Biography

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Emre Koyuncu is an Assistant Professor at Istanbul Technical University, Department of Aeronautical Engineering. He has received his Ph.D. (SESAR JU) degree in Aerospace Engineering from ITU in 2015. He was a visiting researcher at Boeing Research and Technology of Europe during 2013-2014, and Massachusetts Institute of Technology (MIT), Aero-Astro Department during 2014-2015. 

His research interests lie in the broad areas of aeronautics, robotics, navigation and control theory.  In particular, he focuses on the implementation of
optimization-based control, reinforcement learning and probability theory for the design and analysis of high-performance cyber-physical systems.

The application areas of his research include agile unmanned aerial vehicles; flight trajectory optimization and planning; airborne conflict detection and resolution; GPS-denied navigation; flight management and decision support systems; high-level autonomy in air traffic control systems; modeling and resiliency assurance in large-scale air traffic networks.

Prof. Koyuncu has lead in numerous research grants and industrial projects from institutes and companies such as SESAR, BOEING, ASELSAN, HAVELSAN, TAI, TUBITAK, STM, Turkish Airlines, and IGA.

Prof. Koyuncu is currently the Vice-Dean of Aerospace Institute of Istanbul Technical University, Co-PI of ITU Aerospace Research Center (ITU ARC) as the director of AI, Controls and Avionics Research Group.

He is the recipient of a Boeing Early Career Grant in 2015 and SESAR JU Ph.D. Fellowship during 2012-2015

In addition to being IEEE Senior Member, he serves as a Associate Editor of IEEE Access Journal, TC member of IEEE Control Systems Society on Hybrid Systems, and TC member of AIAA Air Transportation Systems.

Research groups

Artificial Intelligence and Smart Air Transportation

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Güney Güner

PhD Student, Aero-Astro Eng.

AI-based Data Manipulation, Data Visualization, Air Combat Simulations

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Arınç Tutku Altun

PhD Student, Aero-Astro Eng.

AI-based Large Scale Network Models

4

Orhan Özdemir

MSc Student, Aero-Astro Eng.

AI-based Data Manipulation, Data Visualization

4

Muhammet Aksoy

MSc Student, Aero-Astro Eng.

ML-based Flight Anomaly Detection, AI-based Air Combat Games


Highly Agile Aerial Vehicle Control

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Mehmet Hasanzade

PhD Candidate, Aero-Astro Eng.

RL-based Agile Flight Trajectory Generation

4

Emre Saldıran

PhD Student, Defence Sc

(Co-advised) Agile Flight Trajectory Tracker and Autopilot Design

4

Mustafa Demir

PhD Candidate, Mechatron Eng.

Cooperation and Combat of SWARM Aerial Vehicles

4

Omar Shadeed

MSc Student, Aero-Astro Eng.

Agile Flight Trajectory Tracker Design


Visual Information-based Navigation

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H. Didem Üzgün

PhD Candidate, Mechatron Eng.

GPS-denied and Visual Navigation

4

Erfan Roghani

PhD Student, Aero-Astro Eng.

Visual Depth Estimation and Visual Navigation

4

Uygar Yengin

PhD Student, Mechatron Eng.

Terrain Referecend Navigation

Undergraduate Research Opportunity Program (UROP)

I am always providing research opportunities for talented undergraduate students to elevate their careers toward academia and professional life. Contact me if you feel eligible.
Current UROP Fellows:

Ahmet Talha Çetin — Robust RL-based control

Oğuzhan Köse — Robust RL-based control

H. Nur Toprak — RL-based agile flight control

Muhammed Akcan — Visual Target Tracking 

Samet Kaplan — Visual Navigation

Industrial Partners and Sponsors

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stm
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Research and development projects

Current Projects


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Co-development with HAVELSAN

SRUS: SWARM of Agile Drones

We are partnering with HAVELSAN to develop agile drone swarms and carrier unmanned VTOL platforms, enabling broad UAV operations. ITU ARC fully designs and builds small-size drones with agile flight envelopes. These drones can navigate in GPS-denied environments using visual odometry and target tracking in addition to leader following. The unmanned VTOL platforms carry small-sized drones and provide tactical level information by utilizing their surveillance and SIGINT capabilities. The carrier platforms can also provide laser designation for coordinated operations. In this collaborative development, for VTOL platforms, the ITU ARC team provides controller, flight management, and navigation modules with their algorithms and avionics.

