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Proper exploitation of derived angle of attack (AOA) and sideslip angle from low-cost attitude heading reference system sensors found in general aviation aircraft is a candidate solution for improv...

This paper develops a multiple-timescale slow state tracking nonlinear controller to accomplish large-amplitude combined longitudinal and lateral/directional maneuvers of a nonlinear, nonstandard s...

This paper develops a theory of output feedback control for a class of nonlinear, nonstandard two-time-scale systems using a controller and a state observer with guaranteed closed-loop stability. U...

The proliferation of unmanned air systems (UAS) in recent years has provided advanced capabilities in many fields and industries. However, this widespread usage of UAS has also introduced many cyber threats. One of the primary threats faced by UAS is the threat of hijacking by cyber attack. This paper seeks to investigate and understand this threat by implementing and validating a cyber hijacking attack on a UAS using a software-in-the-loop simulation. The relevant control theory necessary to develop the attack is explained and demonstrated on a generic linear second-order system, and the attack is implemented in a Gazebo simulation and demonstrated on a quadcopter using the ArduCopter autopilot. This simulation permitted evaluation of the attack throughout several experiments. The experiments allowed for evaluation of the attack under multiple different flight conditions. Simulation results presented in the paper demonstrate that the attack poses a legitimate threat to UAS. The attacker demonstrates successful performance when the vehicle is attempting to hold position, but demonstrates only marginal control over the vehicle when it is attempting to fly to a desired position.

Certain dynamic modes of asymmetric quadrotor configurations are difficult to accurately model analytically. This paper synthesizes an analytical nonlinear parametric state-space model of an asymmetric quadrotor, and verifies it using a non-parametric model calculated from experimentally measured inputs and outputs of the actual vehicle. The offline system identification process produces a discrete-time Linear Time Invariant state-space model using the Observer Kalman Identification algorithm. This model is converted to a continuous time model for comparison to the linearized analytical model. Eigenvlaues, modes, and mode metrics are used to compare the parametric and non-parametric linear models. Results presented in the paper demonstrate that the identified linear model compares well to the linearized analytical model and validates the approach.

In recent years, Unmanned Aerial Systems (UAS) have emerged as an innovative technology to provide spatio-temporal information about weed species in crop fields. Such information is a critical input for any site-specific weed management program. A multi-rotor UAS (Phantom 4) equipped with an RGB sensor was used to collect imagery in three bands (Red, Green, and Blue, 0.8 cm/pixel resolution) with the objectives of (a) mapping weeds in cotton and (b) determining the relationship between image-based weed coverage and ground-based weed densities. For weed mapping, three different weed density levels (high, medium, and low) were established for a mix of different weed species, with three replications. To determine weed densities through ground truthing, five quadrats (1 m &times, 1 m) were laid out in each plot. The aerial imageries were preprocessed and subjected to Hough transformation to delineate cotton rows. Following the separation of inter-row vegetation from crop rows, a multi-level classification coupled with machine learning algorithms were used to distinguish intra-row weeds from cotton. Overall, accuracy levels of 89.16%, 85.83%, and 83.33% and kappa values of 0.84, 0.79, and 0.75 were achieved for detecting weed occurrence in high, medium, and low density plots, respectively. Further, ground-truthing based overall weed density values were fairly correlated (r2 = 0.80) with image-based weed coverage assessments. Among the specific weed species evaluated, Palmer amaranth (Amaranthus palmeri S. Watson) showed the highest correlation (r2 = 0.91) followed by red sprangletop (Leptochloa mucronata Michx) (r2 = 0.88). The results highlight the utility of UAS-borne RGB imagery for weed mapping and density estimation in cotton for precision weed management.

Advances in machine learning and deep neural networks for object detection, coupled with lower cost and power requirements of cameras, led to promising vision-based solutions for sUAS detection. However, solely relying on the visible spectrum has previously led to reliability issues in low contrast scenarios such as sUAS flying below the treeline and against bright sources of light. Alternatively, due to the relatively high heat signatures emitted from sUAS during flight, a long-wave infrared (LWIR) sensor is able to produce images that clearly contrast the sUAS from its background. However, compared to widely available visible spectrum sensors, LWIR sensors have lower resolution and may produce more false positives when exposed to birds or other heat sources. This research work proposes combining the advantages of the LWIR and visible spectrum sensors using machine learning for vision-based detection of sUAS. Utilizing the heightened background contrast from the LWIR sensor combined and synchronized with the relatively increased resolution of the visible spectrum sensor, a deep learning model was trained to detect the sUAS through previously difficult environments. More specifically, the approach demonstrated effective detection of multiple sUAS flying above and below the treeline, in the presence of heat sources, and glare from the sun. Our approach achieved a detection rate of 71.2 +- 8.3%, improving by 69% when compared to LWIR and by 30.4% when visible spectrum alone, and achieved false alarm rate of 2.7 +- 2.6%, decreasing by 74.1% and by 47.1% when compared to LWIR and visible spectrum alone, respectively, on average, for single and multiple drone scenarios, controlled for the same confidence metric of the machine learning object detector of at least 50%. Videos of the solution's performance can be seen at https://sites.google.com/view/tamudrone-spie2020/., Project page: https://sites.google.com/view/tamudrone-spie2020/

