This article describes the design, fabrication, and flight test evaluation of a morphing geometry quadcopter capable of changing its intersection angle in-flight. The experiments were conducted at the Aircraft Computational and Resource Aware Fault Tolerance (AirCRAFT) Lab, Parks College of Engineering, Aviation and Technology at Saint Louis University, St. Louis, MO. The flight test matrix included flights in a “Figure-8” trajectory in two different morphing configurations (21° and 27°), as well as the nominal geometry configuration, two different flight velocities (1.5 m/s and 2.5 m/s), two different number of waypoints, and in three planes—horizontal, inclined, and double inclined. All the experiments were conducted using standard, off-the-shelf flight controller (Pixhawk) and autopilot firmware. Simulations of the morphed geometry indicate a reduction in pitch damping (42% for 21° morphing and 57.3% for 27° morphing) and roll damping (63.5% for 21° morphing and 65% for 27° morphing). Flight tests also demonstrated that the dynamic stability in roll and pitch dynamics were reduced, but the quadcopter was still stable under morphed geometry conditions. Morphed geometry also has an effect on the flight performance—with a higher number of waypoints (30) and higher velocity (2.5 m/s), the roll dynamics performed better as compared to the lower waypoints and lower velocity condition. The yaw dynamics remained consistent through all the flight conditions, and were not significantly affected by asymmetrical morphing of the quadcopter geometry. We also determined that higher waypoint and flight velocity conditions led to a small performance improvement in tracking the desired trajectory as well.

Data acquired from several flights of a custom-fabricated Hexacopter Unmanned Aerial System (UAS) with composite structure (carbon fiber arms and central hub) and composite (carbon fiber) propellers are described in this article. The Hexacopter was assembled from a commercially available kit (Tarot 690) and flown in manual and autonomous modes. Takeoffs and landings were under manual control and the bulk of the flight tests was conducted with the Hexacopter in a “position hold” mode. All flights were flown within the UAS flight cage at Parks College of Engineering, Aviation and Technology at Saint Louis University for approximately 5 min each. Several failure conditions (different types, artificially induced) on the composite (carbon fiber) propellers were tested, including failures on up to two propellers. The dataset described in this article contains flight data from the onboard flight controller (Pixhawk) as well as three accelerometers, each with three axes, mounted on the arms of the Hexacopter UAS. The data are included as supplemental material.

This article describes the data acquired from multiple flights of a custom-built quadcopter. The Quadcopter was programmed to fly a pre-defined “Figure-8” flight path, at a constant altitude. The data set includes flights with a varying number of waypoints (10 and 15 waypoints in each lobe of the “Figure-8”) and at two different velocities (1.5 and 2.5 m/s). The data also contains information on the output of the flight controller in terms of the Pulse Width Modulation (PWM) signals to each of the four Electronic Speed Controllers (ESC) driving the motors, the recorded outputs of the Inertial Measurement Unit (linear accelerations a_x, a_y, a_z and angular velocities p, q, r), GPS data (Latitude, Longitude, altitude, Horizontal Dilution of Precision (HDOP) and Vertical Dilution of Precision (VDOP). The data are included as ^{Supplemental Material}.

The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models.