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Co-development with ASELSAN

AI-RADAR: ML-based Airspace Threat Detection

In this project, we aim to build AI-based real-time airspace monitoring and anomaly/threat detection models in order to provide safe air flight traffic. The model involves two layers, enabling to detect flight trajectory anomalies through processing individual flights and historical trajectory patterns. The individual flight trajectories are tracked through online flight performance clustering, allowing us to classify the aircraft types in the airspace. Historical flight trajectory bundles are identified for each flight operation, and the individual conflicts with these distinguished patterns are detected through artificial intelligence methodologies. The trained models capture not only the spatial patterns but also represent all dimensions of a flight trajectory, including tracks, velocity, heading, and their rates.

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Funded by SESAR H2020 Program

START: Resilient ATM Network Management

In this project, we do research on applying the epidemic spreading process to model the uncertainty propagation over air transportation network. To construct the parametric model of the network, we utilize the real flight data of the European air traffic flow. The physical parameters of the network extracted from the dataset are transformed into a parameter set of the epidemic model to simulate the propagation of delay. Then, we build a control mechanism the infection rates ensuring the stability of the meta-population network, which means to manage the traffic flow between the airports, where the problem is transformed into an optimization problem.

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Funded by SESAR H2020 Program

FACT: Urban Unmanned Aerial Vehicle Management

The project FACT will bring a much-needed update to CNS technology and build the bridge between future U-space services (expecting fully digital and highly automated) and conventional ATM systems considering both technological and users’ perspectives. The main overall objective(s) of the project FACT is to increase safety, security, efficiency, and robustness of future air traffic environment through the development of integrated CNS functional architecture supporting the use of a common performance-based approach for CNS functions addressing the needs of a large spectrum of airspace users across varied operational environments. It will address and bring benefits for both existing and new airspace users, such as drones or urban air mobility. 

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Advising to Turkish Aerospace Industry Co.

Robust Controller Design for Jet Fighter Aircraft

In this contract, the ITU ARC team supports TAI in developing a jet fighter aircraft's digital flight control system. First, we aim to build data-driven system identification models to capture the aircraft's uncertain parameters from the flight tests through machine learning algorithms. This will be a comparative test to be run parallel to classical system identification effort based on frequency sweep algorithms. The team will then support designing H-infinity robust controller for the aircraft, which remains robust against parametric uncertainties and stable upon frequent master/slave controller switchings. The successful algorithms will be tested with real flights with the test pilots of TAI.

 Past Projects


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Funded by Boeing

Flight Trajectory Wind Error Recovery

The project proposed to develop advanced algorithms to reduce uncertainty in weather forecast in trajectory prediction. The fundamental data set is the aircraft trajectories from the radar measurements that encapsulate information about the uncertain parameters such as wind vector. The aim was to build a probabilistic “wind map” to capture the wind estimation error and convert this sparse information into a complete wind map.

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Funded by SESAR H2020 Program

COPTRA: Combining Probable Trajectories

COPTRA's objective is detailed with three sub-objectives developed through three research work packages: a) define the concept of probabilistic trajectory prediction; b) define the probabilistic traffic concept and study how it can be constructed by combining probabilistic trajectories, using the probabilistic trajectory definition; and c) apply probabilistic traffic to Air Traffic Control (ATC) planning. 

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Co-development with STM Co.

HETS: Helicopter Terrain Avoidance Support System

In this project, we developed a safe flight navigation algorithm for helicopter TAWS avionics, which supports pilot with visual guidance for low visibility and close-to-terrain operations. Specifically, the algorithm use the performance models, monitors the potential terrain collisions and issues alerts with visual guidance to the pilot. Moreover, the algorithm tracks the divergence of flown trajectory from the predicted one due to lack of partial state measurement, and provides real-time parametric system identification and pilot intent prediction.

