DroneWiS: Automated Simulation Testing of small Unmanned Aerial System in Realistic Windy Conditions

Unmanned Aerial Systems for Field Scouting and Spraying

This work investigates the integration of solid oxide fuel cells (SOFCs) and a small methanol/nitromethane fueled piston engine as a prospective hybrid powertrain for small unmanned aerial systems (UASs). The increased chemical energy density of a liquid fuel when compared to traditional batteries, along with ease of storage, accessibility, and refuel time make the use of a liquid fuel powered UAS preferable when compared to battery only power UAS’. Currently small UAS’ of increasing interest as a research area, as they have a wide application to a variety of fields. UAS’ are currently being used for precision agricultural crop management and water resource visual inspection. UAS’ are a cost effective avenue to survey water resources and track water runoff that is contaminating water resources. UAS’ can be easily automated and fitted with sensors and cameras capable of providing actionable feedback to the user. The use of UAS’ for land management and survey is expected to continue to expand. However, nearly all UAS’ are powered by a typical lithium polymer battery pack, giving an average endurance of approximately twenty minutes. This is acceptable to most hobbyists and for short filming duration; however, it limits UAS’ to only being able to be operated in close proximity to the user. Current power plants for UAS’ are not suited for long duration missions, such as the survey of water resources. Therefore, the development of a hybrid power plant is crucial for UAS’ to be utilized to their full potential as a survey tool. This work introduces a small internal combustion engine to act as a partial oxidation fuel reformer, producing high temperature exhaust and syngas. The exhaust of this engine is then analyzed as a fuel source for tubular SOFC’s. The SOFC is integrated into the exhaust of a 3.3 cm3 nitromethane fueled two-stroke engine, achieving a maximum power of 680 mW/cm2. A theoretical comparison of flight time indicates that the modular hybrid system could increase a typical small UAS’ flight time beyond 1 hour. The system is capable of achieving a significantly higher energy density than traditional lithium polymer batteries.

Small Unmanned Aerial Systems (UASs) have great potential for many different applications [1- 5]. The small UASs are lightweight, man-portable, and capable of carrying payloads. For military applications, these systems provide valuable intelligence, surveillance, reconnaissance, and target acquisition (ISRTA) capabilities for units at the infantry battalion, company, and platoon levels. The power system is a key component for small UASs to perform extended and long-range missions. We have selected, examined, developed, and evaluated several cutting-edge power and energy technologies to power small UASs. Currently, the capabilities of a small UAS are limited by its power source. Small UASs are mainly powered by advanced batteries, which cannot sustain extended operations. Small engine generators are not a viable solution because they generate pollutants and can be noisy, which could be detected by the adversary. Solar cells are not efficient enough to be used as the primary power system and are limited by weather conditions. Polymer Electrolyte Membrane Fuel Cells (PEMFCs) still have the same technical constraint, the source of hydrogen, as they did many years ago. The objective of this work is to develop, demonstrate, and integrate a highly efficient, lightweight 350 W Solid Oxide Fuel Cell (SOFC) system for small UAS applications. The result of this developmental effort will be a power system to support increased mission duration, power, and reliability of the small UAS, resulting in improved situational awareness. Improved situational awareness capabilities will specifically benefit Department of Defense convoys, route clearance missions, base/defense patrols, and other reconnaissance objectives. The research and development efforts presented here not only apply to small UASs but can also help extend mission operations for unmanned ground vehicle systems and Soldier-portable power application.

