UAV/MAV Design, Prototyping, Instrumentation, and Testing
WVU researchers have extensive experience in designing, manufacturing, and instrumenting
customized UAVs, as well as avionics systems and ground control stations. To date,
nearly 1,000 research flights have been performed using more than 15 different
UAVs. These research platforms have supported a variety of research projects sponsored
by NASA, DOD, DOT, USDA, and private industry.
Platform development projects have spanned size scales from the sub 15 cmspan MAV class to 3 mspan 55 lbTier 1 UAVs. A fleet of three F-22 fighter (Figure 1) platforms were designed, manufactured, and instrumented with customized payloads to validate formation control laws while attempting to match some of the handling qualities and turbine lag seen in the full-scale aircraft at low speeds and maintaining desirable flight characteristics. The highly stable and efficient Phastball research platform (Figure 2) was developed to provide large payload capacity while also providing a test bed for flight control development and in-flight actuator/surface failure and fault tolerance. Phastball’s aerodynamics and structure were a direct outgrowth of the Department's UAV design class and competition team.
Figure 1: F-22 Formation flight platform
Figure 2: Phastball flight control and fault tolerance development
Since 2010, an extremely challenging platform development project has been in progress.
The goal has been to transform 60 mm mortar and 40 mm grenades into hybrid projectiles.
These ballistic-launched projectiles transform to MAVs mid-flight to provide precision
strike and on-demand intelligence, surveillance, and reconnaissance (ISR) capability
to individual squads. These platforms operate in an unusual low Reynolds number,
high G, high velocity environment. Prototype hybrid projectile UAVs (Figure 3)
built in this fashion have been field tested at Aberdeen and Yuma proving grounds
in both 40 mm and 60 mm outer diameter platforms. These field tests proved the
functionality of several WVU hybrid UAV designs, successfully demonstrating wing
and control surface deployment as well as onboard real-time camera operation and
RF data transmission. There were several unique capabilities developed from this
work including transforming UAVs, system miniaturization, and high-G environments.
WVU is also working with Physical Sciences, Inc. on their InstantEye MAV platform with supervised autonomy (Figure 4). Projects have included gust rejection, autonomous ship wake sensing, and measurement and propeller efficiency. It should be noted that the InstantEye is currently fielded by U.S. Special Forces. Other projects have included:
- Biomimetic flying wing with wing morphing showing reduced cruise drag, increased top speed, and equivalent handling qualities (Figure 5)
- Concept development for Tier 2 VTOL long-endurance UAV and advanced STOL cargo aircraft design
Figure 5: Wing morphing
The WVU UAV Simulation Environment for development of path/trajectory generation and tracking algorithms with fault tolerant capabilities is built based on a modular structure for extensive portability and flexibility allowing rapid addition of new aircraft models, new subsystem abnormal condition models, new path generation algorithms, and new trajectory tracking algorithms. Manual control and formation flight modes are implemented allowing for simulation of ground station operation and human operator/UAV interaction. The general focus is on an integrated and comprehensive approach for operational safety including all relevant components: hardware, software, environment, and human factors.
Figure 6: Interface of the WVU UAV Simulation Environment During Simulation
Biomimetic Fault Tolerant Control of Autonomous Flight
WVU researchers have formulated a comprehensive and integrated framework for aircraft abnormal flight condition management inspired by the biological immune system. Artificial intelligence techniques are synergistically blended to develop artificial immune systems (AIS) capable of system status assessment, state prediction, autonomous adaptation, and counter-action of abnormal conditions. The methodology addresses critical issues related to UAV path generation in dynamic environment, on-board decision making, and autonomous trajectory tracking under extended normal and abnormal conditions.
The WVU Design Environment for AIS development is an integrated set of interactive computational tools for data processing, AIS design, system simulation, verification, and evaluation. It provides all capabilities necessary for the development of AIS-based schemes for subsystem abnormal condition detection, identification, evaluation, and accommodation. It creates the premises for the investigation, design, and analysis of advanced technologies for increased UAV operational safety and performance. Such technologies are currently being demonstrated on a UAV within a DARPA-sponsored project.
