Civil infrastructure inspection is crucial to maintaining the quality of that infrastructure, which has a great impact on the economy. Performing this inspection is costly work that requires workers to be trained on how to use varying technologies, which can be error prone when performed manually and can result in damage to the infrastructure in some cases. For this reason, nondestructive evaluation (NDE) sensors are preferred for civil infrastructure inspection as they can perform the necessary inspection without damaging the infrastructure. In this paper, we develop a fully autonomous robotic system capable of real-time data collection and quasi-real-time data processing. The robotic system is equipped with several NDE sensors that allow for a sensor fusion method to be developed that successfully minimizes inspection time while performing adequate inspection of areas that require more in-depth data to be collected. A detailed discussion of the inspection framework developed for this robotic system, and the dual navigation modes for both indoor and outdoor autonomous navigation is presented. The developed robotic system is deployed to inspect several infrastructures (e.g., parking garages, bridges) at and near by the University of Nevada, Reno campus. K E Y W O R D Sconcrete inspection, field robots, non-destructive inspection SUPPORTING INFORMATIONAdditional supporting information may be found online in the Supporting Information section at the end of the article.How to cite this article: Gibb S, La HM, Le T, Nguyen L, Schmid R, Pham H. Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection.

Wildland fire fighting is a very dangerous job, and the lack of information of the fire front is one of main reasons that causes many accidents. Using unmanned aerial vehicle (UAV) to cover wildfire is promising because it can replace human in hazardous fire tracking and save operation costs significantly. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collision as well as cooperate well with other neighbors. Experimental results are conducted to demonstrate the capabilites of the UAV team in covering a spreading wildfire.

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Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the "fire front" to chart the progress of the fire and areas that will likely spread next. Lack of information of the fire front causes many accidents. Using Unmanned Aerial Vehicles (UAVs) to cover wildfire is promising because it can replace humans in hazardous fire tracking and significantly reduce operation costs. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.

Fast and robust gate perception is of great importance in autonomous drone racing. We propose a convolutional neural network-based gate detector (GateNet 1 ) that concurrently detects gate's center, distance, and orientation with respect to the drone using only images from a single fish-eye RGB camera. GateNet achieves a high inference rate (up to 60 Hz) on an onboard processor (Jetson TX2). Moreover, GateNet is robust to gate pose changes and background disturbances. The proposed perception pipeline leverages a fish-eye lens with a wide field-of-view and thus can detect multiple gates in close range, allowing a longer planning horizon even in tight environments. For benchmarking, we propose a comprehensive dataset (AU-DR) that focuses on gate perception. Throughout the experiments, GateNet shows its superiority when compared to similar methods while being efficient for onboard computers in autonomous drone racing. The effectiveness of the proposed framework is tested on a fully-autonomous drone that flies on previously-unknown track with tight turns and varying gate positions and orientations in each lap.