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Co-development with TÜBİTAK BİLGEM

Trajectory Prediction System for ATC WP

In this project, we developed a safe flight navigation algorithm, which provides helicopter pilot with visual guidance for low visibility and close-to-terrain operations. Specifically, the algorithm use the performance models, monitors the potential terrain collisions and issues alerts with visual guidance to the pilot. Moreover, the algorithm tracks the divergence of flown trajectory from the predicted one due to lack of partial state measurement, and provides real-time parametric system identification and pilot intent prediction.

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funded by SESAR WP-E

AUTOFLY-AID: A Smart Cockpit System

AUTOFLY-Aid aimed to develop and demonstrate novel automation support algorithms and tools to the flight crew for flight critical collision avoidance using “dynamic 4D trajectory management”. The automation support system is envisioned to improve the primary shortcomings of TCAS, and to aid the pilot through add-on avionics/head-up displays and reality augmentation devices in dynamically evolving collision avoidance scenarios. 

Teaching

In this course, we consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming, but their exact solution is computationally intractable. Then, we discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are collectively referred to as reinforcement learning, and also by alternative names such as approximate dynamic programming, and neurodynamic programming. Primary focus will be on approximation in value space. Here, the control at each state is obtained by limited lookahead with cost function approximation, i.e., by optimization of the cost over a limited horizon, plus an approximation of the optimal future cost, starting from the end of this horizon. The latter cost is a function of the state where we may be at the end of the horizon. It may be computed by a variety of methods, possibly involving simulation and/or some given or separately derived heuristic/suboptimal policy. The use of simulation often allows for model-free implementations that do not require the availability of a mathematical model, a major idea that has allowed the use of dynamic programming beyond its classical boundaries.

Next offer: Fall 2021

The goal of this course is to guide students to solve real aerospace robotics problems through decision making and artificial intelligence algorithms. In the first part of this course, students learn the basics of decision-making algorithms in addition to use of computer vision libraries (e.g., OpenCV) and robotic simulation tools (e.g., Gazebo). In the second part, students select and solve one of the famous robotics/artificial intelligence problems through hardware implementation or simulation tools. İTÜ Aerospace Research Center provides students some hardwares and VICON Motion Capture System. Some experience in programming with MATLAB, Python, etc. is essential, and embedded programming skill is preferred.

Next offer: Fall 2022

This undergraduate course aims to teach fundamentals of Flight Stability and Controls as to provide them with them basics that lead to analysis of flying and handling characteristics and successful design of automatic flight control systems. The course focuses on the following topics for the student gain expertise on the fundamentals of flight stability and controls: Basic definitions of flight mechanics, control and control surfaces. General structure of flight control systems. Aircraft static and dynamic stability and stability derivatives. Static longitudinal and lateral stability. Aircraft longitudinal and lateral dynamic equations. Nonlinear dynamic equations. Linearization of equations. Longitudinal and lateral transfer functions. Longitudinal modes of motion. Short and long period approximation. Transient response of aircraft dynamic. Basic concept of aircraft control systems. The types of autopilot. Autopilot design. Design of displacement autopilot by the Root Locus method. Inner and outer loop concepts. Pitch orientation control system. Root locus analysis. Acceleration control system. Matlab Simulink simulation of aircraft autopilots.

Next offer: Spring 2021

The course aims to provide solid understanding for the undergraduate students in the following topics: an introduction to analysis and design of feedback control systems, including classical control theory in the time and frequency domain; input/output modeling of dynamical systems using differential equations and transfer functions; stability and performance of interconnected systems, including use of block diagrams, Bode plots, the Nyquist criterion; design of feedback controllers in frequency domain based onstability, performance and robustness specifications.The purpose of the course is also to teach students to use MATLAB/SIMULINK tools for the modeling and controller design. 