Multiple Aircraft Cooperative Control and Sensing
In 2004, WVU researchers achieved the first autonomous formation flight using three
subscale YF-22 research aircraft with turbine propulsion. Recently, in a NASA-sponsored
project, a set of close formation flight (12 m separation) experiments were performed
with two UAVs (Fig. 8). Cooperative wind gust- sensing algorithms were developed
to real-time estimate ambient and wake-induced gust disturbance for improved energy
saving and reduced gust-load during formation flight.
Figure 8: WVU Close Formation Flight Experiment
Pilot Vehicle System Integration
Tools are being developed for real-time monitoring and prediction of the pilot vehicle system (PVS) closed-loop stability. Modern aircraft systems have a highly complex closed-loop, starting from sensors, signal processing, displaying and cueing, human pilot, flight control, signal distribution, to actuators, which finally control the aircraft. Current aviation safety research has emphasized the modeling aspect of individual sub-systems within this link. However, a time-varying pilot control model is needed for deriving the closed-loop PVS describing function. This will help us to evaluate PVS stability margins, to predict unsafe flight conditions, and to develop mitigation approaches. Additionally, an improved understanding of low-level human control strategies will help us to better evaluate the impact of new technologies, such as adaptive sensing and control algorithms, on the safety of future aircraft systems.
Pilot-vehicle interaction is investigated from the point of monitoring and evaluating pilot status through biomimetic non-obtrusive techniques. The approach provides assessments of pilot abnormal conditions, such as excessive workload or fatigue, based on real-time dynamic measurements of pilot-plus-aircraft system readily accessible in the cockpit.
Bio-inspired UAV Design
WVU researchers are currently conducting experimental work in the area of biomimetic research, especially in the low Reynolds number flight regime of birds. These studies are aimed at critically evaluating the various phenotypes—especially the morphology—of raptor and songbird (vulture, hawk, kite, blue jay, etc.) wings to correlate the measured aerodynamic performance and corresponding surrounding vector flow fields to apparent local and global wing form behavior. Analysis of the fundamental flow physics related to specific wing feather groups (e.g., various covert groups, alula groups, primaries, and secondaries) will enhance the current understanding of avian flight and potentially aid in the development of transformative new ideas in low Reynolds number aircraft lift, control, and propulsion methods. One short term goal is to improve flight efficiency of UAV and MAV platforms based on bird feather morphology.
Robustly delivering accurate position, navigation, and timing anywhere and at all times represents current research needs. In particular, researchers at WVU are considering UAV navigation within GPS-degraded or denied environments. One project focuses on insect-inspired sensing approaches, such as wide-field optical flow. In another project, multi-constellation global navigation satellite navigation systems are being coupled with various other navigation sensors (both traditional and emerging) to offer robustness in the face of uncertainty. "Sense and avoid" capability, a key step toward integrating unmanned aircraft systems (UAS) into the national airspace system, is also being developed through real-time processing of optical flow data.
Multi-Vehicle Optimal Mission Planning
WVU has developed a method of generating optimal mission plans that enable simultaneous use of multiple autonomous aircraft for advanced ISR missions. This system takes the burden off the operator to fully plan a mission requiring only input of the points of interest and their associated priority. In order to prove this concept, we have completed an initial demonstrator that embodies the fundamental aspects of mission task generation, including extensive simulation and field testing of a preliminary advanced artificial intelligence system using a genetic algorithm as the optimizer. A variety of scenarios were developed for simulation and field testing using three UAVs. A live demonstration of mission planning and flight execution was performed successfully in 2013 at an ARL test facility. Eight points of interest were identified and five selected for surveillance. The planning algorithm then generated an optimum path and the UAVs were programmed and flew the mission. The current prototype system develops the optimal tasking of three homogeneous UAVs to complete a predefined surveillance mission, while balancing the flight time for the set of UAVs. This flexible system can be generalized to handle other types of missions using heterogeneous unmanned vehicles and incorporating a variety of mission requirements.
Various remote sensing applications have been explored at WVU, which involve plant health monitoring, air-sample collection, and traffic monitoring. Currently, researchers are focused on UAV applications that require very accurate positioning for remote sensing:
- Contributing an inertial navigation processing capability to the Jet Propulsion Laboratory's global navigation satellite system precise point positioning software packages, GIPSY and Real-Time GIPSY-X;
- Developing and experimentally validating accurate and robust real-time relative navigation algorithm architectures for UAS formations/swarms by enhancing the state-of-the-art GPS and inertial navigation processing algorithms with the use of impulse radio–ultra wideband technologies.