Calculated Drag of an Aerial Refueling Assembly Through Airplane Performance Analysis

This viewgraph document reviews NASA Dryden's work on Aerial refueling, with specific interest in calculating the drag of the refueling system. The aerodynamic drag of an aerial refueling assembly was calculated during the Automated Aerial Refueling project at the NASA Dryden Flight Research Center. An F/A-18A airplane was specially instrumented to obtain accurate fuel flow measurements and to determine engine thrus

Optimal scheduling for refueling multiple autonomous aerial vehicles

The scheduling, for autonomous refueling, of multiple unmanned aerial vehicles (UAVs) is posed as a combinatorial optimization problem. An efficient dynamic programming (DP) algorithm is introduced for finding the optimal initial refueling sequence. The optimal sequence needs to be recalculated when conditions change, such as when UAVs join or leave the queue unexpectedly. We develop a systematic shuffle scheme to reconfigure the UAV sequence using the least amount of shuffle steps. A similarity metric over UAV sequences is introduced to quantify the reconfiguration effort which is treated as an additional cost and is integrated into the DP algorithm. Feasibility and limitations of this novel approach are also discussed

Maximizing Accuracy through Stereo Vision Camera Positioning for Automated Aerial Refueling

Aerial refueling is a key component of the U.S. Air Force strategic arsenal. When two aircraft interact in an aerial refueling operation, the accuracy of relative navigation estimates are critical for the safety, accuracy and success of the mission. Automated Aerial Refueling (AAR) looks to improve the refueling process by creating a more effective system and allowing for Unmanned Aerial Vehicle(s) (UAV) support. This paper considers a cooperative aerial refueling scenario where stereo cameras are used on the tanker to direct a \boom (a large, long structure through which the fuel will ow) into a port on the receiver aircraft. The analysis focuses on the effects of camera positioning with the rear-facing stereo vision system. In particular, the research seeks the optimal system design for the camera system to achieve the most accurate navigational estimates. The testing process consists of utilizing a simulation engine and recreating real world flights based on previously collected Global Positioning System (GPS) data. Using the pose estimation results and the ground truth information, the system computes the error between the incoming aircraft\u27s position in the virtual world and its calculated location based on the stereo matching algorithm. The testing process includes both un-obscured scenarios and cases where the boom causes significant occlusions in the camera images. The results define the improvements in position and orientation estimation of camera positioning from the consolidated simulation data. Conclusions drawn from this research will propose and help provide recommendations for future Air Force acquisition and development of aerial refueling systems

A Study On Distributed Model Predictive Consensus

We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.Comment: 20 pages, 4 figures, longer version of paper presented at 17th IFAC World Congres

Stereo Vision: A Comparison of Synthetic Imagery vs. Real World Imagery for the Automated Aerial Refueling Problem

Missions using unmanned aerial vehicles have increased in the past decade. Currently, there is no way to refuel these aircraft. Accomplishing automated aerial refueling can be made possible using the stereo vision system on a tanker. Real world experiments for the automated aerial refueling problem are expensive and time consuming. Currently, simulations performed in a virtual world have shown promising results using computer vision. It is possible to use the virtual world as a substitute environment for the real world. This research compares the performance of stereo vision algorithms on synthetic and real world imagery

Aerial Refueling Simulator Validation Using Operational Experimentation and Response Surface Methods with Time Series Responses

An important program in the Department of Defense is the KC-46 Supertanker. Dubbed the future of the Air Force\u27s aerial refueling inventory, the KC-46 will replace dozens of ailing previous generation tanker aircraft. The Aerial Refueling Airplane Simulator Qualification document governs the methods by which Air Mobility Command validates its simulators, some of which will be KC-46 simulators in the near future. The methodology set forward in this thesis utilizes historical data of aircraft performance from similar air frames to gain statistical insight into the performance design space of the KC-46. Leveraging this insight, the methodology provides through a framework for validation that uses classical experimental design principles as applied to time history responses such as found in aircraft performance measures. These principles guide the generation of response surfaces from real world flight test data that can then be used to validate flight training simulators using a point by point comparison or over an entire surface of points for a variety of different aerial refueling maneuvers. This work also supports the KC-46 Tanker program by proposing statistically efficient and cost conscious experimental designs for the KC-46 flight testing. This framework is demonstrated using flight testing data from the KC-135 Aerial Refueling Simulator Upgrade testing, and is part of an Office of the Secretary of Defense initiative to add increased statistical rigor to the Department of Defense test and evaluation enterprise and specifically the acquisition community