The practical autonomous air refueling of unmanned air system tanker aircraft to unmanned air system receiver aircraft will require an integrated relative navigation system and controller that is t...

Landing is a challenging flight phase for automatic control of fixed-wing aircraft. For unmanned air vehicles in particular, it is imperative that model uncertainty be considered in the control synthesis. These vehicles tend to have limited sensors and instrumentation yet must achieve sufficient performance in the presence of modeling uncertainties and exogenous inputs such as turbulence. Quantitative feedback theory has been reported in the literature for design of automatic landing control laws, but none of these controllers has been flight-tested. In this paper, quantitative feedback theory is employed to synthesize robust discrete-time controllers for automatic landing of an unmanned air vehicle. A low-cost flight vehicle with standard aileron, rudder, elevator, and throttle controls is used. Dynamic simulation is conducted using uncertain aircraft models and sensor noise profiles derived from flight hardware. Controllers are initially synthesized in deterministic simulations. Control validation is pe...

This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions. We implement two components of the Cycle-of-Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems., 9 pages, 6 figures

This paper extends the theory of sequential control for nonlinear, nonstandard two-time-scale systems to address uncertainties in the model structure and parameters. The approach first develops a control law based on the reduced subsystems which include the best estimates or worst cases of the uncertainties. Lyapunov design of the manifold of the fast state and the control ensures stability of the reduced subsystems. A composite Lyapunov analysis for the full-order system then yields an update law for online parameter estimation, and lower and upper bounds of the time-scale separation parameter for stability of the closed-loop system. The bounds are functions of the structural uncertainty, and reduce to the expected results in the special case of no structural uncertainty. Despite the time-derivative of the composite Lyapunov function being negative-semidefinite, convergence of the fast state to its manifold and that of the slow state to zero are proven using a result due to Barbalat's lemma. It is rigorously established that the control law can accomplish the desired objective of slow state regulation when the parameter estimation error remains bounded, but does not necessarily go to zero. The theory is demonstrated on a nonlinear, nonstandard spring-mass-damper system with uncertainties in the model of the nonlinear spring.

This paper extends the method of sequential control of a two-time-scale system with multiple slow and fast states from full-state feedback to observer-based feedback. It provides a guideline for the number and role of the observers needed, and proposes a Lyapunov design method for the selection of observer dynamics. The use of lower-order reduced subsystems simplifies the observer design. Using an extension of the existing composite Lyapunov analysis the stability of the full-order system including the controller and the observer in the loop is investigated. The procedure is demonstrated on a nonlinear two-time-scale system which is a combination of two springs, two masses and two dampers. Results in the paper demonstrate that the observer design methodology for a nonlinear system with one slow and one fast state can be extended to a system with multiple slow and fast states. The extended composite Lyapunov analysis is proposed to find an upper bound of the perturbation parameter up to which the full-order closed-loop system including the controller and the observer is stable.

Unmanned air systems with video capturing systems for surveillance and visual tracking of ground targets have worked relatively well when employing gimbaled cameras controlled by two or more operators: one to fly the vehicle, and one to orient the camera and visually track ground targets. However, autonomous operation to reduce operator workload and crew levels is more challenging when the camera is strapdown, or fixed to the airframe without a pan-and-tilt capability, rather than gimbaled, so that the vehicle must be steered to orient the camera field of view. Visual tracking becomes even more difficult when the target follows an unpredictable path. This paper investigates a machine learning algorithm for visual tracking of stationary and moving ground targets by unmanned air systems with nongimbaling, fixed pan-and-tilt cameras. The algorithm is based on Q learning, and the learning agent initially determines an offline control policy for vehicle orientation and flight path such that a target can be tra...