Next offer: Spring 2021

The course aims to provide solid understanding for the undergraduate students in the connection of linear system, feedback and state-space based controller. The undergraduate student learns analyzing linear systems and designing of feedback control for the systems defined through state-space models. By the end of the class students should be able to i) define the key features of linear feedback control systems, ii) given an application problem (e.g. aerospace etc), decide if it should be formulated as a linear control system, iii) define the given problem formally (in terms of time and frequency), state what model and controller is best suited for addressing it, iv) implement in code common linear controller (as assessed by the homeworks) and describe multiple criteria for analyzing and evaluate controllers on performance and robustness.

Next offer: Fall 2021

Publications


For the full-text of the publications, please visit my ResearchGate or Google Scholar pages. If you are having trouble finding them, feel free to contact me.

to be appear in 2021

Hasanzade, M., Koyuncu, E., A Dynamically Feasible Fast Replanning Strategy with Deep Reinforcement Learning, Journal of Intelligent andRobotic Systems (Submitted)

Hasanzade, M., Shadeed, O., Koyuncu, E., Deep Reinforcement Learning based Aggressive Collision Avoidance with Limited FOV under Dynamic Constraints, IEEE International Conference on Robotics and Automation (ICRA), 30 May-5 June, 2021 (Submitted)

Aksoy, M., Ozdemir, O., Guner, G., Baspinar, B., Koyuncu, E., Flight Trajectory Pattern Generalization and Abnormal Flight Detection with Generative Adversarial Network, AIAA SciTech Forum, 11-15 January 2021

Shadeed, O., Hasanzade, M., Koyuncu, E., Deep Reinforcement Learning based Aggressive Flight Trajectory Tracker, AIAA SciTech Forum, 11-15, January 2021

2020

Baspinar, B., Koyuncu, E., Optimization-Based Autonomous Air Traffic Control for Airspace Capacity Improvement, IEEE Transactions on Aerospace and Electronic Systems, 2020

Roghani, E., Koyuncu, E., Monocular Depth Estimation with Artificial Neural Networks Trained on Synthetic RGB-D Data Sets, 7th InternationalConference on Control, Decision and Information Technologies (CoDIT), Prague, Czech Republic, June 29 - July 2, 2020

Baspinar, B., Koyuncu, E., Assessment of Aerial Combat Game via Optimization-Based Receding Horizon Control, IEEE Access, vol. 8, pp. 35853-35863, 2020, doi: 10.1109/ACCESS.2020.2974792.

Turkmen, H., Shadeed, O., Koyuncu, E., Trajectory-based Agile Multi UAV Coordination through Time Synchronisation, AIAA SciTech Forum,Orlando, Florida, 6-10 January 2020

2019

Baspinar, B., Balakrishnan H., Koyuncu, E., Mission Planning and Control of Multi-Aircraft Systems With Signal Temporal Logic Specifications, IEEE Access, vol. 7, pp. 155941-155950, 2019. doi: 10.1109/ACCESS.2019.2949426

Uzun, M, Demirezen, M. U., Koyuncu, E., Inalhan, G., Lopez J., and Vilaplana, M., Deep Learning Techniques for Improving Estimations of KeyParameters for Efficient Flight Planning, IEEE/AIAA Digital Avionics Systems Conference, 2019

Uzun M., Demirezen M., Koyuncu E., Inalhan G., Design of a Hybrid Digital-twin Flight Performance Model through Machine Learning, IEEEAerospace Conference, Big Sky, Montana, 2-9 March, 2019

Alizadeh A., Koyuncu E., Learning-based Aircraft Trajectory Planning Enhancement, AIAA Science and Technology Forum and Exposition (AIAA SciTech 2019), San Diego, California, 7-11 January 2019

Roghani E., Koyuncu E., Uzun, M., Trajectory Generation and Regeneration for Constrained Differentially Flat Control Systems, IEEE Aerospace Conference, Big Sky, Montana, Mar 2 - Mar 9, 2019

Humaira N., Koyuncu E., A Framework for Analysis of Combat Maneuvers Input Strategy using Energy-Based Metrics, AIAA Science and Technology Forum and Exposition (AIAA SciTech 2019), San Diego, California, 7-11 January 2019

