Yeonju Eun

Cooperative. Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithms

This research was supported by the Korea Aerospace Research Institute for a program of development of the Communications, Navigation, Surveillance/Air Traffic Management system for the next generation.

Optimal Arrival Flight Sequencing and Scheduling Using Discrete Airborne Delays

An algorithm for optimal arrival flight sequencing and spacing in a near-terminal area is proposed. The optimization problem and algorithm proposed in this paper are developed for a decision-support tool for air-traffic control, which uses discrete delay times as optimization variables. The algorithm is applicable to various scenarios with situational and operational constraints such as maximum position shift (MPS) constraints or different sets of discrete delay times, depending on aircraft types or approaching routes. The proposed algorithm is based on a branch-and-bound algorithm with linear programming (LP) and Lagrangian dual decomposition. We formulate the sequencing and scheduling problem as LP with linear matrix inequalities (LMIs), which allows computing the lower bound of the cost for the best first search in the branch-and-bound algorithm and propose Lagrangian dual decomposition for computational efficiency. The proposed algorithm is analyzed and validated through illustrative air-traffic scenarios with various operational constraints, and the simulation results show that the computation time can be significantly reduced using the proposed Lagrangian dual-decomposition method.

Cooperative Control of Multiple Unmanned Aerial Vehicles Using the Potential Field Theory

This research was supported by the Korea Airspace Research Institute (KARI) for a program of development of the CNS/ATM system for the next generation. We truly appreciate their financial support.

Cooperative Control of Multiple UCAVs for Suppression of Enemy Air Defense

In recent years, there is a growing interest in employing Unmanned Combat Air Vehicles (UCAVs) for various military missions. One of typical examples is the suppression of Enemy Air Defense (SEAD) mission, in which the objective is to fly to the enemy territory to destroy, or suppress surface-based air defenses such as radars and surface-to-air missile sites. This paper deals with a hierarchical and fully automated control scheme which is suitable to typical SEAD mission using multiple UCAVs. For the path finding and planning, most of the existing control scheme use the Voronoi-diagram concept, but a new path planning method which is based on the potential field concept is proposed. The advantages and disadvantages of existing method and a new method are presented with virtual battle field simulation results.

Efficient Thrust Distribution with Adaptive Pressure Control for Multinozzle Solid Propulsion System

An integrated approach for chamber pressure control and thrust distribution for multiple nozzles of a solid propulsion system is presented. In this study, a solid propulsion system with variable nozzle throat area controlled by a pintle actuator is considered. To keep the hardware configuration simple and lightweight, multiple nozzles share a combustion chamber. Consequently, changing the throat area of one nozzle results in an unintended change of chamber pressure and thrust from other nozzles. Therefore, pressure stabilization and thrust distribution for multiple nozzles should be conducted simultaneously to achieve the desired performance. For the inner control loop, an adaptive sliding mode controller with feedback linearization is used for pressure stabilization. With the pressure control loop working properly, a thrust distribution algorithm into 10 nozzles for translation and attitude maneuver is developed as an outer control loop.

Cooperative Task Assignment and Path Planning of Multiple UAVs Using Genetic Algorithm

This paper addresses the task assignment and path planning problem of multiple UAVs. As one of the strategies for a SEAD(Suppressions of Enemy Air Defense) mission, an efficiency strategy for assignment and path planning of homogeneous UAVs is developed. There are many path planning methods (e.g. potential function method or probabilistic roadmap method) to avoid obstacles, but the Voronoi diagram is used for the path planning with avoiding the “No fly zones” in this study. Since several candidate-paths are needed to solve the problem of timing constraints, the flyable paths in the shape of net is generated by using Voronoi diagram and multiple paths are found as the candidate paths from each UAV to each target. The objective function is determined as accumulated sum of the required time to finish the tasks on each target and the optimization is achieved by using genetic algorithm with appropriate representation of chromosome and genetic operators.

Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

This study aims to develop a controllers decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILPbased optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA).

