In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition

Defects presented on the facades of a building do have profound impacts on extending the life cycle of the building. How to identify the defects is a crucial issue; destructive and non-destructive methods are usually employed to identify the defects presented on a building. Destructive methods always cause the permanent damages for the examined objects; on the other hand, non-destructive testing (NDT) methods have been widely applied to detect those defects presented on exterior layers of a building. However, NDT methods cannot provide efficient and reliable information for identifying the defects because of the huge examination areas. Infrared thermography is often applied to quantitative energy performance measurements for building envelopes. Defects on the exterior layer of buildings may be caused by several factors: ventilation losses, conduction losses, thermal bridging, defective services, moisture condensation, moisture ingress, and structure defects. Analyzing the collected thermal images can be quite difficult when the spatial variations of surface temperature are small. In this paper the authors employ image segmentation to cluster those pixels with similar surface temperatures such that the processed thermal images can be composed of limited groups. The surface temperature distribution in each segmented group is homogenous. In doing so, the regional boundaries of the segmented regions can be identified and extracted. A terrestrial laser scanner (TLS) is widely used to collect the point clouds of a building, and those point clouds are applied to reconstruct the 3D model of the building. A mapping model is constructed such that the segmented thermal images can be projected onto the 2D image of the specified 3D building. In this paper, the administrative building in Chaoyang University campus is used as an example. The experimental results not only provide the defect information but also offer their corresponding spatial locations in the 3D model.

Combining the 3D model generated from point clouds and thermography to identify the defects presented on the facades of a building

Severe weathers and climate changes do have profound impacts on the exterior layer of structures. Especially for those defects presented on the exterior layers of buildings, those defects can make the exterior layers of buildings worse, and, sometimes, the life cycles of buildings are decreased. How to identify those defects shown on the exterior layers of structures is an important issue for the sustainable structure management. Non-destructive testing (NDT) methods have been widely applied to detect those defects presented on the A terrestrial layers. However, NDT methods cannot provide efficient and reliable information for identifying the defects because of the huge examination areas. Thermography is a product of a thermal infrared camera. Thermography is used to record the surface temperature information, and the differences of the recorded surface temperatures are small such that it was difficult to analyze the collected thermographs. The defects presented on the exterior layer of buildings are usually located at the places whose surface temperatures are higher than their corresponding neighbors. This paper proposed to employ image segmentation to cluster those pixels with similar surface temperatures such that the thermography can be composed of the limited groups. For each segmented group, the surface temperature distribution in each segmented group is homogenous. In doing so, the regional boundaries of the segmented regions can be identified and extracted. A terrestrial laser scanner (TLS) is widely used to collect the point clouds of a structure, and those point clouds can be modeled as a 3D structure. This study established a mapping model such that the segmented thermography can be projected on the 3D structure. In doing so, the 3D structure provides the defects and their spatial locations. In this paper, the structure (a wind turbine) located at Taichung County Gaomei Wetlands is used as an example.

Identifying the Defects Presented on the Exterior Layers of a Structure by Employing 3D Point Clouds and Thermography

Most existing aerial robotics testbeds have significant set-up and maintenance costs that restrict their use. This paper presents a low-cost multi-drone testbed for agile and safe performing proof-of-concept experiments. It uses low-weight, low-size Crazyflies drones and low-cost lighthouse positioning system for drone pose estimations. Its flexible and modular ROS-based architecture can be used to easily test multi-drone perception, control and planning techniques and can easily integrate bigger drones. The presented testbed does not require a dedicated room and can be set-up in less than one hour. The paper presents the testbed and validates its operation with multi-drone target tracking experiments.

An Agile Low-cost Testbed for Multi-Drone Target Tracking

This research focused on the development of unmanned aerial vehicle (UAV) of a dodecacopter system. The dodecacopter is controlled through a Pixhawk 2.4.8 firmware-based flight controller. The communication between the dodecacopter and flight controller is connected through wireless communication system. The dodecacopter is well equipped with latest technology from Pixhawk flight controller for efficient and smooth controlling system. The dodecacopter balancing system is using the technology of barometer MS5611 and magnetometer IST8310 sensors. The developed dodecacopter is equipped with 12 Tarot 4006 brushless motors to increase the payload capability. Based on the results, the experiment shows that the dodecacopter can hover and maintain its position with optimum stability. Observation shows that the dodecacopter performances can be increased by using high-powered motors, lighter batteries. 

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S. van der Velden

Papers

A UAS Testbed for the Flight Demonstration of Structural Health Monitoring Systems