The ability to conduct useful science under the framework of precision agriculture is not only dependent upon the collection of high quality usable data of plants, soil, and water, but also dependent upon the type of vehicle the sensors are flown on, properly tuned sensors, and the way in which the vehicle is flown. To achieve this capability requires the proper matching and integration of air vehicle, sensors, mission design, and image processing techniques. Although commercial Unmanned Air Systems are starting to be equipped with autopilots, sensors, and simple data processing software, they are often limited to only one sensor, and often lack cross platform integration expandability. This paper develops methodologies and procedures for a highly integrated fixed-wing Unmanned Air System that is customized for precision agriculture science. It addresses sensor selection, vehicle platform selection, flight planning, and data processing procedures. The approach is validated by assessment of collected imagery and data from flights conducted on actual plots. Results presented in the paper show that by comparison to data collected during earlier flights with a non-integrated system, the approach presented here which matches vehicle characteristics to sensor characteristics and employs proper flight planning, mission design, and auto-triggering of the sensor produces better data quality, and improved mosaicking. The approach is judged to be a promising candidate for improved data collection for precision agriculture.

Common open-source flight controllers have the ability to log state data, but they often are unable to log at the rates required to obtain good identification of model characteristics. They are also often unable to log important flow parameters such as angle-of-attack and sideslip angle. This paper presents a custom flight test instrumentation system that is capable of providing accurate full-state and control surface deflection measurements for Small Unmanned Air Systems. The system consists of an embedded single-board computer integrated with a collection of sensors including commercial off- the-shelf air data and inertial navigation systems, in addition to direct measurement of control deflections. Developmental Flight Test Instrumentation data logging software runs onboard the computer. This software is released as open-source. Performance of the system is verified by flight test on a quarter scale Piper PA-18 Super Cub airframe. The Observer/Kalman Identification algorithm is used in post-processing to generate linear statespace models, which are then simulated with other input sets and compared to flight data. Results presented in the paper demonstrate that the integrated flight test instrumentation system is able to provide sufficiently accurate data at a high enough sample rate for the generation of quality linear state-space models.

Small unmanned aircraft are being used in an increasing number of applications requiring cooperation between multiple heterogeneous vehicles. Multi-agent control algorithms can be implemented to control such systems but often require an underlying communications framework with integrated payloads. This paper presents a modular heterogeneous multi-agent control framework with payload integration. This framework, named Clark, provides a wireless network between agents without relying on pre-existing communications infrastructure and provides software interfaces for connecting to a variety of payloads. Underlying design requirements that highlight key features of the Clark framework are presented, and an overview of the framework and software architecture is given in the context of fulfilling key design requirements. The architecutre is demonstrated with a flight test of three heterogeneous agents that includes communication performance metrics.

The two major goals of AFOSR Grant FA9550-14-1-0027, Integrated Research/Education University Aircraft Design Program Development, were (a) to contribute to the development cutting edge capstone airplane design courses in U.S. universities. Such courses would take advantage of the most recent developments in software and hardware systems and tools as well as systems design technology to bring into aerospace engineering undergraduate and graduate programs a systems integration perspective and the experience of designing, building, and flying, supported by rigorous analysis and tests, of advanced research unmanned aerial vehicles (UAVs); (b) to contribute to the understanding of design options and tradeoffs that would lead to efficient long-range supersonic aircraft without tail surfaces. No tail surfaces or reduced size tail surfaces may lead to less drag, less weight, lower construction cost, and smaller stealth signatures. The elimination or size-reduction of tail surfaces must be well understood regarding the impact on flight stability and control, especially at low-speed takeoff, approach and landing conditions, and in the context of thorough multidisciplinary optimization that would account for all design criteria, constraints, and objectives. New contributions to tailless or minimal-tail supersonic long-range flight are of importance to the future of both military and civil aircraft.

Hysteretic dynamical systems are challenging to control due to their hard nonlinearity and difficulty in modeling. One type of system with hysteretic dynamics that is gaining use in aerospace systems is the shape-memory alloy-based actuator. These actuators provide aircraft and spacecraft systems with the ability to achieve component-level or vehicle-level geometry or shape changes. Characterization of the material dynamics and properties of these actuators is usually accomplished with empirical testing of physical specimens, in which the hysteresis dynamics are often abstracted to very simplified models or ignored entirely. Machine learning techniques have the potential to learn hysteretic dynamics, but they routinely encounter difficulties that make them unsuitable. This paper proposes and develops a reinforcement learning-based approach that directly learns an input–output mapping characterization of hysteretic dynamics, which is then used as a control policy. A hyperbolic tangent-based model is used t...

