A Review of DJI’s Mavic Pro Precision Landing Accuracy

Precision landing has the potential to increase the accuracy of autonomous landings. Unique applications require specific landing performance; for example, wireless charging loses efficiency with a misalignment of 100mm. Unfortunately, there is no publicly available information for the DJI Mavic Pro’s landing specifications. This research investigated the ability of a Mavic Pro to land at a specified point accurately. The purpose of this research is to determine if precision landings are more accurate than non-precision autonomous landings and if the Mavic Pro is capable of applications such as wireless charging when using precision landings. A total of 128 (64 precision and 64 non-precision) landings were recorded. A two-tail two-sample t-test compared the differences between Precision Landing On vs. Precision Landing Off (PLON vs. PLOFF). Data showed statistical evidence to reject the null hypothesis indicating there was a statistical performance in mean landing accuracy with PLON (M = 3.45, SD = 1.30) over PLOFF (M = 4.40, SD = 1.89), t(109) = -3.313, p = 0.0013. A one-tail one-sample t-test comparing the landing distance of PLON to 100mm (distance for effective wireless charging) produced statistical evidence to reject the null hypothesis indicating the PLON landing accuracy (M = 87.63mm, SD = 33.02mm) was less than 100mm, t(62) = -2.98, p = 0.002. Evidence showed that precision landings increased the landing performance and may allow for future potential applications, including wireless charging

Comparison of Fixed-Wing Unmanned Aircraft Systems (UAS) for Agriculture Monitoring

Florida citrus growers need inexpensive methods to observe citrus plants to detect disease and stress consistently. Health vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) collected from Unmanned Aircraft Systems (UAS), can be used to identify variation in plant health. Simple-to-operate UAS may enable growers to determine within-field variation more frequently than with inspections from scouts, providing more frequent knowledge about the crop condition. This research compared two low-cost fixed-wing UAS, a $5,000 Parrot Disco Pro Ag and a$16,690 senseFly eBee, each equipped with a Parrot Sequoia multispectral camera, to determine if there were differences in the NDVI data results and ease of operation. There were no statistical differences between NDVI reflectance values obtained using the Disco Pro Ag (M = 0.62, SD = 0.15) and the eBee (M = 0.60, SD = 0.15), t(45) = -1.45; p = 0.15. There was a significant positive correlation between the datasets (Pearson correlation = 0.963, p = 0.00). These results suggest that both the Disco Pro Ag and eBee were equally capable of producing the same data from the Parrot Sequoia multispectral camera. Differences in mobility and methods of waypoint planning between these two low-cost UAS may provide remote pilots with different styles of operation. As growers continue to adopt UAS technology to understand their fields better, the characteristics of each system will be important for quick setup time and ease of use

UAS for Public Safety Operations: A Comparison of UAS Point Clouds to Terrestrial LIDAR Point Cloud Data using a FARO Scanner

Unmanned Aircraft Systems (UAS) can be useful tools for public safety agencies during crime or vehicle accident scene investigations if it can provide value to the resource-constrained agency. The speed of data collection, while minimizing first responder risk, while sustaining an acceptable level of accuracy and precision compared to other tools is where the agency may find value. During a recent homicide investigation in Florida, a UAS provided saved 81% in law enforcement labor hours with an acceptable level of accuracy compared to traditional methods. The purpose of this research was to compare UAS to determine if there were differences in accuracy and precision compared to a FARO terrestrial laser scanner in a crime scene reconstruction scenario. UAS registered point clouds were generated in Pix4Dmapper from a DJI Mavic Pro, Mavic 2 Enterprise Dual, Inspire 1, Inspire 2, Phantom 4 Professional, Parrot Anafi, and Bebop 2 at flying heights of 82, 100, 150, 200, and 250 feet respectively in a grid, double grid, circle, and double grid + circle flight pattern and compared to a FARO terrestrial laser scanner. The UAS point cloud accuracy (M = 33.2mm, SD = 6.4mm), compared to the FARO point cloud t(139) = 56.5, p = 0.00 was determined to be not as accurate as the 2.6mm-accurate FARO scanner point cloud; however, may still have an acceptable level of accuracy for investigators. An analysis of variance showed a flying height of 100 feet AGL yielded the most precision and accuracy combined when compared to other flying heights. The double grid + circle flight pattern had smaller RMS errors compared to the other flight patterns. There was also a significant difference by the UAS aircraft model used. The P4P had a smaller RMS error compared to the six other aircraft examined