Demirezen U., Koyuncu E. and et. all., A Simulation-Based Development and Verification Architecture for Micro UAV Teams and Swarms, AIAA Science and Technology Forum and Exposition (AIAA SciTech 2019), San Diego, California, 7-11 January 2019

Baspinar B., Koyuncu E., Differential Flatness-based Optimal Air Combat Maneuver Strategy Generation, AIAA Science and Technology Forum and Exposition (AIAA SciTech 2019), San Diego, California, 7-11 January 2019

Baspinar, B., Koyuncu, E., Evaluation of Two-vs-One Air Combats Using Hybrid Maneuver-Based Framework and Security Strategy Approach, Journal of Aeronautics and Space Technologies, v. 12-1, pg. 95-107, January 2019

2018

Baspinar, B., Koyuncu, E., Survivability based Optimal Air Combat Mission Planning with Reinforcement Learning, 2018 IEEE Conference onControl Technology and Applications (CCTA), Copenhagen, Denmark, August 21-24, 2018

Alizadeh, A., Uzun, M., and Koyuncu, E., Optimal Trajectory Planning based on Wind-Optimal Cost Index, 2018 Aviation Technology, Integration and Operations Conference, AIAA AVIATION Forum, Atlanta, GA, June 25-29, 2018

Casado, E., Civita, M. L., Uzun, M., Koyuncu, E., Inalhan, G., Estimated Time of Arrival Sensitivity to Aircraft Intent Uncertainty, 15th IFAC Sympo-sium on Control in Transportation Systems CTS 2018, Genova, Italy, June 6-8, 2018

Alizadeh, A., Uzun, M., Koyuncu, E., Inalhan, G., Optimal En-Route Trajectory Planning based on Wind Information, 15th IFAC Symposium onControl in Transportation Systems CTS 2018, Genova, Italy, June 6-8, 2018

Hasanzade, M., Herekoğlu, Ö., Yeniçeri, R., Koyuncu, E., İnalhan, G., RF Source Localization using Unmanned Aerial Vehicle with Particle Filter, The 9-th International Conference on Mechanical and Aerospace Engineering (ICMAE 2018), Budapest, Hungary, July 10-13, 2018

Baspinar, B., Koyuncu, E., Aerial Combat Simulation Environment for One-on-One Engagement, AIAA SciTech Forum and Exposition: Modelling and Simulation Technologies, Gaylord Palms, Kissimmee, FL, 8-12 January 2018

2017

Uzun M., Koyuncu E., Data-Driven Trajectory Uncertainty Quantification For Climbing Aircraft To Improve Ground-Based Trajectory Prediction, Journal of Science and Technology (AUJST-A), 2017

Baspinar B., Koyuncu E., Demand and Capacity Balancing Through Probabilistic Queuing Theory and Ground Holding Program for European Air Transportation Network, Journal of Science and Technology (AUJST-A), 2017

Baspinar B., Koyuncu E., Large Scale Data-Driven Delay Distribution Models of European Air Traffic Flow Network, Transportation Research Procedia, Elseiver, 2017

Baspinar, B., Koyuncu, E., Managing Air Transport Demand and Capacity via Stochastic Modelling Approach, 9th Ankara Int. Aerospace Conference, Ankara, September 2017

Baspinar, B., Uzun, M., Guven, A. F., Basturk, T., Tasdelen, I., Koyuncu, E., Inalhan, G., A 4D Trajectory Generation Infrastructure Tool for ControllerWorking Position, IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, FL, USA, 17-21 Sept. 2017

Yeniceri, R., Hasanzade, M., Koyuncu, E., İnalhan, G., Enabling Centralized UTM services through cellular network for VLL UAVs, Integrated Communications, Navigation and Surveillance Conference (ICNS), April 2017

Uzun, M., Başpınar, B., Koyuncu, E., İnalhan, G., Takeoff weight error recovery for tactical trajectory prediction automaton of air traffic control operator, IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, FL, USA, 17-21 Sept. 2017