Modeling Aerial Refueling Operations

Aerial Refueling: AR) is the act of offloading fuel from one aircraft: the tanker) to another aircraft: the receiver) in mid flight. Meetings between tanker and receiver aircraft are referred to as AR events and are scheduled to: escort one or more receivers across a large body of water; refuel one or more receivers; or train receiver pilots, tanker pilots, and boom operators. In order to efficiently execute the Aerial Refueling Mission, the Air Mobility Command: AMC) of the United States Air Force: USAF) depends on computer models to help it make tanker basing decisions, plan tanker sorties, schedule aircraft, develop new organizational doctrines, and influence policy. We have worked on three projects that have helped AMC improve its modeling and decision making capabilities. Optimal Flight Planning: Currently Air Mobility simulation and optimization software packages depend on algorithms which iterate over three dimensional fuel flow tables to compute aircraft fuel consumption under changing flight conditions. When a high degree of fidelity is required, these algorithms use a large amount of memory and CPU time. We have modeled the rate of aircraft fuel consumption with respect to AC Gross Weight, Altitude and Airspeed. When implemented, this formula will decrease the amount of memory and CPU time needed to compute sortie fuel costs and cargo capacity values. We have also shown how this formula can be used in optimal control problems to find minimum costs flight plans. Tanker Basing Demand Mismatch Index: Since 1992, AMC has relied on a Tanker Basing/AR Demand Mismatch Index which aggregates tanker capacity and AR demand data into six regions. This index was criticized because there were large gradients along regional boundaries. Meanwhile tankers frequently cross regional boundaries to satisfy the demand for AR support. In response we developed continuous functions to score locations with respect to their proximity to demand for AR support as well as their isolation from existing tanker bases. Optimal Scheduling:\u3c\bold\u3e Because most of the tanker resources are controlled by individual Air National Guard Units there is little to no central authority coordinating tanker and receiver training schedules. We have been able to show that significant flying hour savings could be achieved if National Guard tanker units were to yield some of their scheduling autonomy to a central authority which was charged with the responsibility of matching tanker training requirements to receiver training requirements

Wide Area Search and Engagement Simulation Validation

As unmanned aerial vehicles (UAVs) increase in capability, the ability to refuel them in the air is becoming more critical. Aerial refueling will extend the range, shorten the response times, and extend loiter time of UAVs. Executing aerial refueling autonomously will reduce the command and control, logistics, and training efforts associated with fielding UAV systems. Currently, the Air Force Research Lab is researching the various technologies required to conduct automated aerial refueling (AAR). One of the required technologies is the ability to autonomously rendezvous with the tanker. The goal of this research is to determine the control required to fly an optimum rendezvous using numerical optimization and to design a controller that will approximate that control. Two problems were examined. The first problem is for the receiver to rendezvous in minimum time, with a known tanker path. The second problem is for the receiver to rendezvous at a specified time with a known tanker path. For the first problem, the simulated controller results will be compared to the calculated optimal control

Towards Automated Aerial Refueling: Real Time Position Estimation with Stereo Vision

Aerial refueling is essential to the United States Air Force (USAF) core mission of rapid global mobility. However, in-flight refueling is not available to remotely piloted aircraft (RPA) or unmanned aerial systems (UAS). As reliance on drones for intelligence, surveillance, and reconnaissance (ISR) and other USAF core missions grows, the ability to automate aerial refueling for such systems becomes increasingly critical. New refueling platforms include sensors that could be used to estimate the relative position of an approaching aircraft. Relative position estimation is a key component to solving the automated aerial refueling (AAR) problem. Analysis of data from a one-seventh scale, real world refueling scenario demonstrates that the relative position of an approaching aircraft can be estimated at rates between 10 Hz and 30 Hz using stereo vision. Linear regression models on position estimate accuracies predict results reported by other research in the simulation domain, suggesting that real world accuracies are comparable to simulation domain accuracies reported by others. Further, by seeding the position estimation algorithm with previous position estimates, subsequent errors in position estimation are reduce

Autonomous Unmarked Aerial Rendezvous for Automated Aerial Refueling (AAR)

As unmanned aerial vehicles (UAVs) increase in capability, the ability to refuel them in the air is becoming more critical. Aerial refueling will extend the range, shorten the response times, and extend loiter time of UAVs. Executing aerial refueling autonomously will reduce the command and control, logistics, and training efforts associated with fielding UAV systems. Currently, the Air Force Research Lab is researching the various technologies required to conduct automated aerial refueling (AAR). One of the required technologies is the ability to autonomously rendezvous with the tanker. The goal of this research is to determine the control required to fly an optimum rendezvous using numerical optimization and to design a controller that will approximate that control. Two problems were examined. The first problem is for the receiver to rendezvous in minimum time, with a known tanker path. The second problem is for the receiver to rendezvous at a specified time with a known tanker path. For the first problem, the simulated controller results will be compared to the calculated optimal control