In this paper an Unmanned Aerial Vehicles (UAVs) - enabled dynamic multi-target tracking and data collection framework is presented. Initially, a holistic reputation model is introduced to evaluate the targets' potential in offloading useful data to the UAVs. Based on this model, and taking into account UAVs and targets tracking and sensing characteristics, a dynamic intelligent matching between the UAVs and the targets is performed. In such a setting, the incentivization of the targets to perform the data offloading is based on an effort-based pricing that the UAVs offer to the targets. The emerging optimization problem towards determining each target's optimal amount of offloaded data and the corresponding effort-based price that the UAV offers to the target, is treated as a Stackelberg game between each target and the associated UAV. The properties of existence, uniqueness and convergence to the Stackelberg Equilibrium are proven. Detailed numerical results are presented highlighting the key operational features and the performance benefits of the proposed framework.
Monitoring the earthquake response of full‐scale structures using UAV vision‐based techniques
Vision-based sensing, when utilized in conjunction with camera-equipped unmanned aerial vehicles (UAVs), has recently emerged as an effective sensing technique in a variety of civil engineering applications (e.g., construction monitoring, conditional assessment, and post-disaster reconnaissance). However, the use of these non-intrusive sensing techniques for extracting the dynamic response of structures has been restricted due to the perspective and scale distortions or image misalignments caused by the movement of the UAV and its on-board camera during flight operations. To overcome these limitations, a vision-based analysis methodology is proposed in the present study for extracting the dynamic response of structures using unmanned aerial vehicle (UAV) aerial videos. Importantly, geo-referenced targets were strategically placed on the structures and the background (stationary) region to enhance the robustness and accuracy related to image feature detection. Image processing and photogrammetric techniques are adopted in the analysis procedures first to recover the camera motion using the world-to-image correspondences of the background (stationary) targets and subsequently to extract the dynamic structural response by reprojecting the image feature of the (moving) targets attached to the structures to the world coordinates. The displacement tracking results are validated using the responses of two full-scale test structures measured by analog displacement sensors more »
- Award ID(s):
- 1663569
- Publication Date:
- NSF-PAR ID:
- 10341550
- Journal Name:
- Structural control health monitoring
- Volume:
- 29
- Issue:
- 1
- ISSN:
- 1545-2255
- Sponsoring Org:
- National Science Foundation
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