Hasanzade, M., Herekoglu, O., Ure, N. K., Koyuncu, E., Yeniceri, R., Inalhan, G., Localization and tracking of RF emitting targets with multiple un-manned aerial vehicles in large scale environments with uncertain transmitter power, International Conference on Unmanned Aircraft Systems(ICUAS), Miami, FL, 13-16 June 2017

2016

Baspinar B., Koyuncu E., A Data-Driven Air Transportation Delay Propagation Model Using Epidemic Process Models, International Journal ofAerospace Engineering 2016:1-11

Koyuncu E., Uzun M., Inalhan, G., Cross-Entropy based Cost Efficient 4D Trajectory Generation for Airborne Conflict Resolution, The Inst. ofMechanical Eng., Part G, Jour. of Aerospace Engineering, SAGE, 2016

Pasaoglu C., Akcam N., Koyuncu E., Tarhan A. F., Inalhan G., Collaborative Intent Exchange Based Flight Management System with AirborneCollision Avoidance for UAS, Journal of Intelligent and Robotics Systems, Springer, 2016

Massimiliano Z., Koyuncu E., et al., Towards a secure trading of aviation CO₂ allowance, Journal of Air Traffic Management (JATM), Elseiver, 2016

Baspinar B., Ure N.K., Koyuncu E. and Inalhan G., Analysis of Delay Characteristics of European Air Traffic through a Data-Driven Airport-Centric Queuing Network Model, 14th IFAC Symposium on Control in Transportation Systems, May 18-20, 2016

Koyuncu E, Baspinar B., Guney G. et al, Implementing Dynamic Air Transport Slot Trading Through Secure Auction Mechanism, 7th InternationalConference on Research in Air Transportation, June 20-24, 2016

Baspinar B., Koyuncu E., Inalhan G., Large Scale Data-Driven Delay Distribution Model of European Air Traffic Flow Network, 19th Euro WorkingGroup on Transportation Meeting (EWGT 2016), 05-07 September 2016

2015

Karaman, S., Koyuncu E., Inalhan, G., Innovative Collaborative Task Allocation for UAVs, Handbook of Unmanned Aerial Vehicles, Ed. Kimon P. Valavanis and George J. Vachtsevanos, 2015

Guner G., Koyuncu E., et. al., Delay Analysis Platform Through Secure Information Sharing, Fifth SESAR Innovation Days, Bologna, Italy, 1 – 3December 2015

Uzun, M., Koyuncu, E., Inalhan, G., Flight Deck Centered Tactical 4D Trajectory Planning and Collision Avoidance with Flight Envelope Sampling, International Conference on Application and Theory of Automation in Command and Control Systems, Toulouse, France, September 30-October 2, 2015

before 2015

Massimiliano Z., KoyuncuE., et. al., Enabling the Aviation CO2 Allowance Trading Through Secure Market Mechanisms, Fourth SESAR Innovation Days, Madrid, Spain, 25th – 27th November 2014

Tarhan A. F., Koyuncu E., Hasanzade M., Ozdemir U., Inalhan G., Formal Intent Based Flight Management System Design for Unmanned AerialVehicles, International Conference on Unmanned Aircraft Systems (ICUAS’14),pp. 984-992 27-30, Orlando, FL, USA, 2014

Uzun M., Guner G., Koyuncu E., Inalhan G., Integrated Flight Deck Testbed with Next Generation Visual Decision Support Tools, International Conference on Research in Air Transportation (ICRAT’14), May 26-30, Istanbul, 2014

Koyuncu E., Inalhan, G., Exploiting Delayed and Imperfect Information for Generating Approximate UAV Target Interception Strategy, Journal ofIntelligent and Robotics Systems, DOI: 10.1007/s10846-012-9693-6, Springer, 2012

Karakas, H., Koyuncu E., Inalhan, G., ITU Tailless UAV Design, Journal of Intelligent and Robotics Systems, DOI: 10.1007/s10846-012-9695-4,Springer, 2012

Koyuncu E., Ure, N. K., Inalhan, G., Integration of Path/Maneuver Planning in Complex Environments for Agile Maneuvering UCAVs, Journal ofIntelligent and Robotics Systems, DOI: 10.1007/s10846-009-9367-1, Springer, January 2010

