Browsing by Author "Inalhan, Gokhan"
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Item Open Access Adaptive UAV swarm mission planning by temporal difference learning(IEEE, 2021-11-15) Gopalakrishnan, Shreevanth Krishnaa; Al-Rubaye, Saba; Inalhan, GokhanThe prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.Item Open Access AI-based multifidelity surrogate models to develop next generation modular UCAVs(AIAA, 2023-01-19) Karali, Hasan; Inalhan, Gokhan; Tsourdos, AntoniosThe next generation low-cost modular unmanned combat aerial vehicles (UCAVs) provide the opportunity to implement innovative solutions to complex tasks, while also bringing new challenges in design, production, and certification subjects. Solving these problems with tools that provide fast modeling in line with the digital twin concept is possible. In this work, we develop an artificial intelligence (AI) based multifidelity surrogate model to determine performance parameters of innovative modular UCAVs. First, we develop a data generation algorithm that includes a high-fidelity model based on computational fluid dynamics methods and a low-fidelity model based on computational aerodynamic approaches. In the next step, a new transfer learning-based surrogate model is generated using multifidelity data. Thanks to this approach, the developed AI model more accurately predicted the flow conditions that were missing in the high-fidelity data with the data obtained from the low-order model. The performance of the proposed AI-based surrogate model is to be investigated in terms of accuracy, robustness, and computational cost using a generic modular UCAV configuration.Item Open Access AI-driven multidisciplinary conceptual design of unmanned aerial vehicles(AIAA, 2024-01-04) Karali, Hasan; Inalhan, Gokhan; Tsourdos, AntoniosThis paper presents a multidisciplinary conceptual design framework for unmanned aerial vehicles based on artificial intelligence-driven analysis models. This approach leverages AI- driven analysis models that include aerodynamics, structural mass, and radar cross-section predictions to bring quantitative data to the initial design stage, enabling the selection of the most appropriate configuration from various optimized concept designs. Due to the design optimization cycle, the initial dimensions of key components such as the wing, tail, and fuselage are provided more accurately for later design activities. Simultaneously, the generated structure enables more suitable design point selection through the feedback loop within the design iteration. Therefore, in addition to reducing design costs, this approach also offers a substantial time advantage in the overall design process.Item Open Access AI-driven unmanned aerial system conceptual design with configuration selection(IEEE, 2023-08-02) Karali, Hasan; Inalhan, Gokhan; Tsourdos, AntoniosThis paper presents an intelligent conceptual design framework for the configuration selection of aerial vehicles. In this approach, the quantitative data is brought to the earliest stage of design utilizing AI-driven analysis models and it allows to choose the most suitable one among the possible configurations. Thanks to the design optimization cycle, the initial dimensions of the main components such as the wing, tail and fuselage are more accurately provided for later design activities. At the same time, the generated structure provides a more appropriate design point selection thanks to the feedback loop in design iteration. Thus, while reducing the design cost, a significant time advantage is also provided in the design process. The paper presents a generic use case based on a high-performance combat UAV design study to demonstrate the abilities of the proposed model.Item Open Access AI-enabled interference mitigation for autonomous aerial vehicles in urban 5G networks(MDPI, 2023-10-13) Warrier, Anirudh; Al-Rubaye, Saba; Inalhan, Gokhan; Tsourdos, AntoniosIntegrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field.Item Open Access AMU-LED Cranfield flight trials for demonstrating the advanced air mobility concept(MDPI, 2023-08-31) Altun, Arinc Tutku; Hasanzade, Mehmet; Saldiran, Emre; Guner, Guney; Uzun, Mevlut; Fremond, Rodolphe; Tang, Yiwen; Bhundoo, Prithiviraj; Su, Yu; Xu, Yan; Inalhan, Gokhan; Hardt, Michael W.; Fransoy, Alejandro; Modha, Ajay; Tena, Jose Antonio; Nieto, Cesar; Vilaplana, Miguel; Tojal, Marta; Gordo, Victor; Menendez, Pablo; Gonzalez, AnaAdvanced Air Mobility (AAM) is a concept that is expected to transform the current air transportation system and provide more flexibility, agility, and accessibility by extending the operations to urban environments. This study focuses on flight test, integration, and analysis considerations for the feasibility of the future AAM concept and showcases the outputs of the Air Mobility Urban-Large Experimental Demonstration (AMU-LED) project demonstrations at Cranfield University. The purpose of the Cranfield demonstrations is to explore the integrated decentralized architecture of the AAM concept with layered airspace structure through various use cases within a co-simulation environment consisting of real and simulated standard-performing vehicle (SPV) and high-performing vehicle (HPV) flights, manned, and general aviation flights. Throughout the real and simulated flights, advanced U-space services are demonstrated and contingency management activities, including emergency operations and landing, are tested within the developed co-simulation environment. Moreover, flight tests are verified and validated through key performance indicator analysis, along with a social acceptance study. Future recommendations on relevant industrial and regulative activities are provided.Item Open Access Analyzing fragility of the advanced air mobility system and exploring antifragile networks(IEEE, 2023-11-10) Altun, Arinc Tutku; Xu, Yan; Inalhan, Gokhan; Hardt, Michael W.Future Advanced Air Mobility (AAM) is a concept that envisions to transform the current air transportation system into a more agile, flexible, and accessible system. Yet, the considered transformation and integrated system is not easy to achieve since it involves providing a high level of safety as well as efficiency. For