As small unmanned aircraft systems become increasingly affordable, reliable, and formally recognized under federal regulation, they become increasingly attractive as novel platforms for civil applications. This paper details the development and demonstration of both fixed-wing and rotorcraft-based unmanned systems for infrastructure assessment tasks. Tasks such as inspecting pavement condition and vegetation encroachment on roadways, debris and tree coverage on railways, and corrosion on bridges and water towers are considered. Rationale for sensor, vehicle, and ground equipment selections are provided, in addition to developed flight operation procedures for two-man crews. Preliminary imagery results are presented and analyzed, and these results demonstrate that both fixed-wing and rotor-wing small unmanned aircraft systems hold promise in enabling rapid and cost-efficient infrastructure health assessment.

This paper presents linear system identification results using noisy data from a six-degree-of-freedom aircraft simulation and data obtained from flight test of an Unmanned Aerial System using the recently developed novel Artificial Neural Network System Identification algorithm. The method uses an artificial neural network with a single input layer and single output layer to learn the elements of the state transition and control distribution matrices directly. This results in a discrete time state-space model of the aircraft dynamics which is then converted to continuous time using standard techniques. For simulated data, the true linear model is used for verifying the identified model. Linear models are generated from the flight data and are compared with results obtained using the well-established Observer/Kalman Identification algorithm. Results show that the neural-network method identifies valid models for both longitudinal and lateral/directional axes, and performs comparably to Observer/Kalman Identification. Advantages of the method include ease-of-use and low complexity.

As small unmanned aircraft systems become increasingly affordable, reliable, and formally recognized under federal regulation, they become increasingly attractive as novel platforms for civil applications. This paper details the development and demonstration of fixed-wing unmanned aircraft systems for precision agriculture tasks. Tasks such as soil moisture content and high throughput phenotyping are considered. Rationale for sensor, vehicle, and ground equipment selections are provided, in addition to developed flight operation procedures for minimal numbers of crew. Preliminary imagery results are presented and analyzed, and these results demonstrate that fixed-wing unmanned aircraft systems modified to carry non-traditional sensors at extended endurance durations can provide high quality data that is usable for serious scientific analysis.

Recent development of unmanned aerial systems has created opportunities in automation of field-based high-throughput phenotyping by lowering flight operational cost and complexity and allowing flexible re-visit time and higher image resolution than satellite or manned airborne remote sensing. In this study, flights were conducted over corn and sorghum breeding trials in College Station, Texas, with a fixed-wing unmanned aerial vehicle (UAV) carrying two multispectral cameras and a high-resolution digital camera. The objectives were to establish the workflow and investigate the ability of UAV-based remote sensing for automating data collection of plant traits to develop genetic and physiological models. Most important among these traits were plant height and number of plants which are currently manually collected with high labor costs. Vegetation indices were calculated for each breeding cultivar from mosaicked and radiometrically calibrated multi-band imagery in order to be correlated with ground-measured plant heights, populations and yield across high genetic-diversity breeding cultivars. Growth curves were profiled with the aerial measured time-series height and vegetation index data. The next step of this study will be to investigate the correlations between aerial measurements and ground truth measured manually in field and from lab tests.

The goal of precision agriculture is to increase crop yield while maximizing the use efficiency of farm resources. In this application, UAV-based systems are presenting agricultural researchers with an opportunity to study crop response to environmental and management factors in real-time without disturbing the crop. The spatial variability soil properties, which drive crop yield and quality, cannot be changed and thus keen agronomic choices with soil variability in mind have the potential to increase profits. Additionally, measuring crop stress over time and in response to management and environmental conditions may enable agronomists and plant breeders to make more informed decisions about variety selection than the traditional end-of-season yield and quality measurements. In a previous study, seed-cotton yield was measured over 4 years and compared with soil variability as mapped by a proximal soil sensor. It was found that soil properties had a significant effect on seed-cotton yield and the effect was not consistent across years due to different precipitation conditions. However, when seed-cotton yield was compared to the normalized difference vegetation index (NDVI), as measured using a multispectral camera from a UAV, predictions improved. Further improvement was seen when soil-only pixels were removed from the analysis. On-going studies are using UAV-based data to uncover the thresholds for stress and yield potential. Long-term goals of this research include detecting stress before yield is reduced and selecting better adapted varieties.