Koyuncu, E., Inalhan, G., Dynamically Feasible Probabilistic Motion Planning in Complex Environments for UAVs, Robotics 2010; Current andFuture Challenges, ed. Abdellatif H., ISBN 978-953-7619-78-7, Publishing date: February 2010

Koyuncu, E., Garcia, E., and Inalhan, G., AUTOFLY-Aid: Multi-Modal Trajectory Projection Approach for Airborne Collision Detection and Avoidance, International Conference on Application and Theory of Automation in Command and Control Systems, 28-30 May, Naples, 2013

Koyuncu, E., Tokadli, G., Bahcivan, Z., Inalhan, G., Design of a Pilot-Centered Visual Decision-Support System for Airborne Collision Avoidance, 31st Digital Avionics Systems Conference (DASC’12), Williamsburg, VA, October 14-18, 2012

Koyuncu, E., Garcia, E., and Inalhan, G., Flight Deck Automation Support with Dynamic 4D Trajectory Management for ACAS: AUTOFLY-AID, Integrated Communications Navigation and Surveillance (ICNS) Conference, pp. C4-1 - C4-9, Herndon, VA, 2012

Koyuncu, E., Garcia, E., and Inalhan, G., AUTOFLY-Aid: Flight Deck Automation Support with Dynamic 4D Trajectory Management for Responsive and Adaptive Airborne Collision Avoidance, International Conference on Application and Theory of Automation in Command and Control Systems, 29-31 May, London, 2012

Eren, U., Baskaya, E., Koyuncu, E., Cihan, M., Akay, C., Inalhan, G., Design of a Flexible Nanosatellite Bus System for ITUpSAT II, InternationalConference on Small Satellites 2012, ISSN 0183-0570

Baskaya, E., Eren, U., Koyuncu, E., Cihan, M., Akay, C., Inalhan, G., A Precise ADCS Design for ITUpSAT II, International Conference on SmallSatellites, 2012, ISSN 0183-0570

Baskaya, E., Eren, U., Koyuncu E., Inalhan, G., Design and Development of a Reliable ADCS and Indigenous Bus Architecture for Nanosatellites:ITUpSAT II, 63rd International Astronautical Congress, Naples, ITALY, October 1-5, 2012

Koyuncu, E., Karaman, S., Frazzoli, E., and Inalhan, G., Probabilistic 4D Conflict Detection and Avoidance Strategy in En-Route Air Traffic, Inter-national Conference on Application and Theory of Automation in Command and Control Systems, 26-27 May, Barcelona, Spain, 2011

Bahcivan, Z., Koyuncu, E., Aydin, S. S., Cinar, E., Cavcar, A., and Inalhan, G., ITU/AU Air Traffic Control Network Simulator for Design, Developmentand Testing of Automated ATM Systems, International Conference on Application and Theory of Automation in Command and Control Systems,26-27 May, Barcelona, Spain, 2011

Koyuncu, E., and et. all, ITU-pSATII: High-precision Nanosatellite ADCS Development Project, 5th International Conference on Recent Advancesin Space Technologies, 9-11 June, Istanbul, Turkey, 2011

Inalhan, G., Koyuncu, E., et. all, Design and Development of ITU pSAT II: On orbit demonstration of a high¬precision ADCS for nanosatellites, 8th International ESA Conference on Guidance and Navigation Control Systems, 5-10 June, Carlsbad, Czech Republic, 2011

Koyuncu, E., Inalhan, G., A Probabilistic B-Spline Motion Planning Algorithm for Unmanned Helicopters Flying in Dense 3D Environments, Int.Conf. Intelligent Robots and Systems (IROS’08),Nice, France, September 2008

Koyuncu, E., Ure, N.K., Inalhan, G., A Probabilistic Algorithm for Mode Based Motion Planning of Agile Air Vehicles in Complex Environments, Int. Federation of Automatic Control World Congress (IFAC WC’08),Seoul, South Korea, June 2008