Corrosion has been concerned as a serious safety issue for metallic facilities. Visual inspection carried out by an engineer is expensive, subjective and time-consuming. Micro Aerial Vehicles (MAVs) equipped with detection algorithms have the potential to perform safer and much more efficient visual inspection tasks than engineers. Towards corrosion detection algorithms, convolution neural networks (CNNs) have enabled the power for high accuracy metallic corrosion detection. However, these detectors are restricted by MAVs on-board capabilities. In this study, based on You Only Look Once v3-tiny (Yolov3-tiny), an accurate deep learning-based metallic corrosion detector (AMCD) is proposed for MAVs on-board metallic corrosion detection. Specifically, a backbone with depthwise separable convolution (DSConv) layers is designed to realise efficient corrosion detection. The convolutional block attention module (CBAM), three-scale object detection and focal loss are incorporated to improve the detection accuracy. Moreover, the spatial pyramid pooling (SPP) module is improved to fuse local features for further improvement of detection accuracy. A field inspection image dataset labelled with four types of corrosions (the nubby corrosion, bar corrosion, exfoliation and fastener corrosion) is utilised for training and testing the AMCD. Test results show that the AMCD achieves 84.96% mean average precision (mAP), which outperforms other state-of-the-art detectors. Meanwhile, 20.18 frames per second (FPS) is achieved leveraging NVIDIA Jetson TX2, the most popular MAVs on-board computer, and the model size is only 6.1 MB.

InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection

Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision is limited by the scale of the dataset and parameters. Recently, the Generative Adversarial Network (GAN) was found to offer excellent image generation. Therefore, we propose a novel model called InsulatorGAN based on using conditional GANs to detect insulators in transmission lines. However, due to the fixed categories in datasets such as ImageNet and Pascal VOC, the generated insulator images are of a low resolution and are not sufficiently realistic. To solve these problems, we established an insulator dataset called InsuGenSet for model training. InsulatorGAN can generate high-resolution, realistic-looking insulator-detection images that can be used for data expansion. Moreover, InsulatorGAN can be easily adapted to other power equipment inspection tasks and scenarios using one generator and multiple discriminators. To give the generated images richer details, we also introduced a penalty mechanism based on a Monte Carlo search in InsulatorGAN. In addition, we proposed a multi-scale discriminator structure based on a multi-task learning mechanism to improve the quality of the generated images. Finally, experiments on the InsuGenSet and CPLID datasets demonstrated that our model outperforms existing state-of-the-art models by advancing both the resolution and quality of the generated images as well as the position of the detection box in the images.

Comparison of Display Modality and Human-in-the-Loop Presence for On-Orbit Inspection of Spacecraft

Objective To investigate the impact of interface display modalities and human-in-the-loop presence on the awareness, workload, performance, and user strategies of humans interacting with teleoperated robotic systems while conducting inspection tasks onboard spacecraft. Background Due to recent advancements in robotic technology, free-flying teleoperated robot inspectors are a viable alternative to extravehicular activity inspection operations. Teleoperation depends on the user’s situation awareness; consequently, a key to successful operations is practical bi-directional communication between human and robot agents. Method Participants ( n = 19) performed telerobotic inspection of a virtual spacecraft during two degrees of temporal communication, a Synchronous Inspection task and an Asynchronous Inspection task. Participants executed the two tasks while using three distinct visual displays (2D, 3D, AR) and accompanying control systems. Results Anomaly detection performance was better during Synchronous Inspection than the Asynchronous Inspection of previously captured imagery. Users’ detection accuracy reduced when given interactive exocentric 3D viewpoints to accompany the egocentric robot view. The results provide evidence that 3D projections, either demonstrated on a 2D interface or augmented reality hologram, do not affect the mean clearance violation time (local guidance performance), even though the subjects perceived a benefit. Conclusion In the current implementation, the addition of augmented reality to a classical egocentric robot view for exterior inspection of spacecraft is unnecessary, as its margin of performance enhancement is limited in comparison. Application Results are presented to inform future human–robot interfaces to support crew autonomy for deep space missions.

Analysis of Mechanical Adhesion Climbing Robot Design for Wind Tower Inspection

Mmaintenance of wind turbine farms is a huge task, with associated significant risks and potential hazard to the safety and wellbeing of people who are responsible for carrying the tower inspection tasks. Periodic inspections are required for wind turbine tower to ensure that the wind turbines are in full working order, with no signs of potential failure. Therefore, the development of an automated wind tower inspection system has been very crucial for the overall performance of the renewable wind power generation industry. In order to determine the life span of the tower, an investigation of robot design is discussed in this paper. It presents how a mechanical spring-loaded climbing robot can be designed and constructed to climb and rotate 360° around the tower. An adjustable circular shape robot is designed that allows the device to fit in different diameters of the wind generator tower. The rotational module is designed to allow the wheels to rotate and be able to go in a circular motion. The design further incorporates a suspension that allows the robot to go through any obstacle. This paper also presents afiniteelement spring stress analysis and Simulink control system model to find the optimal parameters that are required for the wind tower climbing robot.

Localization System for Lightweight Unmanned Aerial Vehicles in Inspection Tasks

This paper presents a localization system for Unmanned Aerial Vehicles (UAVs) especially designed to be used in infrastructure inspection, where the UAVs have to fly in challenging conditions, such as relatively high altitude (e.g., 15 m), eventually with poor or absent GNSS (Global Navigation Satellite System) signal reception, or the need for a BVLOS (Beyond Visual Line of Sight) operation in some periods. In addition, these infrastructure inspection applications impose the following requirements for the localization system: defect traceability, accuracy, reliability, and fault tolerance. Our system proposes a lightweight solution combining multiple stereo cameras with a robotic total station to comply with these requirements, providing full-state estimation (i.e., position, orientation, and linear and angular velocities) in a fixed and time-persistent reference frame. Moreover, the system can align and fuse all sensor measurements in real-time at high frequency. We have integrated this localization system in our aerial platform, and we have tested its performance for inspection in a real-world viaduct scenario, where the UAV has to operate with poor or absent GNSS signal at high altitude.