Abstract. This paper presents the ongoing development of a small unmanned aerial mapping system (sUAMS) that in the future will track its trajectory and perform 3D mapping in near-real time. As both mapping and tracking algorithms require powerful computational capabilities and large data storage facilities, we propose to use the RoboEarth Cloud Engine (RCE) to offload heavy computation and store data to secure computing environments in the cloud. While the RCE's capabilities have been demonstrated with terrestrial robots in indoor environments, this paper explores the feasibility of using the RCE in mapping and tracking applications in outdoor environments by small UAMS. The experiments presented in this work assess the data processing strategies and evaluate the attainable tracking and mapping accuracies using the data obtained by the sUAMS. Testing was performed with an Aeryon Scout quadcopter. It flew over York University, up to approximately 40 metres above the ground. The quadcopter was equipped with a single-frequency GPS receiver providing positioning to about 3 meter accuracies, an AHRS (Attitude and Heading Reference System) estimating the attitude to about 3 degrees, and an FPV (First Person Viewing) camera. Video images captured from the onboard camera were processed using VisualSFM and SURE, which are being reformed as an Application-as-a-Service via the RCE. The 3D virtual building model of York University was used as a known environment to georeference the point cloud generated from the sUAMS' sensor data. The estimated position and orientation parameters of the video camera show increases in accuracy when compared to the sUAMS' autopilot solution, derived from the onboard GPS and AHRS. The paper presents the proposed approach and the results, along with their accuracies.

<p>Thermal infrared (TIR) remote sensing has a wide array of applications in the environmental sciences, but such applications often require absolute temperature estimates with a high degree of accuracy. Low cost microbolometer-based imaging sensors present a possible alternative for such applications, being lightweight enough for deployment on small Unmanned Aerial Systems (UASs), and thus potentially opening up a new range of applications requiring high spatial or temporal resolution and flexible flight planning. These sensors however lack temperature stabilization of the imaging focal plane array (FPA), prohibiting the reliable retrieval of absolute temperature. Here we present a radiometric calibration methodology developed in laboratory settings using a temperature-controlled chamber and programmable blackbody, allowing for independent control of sensor and target temperatures. These laboratory data provided the basis for linear calibration equations that account for both mean and non-uniformity corrections of the FPA raw radiance counts, as a function of ambient sensor operating temperature. Multiple independent experimental trials were used to extensively validate the algorithm in the laboratory, demonstrating a retrieval error of less than 1 degree Celsius. The calibration methodology was tested under realistic field conditions during a two-day field campaign that utilized ground-based observations of land surface temperature (LST) for both a collection of ground targets with a range of reflectance / emissivity properties, and agricultural plots in Northern California. These field experiments included the deployment of the uncooled microbolometer imaging sensor on a UAS, with acquisitions made throughout a highly variable diurnal period. These UAS experiments demonstrated the effectiveness of the pre-flight calibration methodology under field conditions with excellent agreement between retrieved LST and ground-based infrared thermometers for both homogeneous tarps (R^2 = 0.95) and heterogeneous vegetation plots (R^2 = 0.69 across all crop types), with the full range of target temperatures spanning approximately 15-60 degrees Celsius throughout the campaign. The prediction error for absolute temperature estimates of field targets was found to be within 1 degree Celsius, within the range considered acceptable for many vegetation monitoring applications. We further present results of the application of these UAS-based remote measurements of LST to quantify evapotranspiration (ET) for multiple crop systems. UAS flights were conducted over wheat, soybean and maize fields throughout diurnal periods during the growing season of each crop. LST observations were integrated into the Surface Temperature Initiated Closure (STIC) biophysical evapotranspiration model to estimate ET. Validation against eddy covariance system estimates of evapotranspiration (latent energy flux) shows high predictive accuracy (R^2 > 0.95).</p>

This paper reports on lessons learned in rapidly getting data from a small tactical unmanned aerial system (sUAS) to an incident commander during a 2012 high fidelity hazardous materials exercise. In order to capture the Public Safety data-to-decision path, observational data was collected on three flights of an AirRobot 100B sUAS, used extensively by the US Army, with HazMat specialists as part of a chemical train derailment exercise at the 2012 Summer Institute at Disaster City®. The Summer Institute found that (i) the data path requires an average of 4 steps to go from the field to the incident commander, (ii) there is no standard data format which reduces the value of the data nor agreed upon paths for submission which leads to “broken” paths, (iii) redundant data-to-decision paths are essential in order to ensure information flow, and (iv) the average time from when the data was seen by the sUAS to its arrival at incident command was 27.8 minutes. The observations also led to three recommendations for companies producing devices: (i) sUAS should have a reliable capability to record to USB flash drive; (ii) all video and photographic imagery should have the relevant GPS and heading information embedded in the data; and (iii) systems should have the ability to provide cellular and wireless transmission capabilities (including web browsers and email) as responders may not have access to public phone Wi-Fi and internal Ethernet networks. The analysis also suggests that current measures of quality of service (QoS) focus only on device-to-device transfer rates, not the when the decision maker sees the data and if it is in a form to act upon.

