Structural health assessments are essential for infrastructure. By using an autonomous panorama vision‐based inspection system, the limitations of the human cost and safety factors of previously time‐consuming tasks have been overcome. The main damage detection challenges to panorama images are (1) the lack of annotated panorama defect image data, (2) detection in high‐resolution images, and (3) the inherent distortion disturbance for panorama images. In this paper, a new PAnoramic surface damage DEtection Network (PADENet) is presented to solve the challenges by (a) using an unmanned aerial vehicle to capture panoramic images and a distorted panoramic augmentation method to expand the panoramic dataset, (b) employing the proposed multiple projection methods to process high‐resolution images, and (c) modifying the faster region‐based convolutional neural network and training via transfer learning on VGG‐16, which improves the precision for detecting multiple types of damage in distortion. The results show that the proposed method is optimal for surface damage detection.

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With the increasing demands for energy, oil and gas companies have a demand to improve their efficiency, productivity and safety. Any potential corrosions and cracks on their production, storage or transportation facilities could cause disasters to both human society and the natural environment. Since many oil and gas assets are located in the extreme environment, there is an ongoing demand for robots to perform inspection tasks, which will be more cost-effective and safer. This paper provides a state of art review of inspection robots used in the oil and gas industry which including remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). Different kinds of inspection robots are designed for inspecting different asset structures. The outcome of the review suggests that the reliable autonomous inspection UAVs and AUVs will gain interest among these robots and reliable autonomous localisation, environment mapping, intelligent control strategies, path planning and Non-Destructive Testing (NDT) technology will be the primary areas of research.

This paper presents the design, implementation, and testing of a soft landing gear together with a neural network-based control method for replicating avian landing behavior on non-flat surfaces. With full consideration of unmanned aerial vehicles and landing gear requirements, a quadrotor helicopter, comprised of one flying unit and one landing assistance unit, is employed. Considering the touchdown speed and posture, a novel design of a soft mechanism for non-flat surfaces is proposed, in order to absorb the remaining landing impact. The framework of the control strategy is designed based on a derived dynamic model. A neural network-based backstepping controller is applied to achieve the desired trajectory. The simulation and outdoor testing results attest to the effectiveness and reliability of the proposed control method.

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Vision-based techniques are widely used in micro aerial vehicle autonomous landing systems. Existing vision-based autonomous landing schemes tend to detect specific landing landmarks by identifying their straightforward visual features such as shapes and colors. Though efficient to compute, these schemes only apply to landmarks with limited variability and require strict environmental conditions such as consistent lighting. To overcome these limitations, we propose an end-to-end landmark detection system based on a deep convolutional neural network, which not only easily scales up to a larger number of various landmarks but also exhibit robustness to different lighting conditions. Furthermore, we propose a separative implementation strategy which conducts convolutional neural network training and detection on different hardware platforms separately, i.e. a graphics processing unit work station and a micro aerial vehicle on-board system, subject to their specific implementation requirements. To evaluate the performance of our framework, we test it on synthesized scenarios and real-world videos captured by a quadrotor on-board camera. Experimental results validate that the proposed vision-based autonomous landing system is robust to landmark variability in different backgrounds and lighting situations.

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