Using 3 Dimension Health Vegetation Index Point Clouds to Determine HLB Infected Citrus Trees

Three-dimensional NDVI point clouds can be an innovative method for detecting Huanglongbing (HLB) disease in citrus trees. In February 2018, an Unmanned Aircraft System (UAS) captured narrow-band multispectral images to detect healthiness variations of infected citrus trees. A 30-acre section of a citrus grove in Florida with a known HLB infection was examined to determine if three-dimensional Normalized Difference Vegetation Index (NDVI) point clouds can indicate healthiness variations in HLB-infected citrus trees and how three-dimensional NDVI point clouds compared to two-dimensional NDVI reflectance maps for detecting healthiness variations in HLB-infected citrus trees. Wilcoxon Sign Rank testing compared Whole-Tree Vegetation Indices (WTVI) comprising of point or pixel proportions within five NDVI classifications between three-dimensional NVDI point clouds and two-dimensional NDVI reflectance maps. The results indicated significant differences between three-dimensional and two-dimensional points, grouped at the tree level, for suspected HLB-infected trees (p = 0.000). The data suggests three-dimensional NDVI point cloud points were more sensitive to less healthy levels of NDVI values by 2.7% compared to two dimensional NDVI data for suspected HLB-infected trees and by 10.6% (p = 0.000) for non-suspected HLB-infected trees. Researchers concluded three-dimensional NDVI point clouds could be used to determine healthiness variations in suspected HLB-infected citrus trees. Three-dimensional NVDI point clouds had a wider distribution of five index classifications than two-dimensional NDVI reflectance maps for suspected HLB-infected trees. The vertical structure of the citrus tree may contribute to the difference in distribution. There was a 10.01% (p = 0.021) increase in 3D NDVI point cloud points for non-suspected HLB-infected trees compared to the suspected HLB-infected trees. Additionally, there was a 9.04% (p = 0.032) increase in tree crown dimension for non-suspected HLB-infected trees compared to suspected HLB-infected trees. These data suggest non-suspected HLB-infected trees were larger than suspected HLB-infected trees

Accuracy Assessment of the eBee Using RTK and PPK Corrections Methods as a Function of Distance to a GNSS Base Station

The use of unmanned aircraft systems to collect data for photogrammetry models has grown significantly in recent years. The accuracy of a photogrammetric model can depend on image georeferencing. The distance from a reference base station can affect the accuracy of the results. Positioning corrections data relies on precise timing measurements of satellite signals. The signals travel through the Earth\u27s atmosphere, which introduces errors due to ionospheric and tropospheric delays. The aim of this research was to examine the eBee X and its global GNSS accuracy by comparing the RTK and PPK methods at different base station distances in photogrammetry models. Three factors were compared: 1) RTK and PPK methods, 2) local GNSS receiver via caster and NTRIP service corrections sources, and 3) base station distances between 2.4 km and 42.0 km. The eBee X flights occurred in 2023, at three different flying sites in Southwest Arizona in the United States. The RMSEXYZ values from eight Check Points at each of three flying sites were measured with traditional GNSS survey methods. Through ANOVA testing, there were no statistical differences in RMSEXYZ accuracy between RTK and PPK methods as well as between using a local Reach RS2 GNSS receiver via caster and NTRIP service for the eBee X; however, there was a statistical difference in RMSEXYZ accuracy between base station distances of 2.4 km to 42.0 km, whereas, F(5, 33) = 11.99, p = 0.000. Specifically, base station distances of less than 16.2 km were significantly less than larger distances up to 42.0 km. These data suggest there was a significant difference in total accuracy based on the distance from the GNSS receiver base station providing corrections for the eBee X

Using Unmanned Aircraft Systems to Investigate the Detectability of Burmese Pythons in South Florida