This paper proposes a solution to generate energy-efficient trajectories for small fixed-wing Unmanned Aerial Systems (UAS) to harvest energy available from the surrounding wind mass. The process of generating such trajectories consists of the identification of the wind field and the different wind features such as gusts and/or wind shear in order to generate on a waypoint-to-waypoint basis trajectories that increase the efficiency. The paper presents a method to generate smooth trajectories that prioritize the wind energy harvesting for different cases of wind fields. The algorithm for curve generation is a tridiagonal system of linear equations with slope unitary vector, tension and velocity function parameters in order to determine the spline direction at each node. The unitary direction is adjusted for each node to harvest wind energy and also the tension parameters can be determined to adjust the curvature and its rate of change, so that they can be aligned with the dynamic specification of the UAS. Simulations and results are presented in order to demonstrate the performance of the proposed methods showing dramatic airspeed gains for cases in which the wind vector is considered as an input of the curve parametrization.

In the dynamic mechanics analysis of light and small unmanned aerial systems (UAS), The importance of the high-precision modelling of UAS is unquestionable. Modelling can be classified into 3D geometric model modelling and finite element model modelling, of which the finite element model modelling is mainly divided into four parts: discretization, connection modelling, contact modelling and material attribute definition. This review summarizes the existing modelling methods of light and small UAS, and provides the basic methods and modelling methods for the simulation calculation of light and small UAS. Implications for practice and future research are provided.

Interest in using inexpensive Unmanned Aerial System (UAS) technology for topographic mapping has recently significantly increased. Small UAS platforms equipped with consumer grade cameras can easily acquire high-resolution aerial imagery allowing for dense point cloud generation, followed by surface model creation and orthophoto production. In contrast to conventional airborne mapping systems, UAS has limited ground coverage due to low flying height and limited flying time, yet it offers an attractive alternative to high performance airborne systems, as the cost of the sensors and platform, and the flight logistics, is relatively low. In addition, UAS is better suited for small area data acquisitions and to acquire data in difficult to access areas, such as urban canyons or densely built-up environments. The main question with respect to the use of UAS is whether the inexpensive consumer sensors installed in UAS platforms can provide the geospatial data quality comparable to that provided by conventional systems.This study aims at the performance evaluation of the current practice of UAS-based topographic mapping by reviewing the practical aspects of sensor configuration, georeferencing and point cloud generation, including comparisons between sensor types and processing tools. The main objective is to provide accuracy characterization and practical information for selecting and using UAS solutions in general mapping applications. The analysis is based on statistical evaluation as well as visual examination of experimental data acquired by a Bergen octocopter with three different image sensor configurations, including a GoPro HERO3+ Black Edition, a Nikon D800 DSLR and a Velodyne HDL-32. In addition, georeferencing data of varying quality were acquired and evaluated. The optical imagery was processed by using three commercial point cloud generation tools. Comparing point clouds created by active and passive sensors by using different quality sensors, and finally, by different commercial software tools, provides essential information for the performance validation of UAS technology.