Operational Characteristics Identification and Simulation Model Verification for Incheon International Airport

Incheon International Airport (ICN) is one of the hub airports in East Asia. Airport operations at ICN have been growing more than 5 percent per year in the past five years. According to the current airport expansion plan, a new passenger terminal will be added and the current cargo ramp will be expanded in 2018. This expansion project will bring 77 new stands without adding a new runway to the airport. Due to such continuous growth in airport operations and future expansion of the ramps, it will be highly likely that airport surface traffic will experience more congestion, and therefore, suffer from efficiency degradation. There is a growing awareness in aviation research community of need for strategic and tactical surface scheduling capabilities for efficient airport surface operations. Specific to ICN airport operations, a need for A-CDM (Airport - Collaborative Decision Making) or S-CDM (Surface - Collaborative Decision Making), and controller decision support tools for efficient air traffic management has arisen since several years ago. In the United States, there has been independent research efforts made by academia, industry, and government research organizations to enhance efficiency and predictability of surface operations at busy airports. Among these research activities, the Spot and Runway Departure Advisor (SARDA) developed and tested by National Aeronautics and Space Administration (NASA) is a decision support tool to provide tactical advisories to the controllers for efficient surface operations. The effectiveness of SARDA concept, was successfully verified through the human-in-the-loop (HITL) simulations for both spot release and runway operations advisories for ATC Tower controllers of Dallas-Fort Worth International Airport (DFW) in 2010 and 2012, and gate pushback advisories for the ramp controller of Charlotte-Douglas International Airport (CLT) in 2014. The SARDA concept for tactical surface scheduling is further enhanced and is being integrated into NASAs Airspace Technology Demonstration-2 (ATD-2) project for technology demonstration of Integrated Arrival-Departure-Surface (IADS) operations at CLT. This study is a part of the international research collaboration between KAIA (Korea Agency for Infrastructure Technology Advancement), KARI (Korea Aerospace Research Institute) and NASA, which is being conducted to validate the effectiveness of SARDA concept as a controller decision support tool for departure and surface management of ICN. This paper presents the preliminary results of the collaboration effort. It includes investigation of the operational environment of ICN, data analysis for identification of the operational characteristics of the airport, construction and verification of airport simulation model using Surface Operations Simulator and Scheduler (SOSS), NASAs fast-time simulation tool.

Nonlinear aircraft tracking filter utilizing a point mass flight dynamics model

A nonlinear aircraft tracking filter using a point mass flight dynamics model with three degrees of freedom is presented. While the models used by conventional air traffic control tracking filters are based on simple kinematics, the model for the present filter is based not only on kinematic relations but also on three-dimensional aircraft translational force equations and control variables. This allows for practical and sophisticated implementation of the attitude effects on translational acceleration. The control variables, which consist of the angle of attack, roll angle, and thrust setting, are treated as states with random processes. Tracking with simulation data indicates that the present filter is superior to other single and multiple model-based filters in terms of position and course accuracy, and the model associated with it is insensitive to flight motion types and design parameters. The results of tracking with real flight data also correspond well with those found by tracking with the simulation data.

Comparison of First-Come First-Served and Optimization Based Scheduling Algorithms for Integrated Departure and Arrival Management [STUB]

Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA) are investigating scheduling algorithms that will be a part of an integrated arrival and departure management system. Inha University, one of the Korean collaborators of KARI, developed an Extended First-Come First-Served (EFCFS) algorithm that is robust and efficient. However, since the EFCFS algorithm sequentially computes the schedule based on priority, the end results may not be optimal for system efficiency. The approach based on Mixed Integer Linear Programming (MILP) originally developed by NASA and modified by KARI is known to produce better schedules at the expense of computational cost. In this paper, the two different scheduling approaches are compared using common traffic scenarios and constraints at Incheon International Airport. Capabilities to apply weight class based wake turbulence runway separation minima and Miles-in-Trail (MIT) restrictions at selected meter fixes are added to the previously developed EFCFS scheduler. Based on historic data, 40 departures and 20 arrivals are chosen in a one-hour period and 100 scenarios were created by randomly assigning gate numbers, gate departure times, and runway landing times. With the current runway separation requirements, MILP resulted in about ten to twenty percent smaller average delays depending on the constraints. With artificially increased separation minima, the difference between MILP and EFCFS became more noticeable. However, the EFCFS was about ten times faster with smaller variations among different scenarios and constraints. The comparison suggests that the MILP-based algorithm has a small advantage at the current traffic level; however, has potential to be more effective in higher demand or severe weather situations. The EFCFS algorithm may be better suited for real-time applications or investigating larger scale scheduling problems.