Burmese pythons are an invasive, non-native species of snake to southern Florida and attempts at eradicating the snakes had yielded mixed results. The current rate of detection had been reported as 0.05%. The purpose of this research project was to determine if a UAS equipped with a near-infrared (NIR) camera could be used to detect pythons at a higher rate when compared to a RGB camera. The approach involved collecting 55 images from RGB and NIR cameras, over carcass pythons at flying heights of 3, 6, 9, 12, and 15 meters. A likelihood ratio consisting of a true positive rate over false positive rate was calculated from 101 participant survey responses. Participants were able to detect pythons from an NIR camera with greater likelihood (M = 6.05, SD = 1.94) than a RGB camera (M = 4.74, SD = 1.32), t(10) = 1.77, p = .048. The data suggests that survey participants could correctly detect pythons in images containing the pythons at a 1.3x greater rate with the NIR sensor than with the RGB sensor. Also, a true positive rate (TPR) showed the observation rate of correctly detecting a python when one was present in the image. The NIR camera images had higher TPR rates compared to RGB images. The largest difference between camera types was observed at the 15 meters flying height over an outstretched python; there was a 35% increase in participant detection accuracy using the NIR camera compared to the RGB camera. These results suggest that a UAS equipped with an NIR camera flying between 3 and 15 meters in a nadir-oriented position of 90 degrees can be used to detect pythons. Using a UAS equipped with an NIR camera over levees searching for exposed pythons may help biologists responsible for managing these invasive species determine if a python is present

UAS for Public Safety: Active Threat Recognition

The Center for Homeland Defense and Security identified an increase of active threat events, such as mass shootings, annually since 1999. Literature suggests that 90% of shootings were over before law enforcement arrived at the scene and the first responder response was limited to “surround and contain” until Special Weapons and Tactics Teams (SWAT) arrived on the scene. Using Unmanned Aircraft Systems (UAS) to detect which individual was the threat and type of weapon used can provide useful information to increase the speed of the response for first-on-scene rather than waiting for SWAT if the type of weapon was known. A UAS equipped with a full spectrum sensor compared traditional red-green-blue (RGB) images to near-infrared (NIR) images in a simulated active threat scenario. A true positive rate (TPR) metric was used to measure the percentage of correctly-detected weapons consisting of either a knife, pistol, rifle, shotgun, or shovel at slant range distances of 25-, 50-, 75-, and 100-feet respectively. A convenience sample of 102 survey participants, recruited from constituents of the Airborne Public Safety Association (APSA) and DRONERESPONDERS was conducted to observe 48 randomly-presented images to determine which type of weapon was detected. The results suggest that survey participants could correctly detect weapons at a 12% greater rate with the NIR sensor than the RGB sensor; however, the pistol had the largest difference in TPR between NIR and RGB sensors. The pistol had an increased probability of detection by 33% when using the NIR sensor compared to an RGB sensor. Additionally, differences were also observed between slant range distances. The closest distance of 25 feet showed a 42% increase in participants’ ability to correctly determine the weapon type compared to the 100-foot slant range distance. Therefore, using a NIR sensor-equipped UAS at flying a maximum slant range distance of 50 feet may help a first-responder determine the type of weapon before SWAT arrives on the scene

Viability and Application of Mounting Personal PID VOC Sensors to Small Unmanned Aircraft Systems

Using a UAS-mounted sensor to allow for a rapid response to areas that may be difficult to reach or potentially dangerous to human health can increase the situational awareness of first responders of an aircraft crash site through the remote detection, identification, and quantification of airborne hazardous materials. The primary purpose of this research was to evaluate the remote sensing viability and application of integrating existing commercial-off-the-shelf (COTS) sensors with small unmanned aircraft system (UAS) technology to detect potentially hazardous airborne contaminants in emergency leak or spill response situations. By mounting the personal photoionization detector (PID) with volatile organic compound VOC sensor technology on UAS platforms, the needed information may be obtained at an optimum range and resolution without needlessly exposing a human to possible adverse conditions

A10 – Human Factors Considerations of UAS Procedures and Control Stations: Tasks PC-1 through PC-3 Pilot and Crew (PC) Subtask, Recommended Requirements and Operational Procedures

The Alliance for System Safety of UAS through Research Excellence (ASSURE) conducted research focused on minimum pilot procedures and operational practices used by unmanned aircraft systems (UAS) operators today for the purpose of developing recommendations. This research recommends four pilot and 46 operational minimum procedures to operate a civil single-engine, fixed-wing, single-pilot-configured UAS flying in beyond visual line-of-sight (BVLOS) conditions. These recommendations are anticipated to support potential future aircrew procedure requirements for UAS larger than 55 lbs. operating in the National Airspace System (NAS). These procedures were validated using representative Control Stations in simulated environments