Hybrid-Electric aircraft powertrain modeling for Unmanned Aerial Systems (UAS) is a useful tool for predicting powertrain performance of the UAS aircraft. However, for small UAS, potential gains in range and endurance can depend significantly on the aircraft flight profile and powertrain control logic in addition to the subsequent impact on the performance of powertrain components. Small UAS aircraft utilize small-displacement engines with poor thermal efficiency and, therefore, could benefit from a hybridized powertrain by reducing fuel consumption. This study uses a dynamic simulation of a UAS, representative flight profiles, and powertrain control logic approaches to evaluate the performance of a series hybrid-electric powertrain. Hybrid powertrain component models were developed using lookup tables of test data and model parameterization approaches to generate a UAS dynamic system model. These models were then used to test three different hybrid powertrain control strategies for their ability to provide efficient IC engine operation during the charging process. The baseline controller analyzed in this work does not focus on optimizing fuel efficiency. In contrast, the other two controllers utilize engine fuel consumption data to develop a scheme to reduce fuel consumption during the battery charging operation. The performance of the powertrain controllers is evaluated for a UAS operating on three different representative mission profiles relevant to cruising, maneuvering, and surveillance missions. Fuel consumption and battery state of charge form two metrics that are used to evaluate the performance of each controller. The first fuel efficiency-focused controller is the ideal operating line (IOL) strategy. The IOL strategy uses performance maps obtained by engine characterization on a specialized dynamometer. The simulations showed the IOL strategy produced average fuel economy improvements ranging from 12%-15% for a 30-minute mission profile compared to the baseline controller. The last controller utilizes fuzzy logic to manage the charging operations while maintaining efficient fuel operation where it produced similar fuel saving to the IOL method but were generally higher by 2-3%. The importance of developing detailed dynamic system models to capture the power variations during flight with fuel-efficient powertrain controllers is key to maximizing small UAS hybrid powertrain performance in varying operating conditions.

The use of aerial systems in a variety of real applications is increasing nowadays. These offer solutions to existing problems in ways that have never seen before thanks to their capability to perform perching, grasping or manipulating in inaccessible or dangerous places. Many of these applications require small-sized robots that can maneuver in narrow environments. However, these are required to have also strength enough to perform the desired tasks. This balance is sometimes unreachable due to the fact that traditional servomotors are too heavyweight for being carried by such small unmanned aerial systems (UAS). This paper, offers a innovative solution based on twisted and coiled polymers (TCP) muscles. These tensors have a high weight/strength ratio (up to 200 times) compared with traditional servos. In this work, the practical and modeling work done by the authors is presented. Then, a preliminary design of a bio-inspired claw for an unmanned aerial system (UAS) is shown. This claw has been developed using additive manufacturing techniques with different materials. Actuated with TCP, it is intrinsically compliant and offers a great force/weight ratio.

Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.

Experiments conducted in the low-altitude coastal atmosphere in New Zealand have demonstrated the potential of a new unmanned aerial system (UAS) for meteorological research. The Kahu unmanned aerial vehicle flies autonomously using GPS and pre-programmed waypoints, collecting observations of air temperature and relative humidity that are relayed to a ground-station near-instantaneously. Experiments conducted in the Hauraki Gulf, Auckland, show that the Kahu's radio transmission system can successfully transmit data across the ocean surface at distances up to 25 km. Accuracy of the meteorological data collected by the UAS was assessed via a direct comparison with weather station sensors and radiosonde soundings at heights of up to 500 m in the Bay of Plenty. Close agreement between the UAS, radiosonde and weather station data suggests that the Kahu UAS has considerable scope as a new field research tool in New Zealand, capable of providing reliable atmospheric data that can complement and even supplement conventional low-altitude sampling techniques.

Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes).

As small Unmanned Aerial Systems (UAS) proliferate, encounters between non-participants and UAS become much more frequent. Many of these are due to carelessness or not following rules on the part of the UAS operator. However, even when flying within the rules, encounters-potentially fatal-are possible. To help mitigate the risk of injury by UAS, health monitoring systems are imperative for reducing situations in which loss of control is likely. Current health monitoring tends to use real-time checks for power and navigation issues while a few systems are available for testing changes in vehicle responses to control inputs after flights. To reduce the likelihood of loss of control, we introduce a real-time health monitoring system that analyzes navigation, control, power, sensor, and communications integrity. Through experimental validation, we define metrics which detect degradations in the integrity of each system stated previously. Most failures present symptoms over time which can be detected, preventing the final catastrophic failure from occurring. Information requirements and necessary response times and thresholds are evaluated for each of the monitored subsystems, helping to define the implementation of each integrity check. Integration with a flight controller, particularly on small UAS which do not have the capacity to carry an auxiliary computer, is factored into the architecture, ensuring that health monitoring does not adversely affect flight control. Overall, this architecture provides a template and the considerations necessary for implementing more robust realtime health monitoring systems on the various UAS flight systems in operation.