Practical Distributed Control for VTOL UAVs to Pass a Tunnel

Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. An air traffic management (ATM) system is needed to help ensure that this newest entrant into the skies does not collide with others. In an ATM, airspace can be composed of airways, intersections and nodes. In this paper, for simplicity, distributed coordinating the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass an airway is focused. This is formulated as a tunnel passing problem, which includes passing a tunnel, inter-agent collision avoidance and keeping within the tunnel. Lyapunov-like functions are designed elaborately, and formal analysis based on invariant set theorem is made to show that all UAVs can pass the tunnel without getting trapped, avoid collision and keep within the tunnel. What is more, by the proposed distributed control, a VTOL UAV can keep away from another VTOL UAV or return back to the tunnel as soon as possible, once it enters into the safety area of another or has a collision with the tunnel during it is passing the tunnel. Simulations and experiments are carried out to show the effectiveness of the proposed method and the comparison with other methods

Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation

Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system, equipped with a visual tracking approach, has been widely used in aviation, navigation, agriculture,transportation, and public security, etc. As is mentioned above, the UAV-based aerial tracking platform has been gradually developed from research to practical application stage, reaching one of the main aerial remote sensing technologies in the future. However, due to the real-world onerous situations, e.g., harsh external challenges, the vibration of the UAV mechanical structure (especially under strong wind conditions), the maneuvering flight in complex environment, and the limited computation resources onboard, accuracy, robustness, and high efficiency are all crucial for the onboard tracking methods. Recently, the discriminative correlation filter (DCF)-based trackers have stood out for their high computational efficiency and appealing robustness on a single CPU, and have flourished in the UAV visual tracking community. In this work, the basic framework of the DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art DCF-based trackers are orderly summarized according to their innovations for solving various issues. Besides, exhaustive and quantitative experiments have been extended on various prevailing UAV tracking benchmarks, i.e., UAV123, UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903 frames in total. The experiments show the performance, verify the feasibility, and demonstrate the current challenges of DCF-based trackers onboard UAV tracking.Comment: 28 pages, 10 figures, submitted to GRS

Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement

Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently

Suitability of Low Cost Commercial Off-the-Shelf Aerial Platforms and Consumer Grade Digital Cameras for Small Format Aerial Photography

Many research projects require the use of aerial images. Wetlands evaluation, crop monitoring, wildfire management, environmental change detection, and forest inventory are but a few of the applications of aerial imagery. Low altitude Small Format Aerial Photography (SFAP) is a bridge between satellite and man-carrying aircraft image acquisition and ground-based photography. The author’s project evaluates digital images acquired using low cost commercial digital cameras and standard model airplanes to determine their suitability for remote sensing applications. Images from two different sites were obtained. Several photo missions were flown over each site, acquiring images in the visible and near infrared electromagnetic bands. Images were sorted and analyzed to select those with the least distortion, and blended together with Microsoft Image Composite Editor. By selecting images taken within minutes apart, radiometric qualities of the images were virtually identical, yielding no blend lines in the composites. A commercial image stitching program, Autopano Pro, was purchased during the later stages of this study. Autopano Pro was often able to mosaic photos that the free Image Composite Editor was unable to combine. Using telemetry data from an onboard data logger, images were evaluated to calculate scale and spatial resolution. ERDAS ER Mapper and ESRI ArcGIS were used to rectify composite images. Despite the limitations inherent in consumer grade equipment, images of high spatial resolution were obtained. Mosaics of as many as 38 images were created, and the author was able to record detailed aerial images of forest and wetland areas where foot travel was impractical or impossible

Application of chemical kinetic modeling to improve design and performance criteria for a practical incineration system

In this study, detailed thermo-chemical kinetics with networked ideal reactor model were applied to simulate a practical combustion system -the Secondary Combustion Chamber (SCC) of the Rotary Kiln incineration Simulator (RKIS) at the EPA facility at Research Triangle Park, NC. The networked ideal model was developed using analysis of reactor geometry, temperature profile measurements, and SO2 tracer data provided by EPA. A computer simulation of the networked model was developed using the CHEMKIN H library. A parallel effort considered the effects of non-ideal mixing on detailed thermo-kinetic, simulations. Specifically, an alternate approach was developed to solve the Partially Stirred Reactor (PaSR) model that allowed the incorporation of large detailed mechanisms. Both ideal and non-ideal modeling approaches were compared with experimental data gathered on a Toroidal Jet Stirred Combustor (TJSQ and the SCC at EPA. SCC experiments measured Product of Incomplete Combustion (PIC) formation of surrogate chlorinated wastes (CCl4 and CH2Cl2)lwhile the TSJC experiments measured PIC formation in ethylene/air combustion for fuel-lean conditions near blowout and fuel-rich conditions. Analysis of the geometry and temperature profiles of the SCC suggested the existence of up to four distinct mixing zones. The RTDs, which were resolved from the tracer studies, further supported a multiple PSR model. A model was chosen based on the best fit to SO2, tracer data and consistency with physical geometry, resulting flow patterns, and temperature measurements. A thermo-kinetic mechanism developed by Chiang (1995) was applied to the model. The model results did not agree well with the experimental data. However, it followed many of the underlying trends revealed by the data. Sensitivity analysis of the parameters was used to further explore trends and recommend potential design improvements to reduce PIC formation. An alternate solution technique was developed for the PaSR which approximated mean conditions and solved the deterministic model to refme the approximation and eventually converge on a solution. The approximation, direct integration, and convergence technique compared favorably with the published Monte Carlo modeling calculations, but used, on average, less than 1/200th of the CPU time. This new technique allowed use of considerably larger detailed mechanisms. Additionally, a generalized PaSR model was proposed to account for the effects of non-ideal macromixing

Obstacle alert and collision avoidance system development for UAVs with Pixhawk flight controller

In recent years, the unmanned aerial vehicles sector has been characterized by its sharp growth, spreading its line of applications and becoming one of the cutting edge technologies in the world. However, this exponential advancement would have been even more extreme but for the restrictive existing legislation that limits its operations. That constraints imposed to drone operations are not legislated in vain. Specially in populated areas, flying drones is a dangerous service that entails risks for the safety of the population. Useless have been the attempts of many major online selling companies to make use of unmanned aerial vehicles serving as dealers of parcels. Further technology needs still to be implemented, for guarantying standards levels of safety in urban zones. This thesis aims to contribute to this required development of drone technology by proposing a preliminary collision avoidance system for unmanned aerial vehi- cles. The project involves assembling a flight-capable quadcopter from scratch and implementing the collision avoidance as an additional subsystem. To that end, a set of ultrasonic range finders are located around the quadcopter. Their acquired raw data is processed in an auxiliary arduino microcontroller board that send tra- jectory corrections to the flight controller of the quadcopter based on the distance information. As a result, a concept proof of an autonomous collision avoidance system is integrated into an unmanned aerial vehicle system. Results are obtained and consecutively analyzed based on ground and flight tests. For that purpose, the flight capabilities of the built quadcopter are proved, and aftewards, the collision avoidance is tested. Overall, the collision avoidance system e ectiveness was achieved. The collision avoidance work principle consisted on warding off from obstacles when detected. Major faced problems are related to stability recover after the collision is avoided. That problem was solved by proving different flight modes, but that issue needs future work to make the collision avoidance more reliable.Ingeniería Aeroespacia

Performance Comparison Of Weak And Strong Learners In Detecting GPS Spoofing Attacks On Unmanned Aerial Vehicles (uavs)

Unmanned Aerial Vehicle systems (UAVs) are widely used in civil and military applications. These systems rely on trustworthy connections with various nodes in their network to conduct their safe operations and return-to-home. These entities consist of other aircrafts, ground control facilities, air traffic control facilities, and satellite navigation systems. Global positioning systems (GPS) play a significant role in UAV\u27s communication with different nodes, navigation, and positioning tasks. However, due to the unencrypted nature of the GPS signals, these vehicles are prone to several cyberattacks, including GPS meaconing, GPS spoofing, and jamming. Therefore, this thesis aims at conducting a detailed comparison of two widely used machine learning techniques, namely weak and strong learners, to investigate their performance in detecting GPS spoofing attacks that target UAVs. Real data are used to generate training datasets and test the effectiveness of machine learning techniques. Various features are derived from this data. To evaluate the performance of the models, seven different evaluation metrics, including accuracy, probabilities of detection and misdetection, probability of false alarm, processing time, prediction time per sample, and memory size, are implemented. The results show that both types of machine learning algorithms provide high detection and low false alarm probabilities. In addition, despite being structurally weaker than strong learners, weak learner classifiers also, achieve a good detection rate. However, the strong learners slightly outperform the weak learner classifiers in terms of multiple evaluation metrics, including accuracy, probabilities of misdetection and false alarm, while weak learner classifiers outperform in terms of time performance metrics

Near real-time monitoring of buried oil pipeline right-of-way for third-party incursion

Many security systems employing different methods have been proposed to protect buried oil pipelines transporting petroleum products from the well head via the refinery to: depots and other receiving stations. Currently there is a security gap in the monitoring of these buried pipelines in real time and in keeping them protected from third party interference. This thesis addresses the problem of monitoring these systems by developing an automated image analysis system with the aid of a low-cost multisensory Unmanned Aerial Vehicle (UAV) for monitoring of buried pipeline right-of-way (ROW). The method used in this research is based on the identification of threat objects of interest from the video frame sequences of the pipeline right-of-way acquired by the UAV. This is achieved by training the system to recognise objects of interest using trained correlation filters. To determine the geographical location of detected objects, the Video frame sequences captured by the UAV platform were ortho-rectified to form ortho-images which were then mosaicked to form a seamless Digital Surface Model (DSM) covering the test area using a photogrammetry model. The DSM formed from the mosaicking of ortho-images is then emerged with a digital globe for geo-referencing of detected objects. Experiments were carried out on a test field located in United Kingdom and Nigeria, where video and telemetry data were collected, then processed using the techniques created in this research. The results demonstrated that the developed correlation filter was able to detect objects of interest despite the distortions that come with the object image, due to the fact that the expected distortion was compensated for using the training images. When compared with the 6 control points in the digital globe the accuracy of the two-dimension DSM gave a misalignment error of between 2 and 3 metres