that purpose, in this paper, we explored the fragility and antifragility concepts to analyze the AAM traffic network and provide an understanding of a system where it can benefit even under adverse conditions such as contingency events. For the analysis, first, a complex AAM traffic network is built via various AAM vehicles and possible vertiport locations that are analyzed for the Northern California area. After that, the AAM network is modeled via queue theory to simulate the considered flight plans, obtain the actual departure and arrival times under different conditions, and observe the delay propagation. Then, metrics from network theory based on targeted node and edge removals are studied to analyze the fragility of the AAM network and used for antifragility analysis. The methodology is used to analyze different disruptive cases over an AAM network such that disruptions at vertiports and over origin-destination pairs. Finally, an analysis of making the considered traffic antifragile through flight cancellations and its trade-off based on flight cancellation costs is provided.Item Open Access A comprehensive flight plan risk assessment and optimization method considering air and ground risk of UAM(IEEE, 2022-10-31) Su, Yu; Xu, Yan; Inalhan, GokhanInspired by risk analysis assistance service and flight plan preparation / optimization service in U-space service, this paper investigates a flight plan risk assessment and optimization method for future urban air mobility. The quantitative risk assessment of the flight plan is divided into two parts: the ground and air risks of the flight plan. After evaluating the risk of the flight plan, optimization suggestions are given to guide the path planning algorithm to optimize the flight plan at low risk. The quantitative risk assessment of the flight plan corresponds to risk analysis assistance service in U-space service, and the procedure to give optimization suggestions correspond to flight plan preparation / optimization service in U-space service. This paper selects the task scenario of logistics drone cargo transportation and carries out risk assessment on the specific flight plan. From the assessment results, when the flight plan crosses the pedestrian intensive area on the ground or the road with high-speed vehicles, the risk value of the corresponding flight plan segment increases significantly. When the flight plan segment approaches the area near the airport or intersects with other UAM participants with the same mission time window, the corresponding risk value is also high. After obtaining the risk assessment results, the targeted optimization suggestions are given to guide the path planning algorithm to optimize the flight plan at low risk. The risk of the optimized flight plan has been significantly reduced.Item Open Access Comprehensive risk assessment and utilization for contingency management of future AAM system(AIAA, 2023-06-08) Altun, Arinc Tutku; Xu, Yan; Inalhan, Gokhan; Hardt, Michael W.This paper presents a risk assessment methodology to be used in the future Advanced Air Mobility (AAM) systems especially for supporting the planning phase and onboard contingency management solutions. Two types of dynamic risk maps are introduced as Contingency Risk Map that includes the probability of observing a contingency onboard and Risk Severity Map which covers various sources of data such as population density, a dense air traffic, obstacles, terrain, no-fly zones, and so forth. Contingency Risk Map is to quantify the probability of having a contingency and decide if the quantified probability is above the threshold. If the contingency risk probability is at unacceptable limit, Risk Severity Map assists to select a pre-defined secure emergency landing zone or non-secure emergency landing zone defined onboard. The developed risk assessment structure is tested through two different use cases. First one is about defining locations as vertiport alternatives based on the generated map, in case of a contingency ending up with an AAM vehicle to do emergency landing. Second case considers minimum risk onboard rerouting of an AAM vehicle to a secure/non-secure emergency landing zone under contingency management process. The main objective of this work is to build a system-wide contingency management concept for the AAM system by supporting with UTM services such as risk analysis assistance.Item Open Access Conflict probability based strategic conflict resolution for UAS traffic management(IEEE, 2023-11-10) Tang, Yiwen; Xu, Yan; Inalhan, GokhanIn this paper, we present a strategic conflict resolution method based on the conflict probability estimation, in the context of Unmanned Aircraft System (UAS) Traffic Management. We first elaborate a classic approach for flight trajectory generation in a designated realistic airspace environment, which is then smoothed by B-spline algorithm to achieve higher realism. The trajectories are extended to 4-dimensional Operational Volumes (OV) following the current UTM development visions. This forms the basis for performing a coarse conflict screening process, as the initial part for conflict detection, primarily based on identifying any OVs overlapping in temporal and spatial. Next, we look into the captured OVs and apply a well-studied conflict probability estimation approach, which contributes to a refined and more accurate conflict detection outcome. To resolve the potential conflicts, we propose two models including First-Come, First-Served (FCFS) and optimisation, both embedded with the probability-based conflict detection. In the FCFS approach, flights are delayed in the order of their submission, while the optimisation model aims at cherry-picking flights to seek the optimal solution. Numerical experiments with various case studies are performed to assess the effects with and without such probability concern, as well as different implementation strategies in real world. Results suggest that, allowing OVs’ overlapping to some extent does not necessarily incur conflict over an acceptable probability, whereas the efficiency of airspace use could be improved.Item Open Access Contingency management concept generation for U-space system(IEEE, 2022-05-12) Altun, Arinc Tutku; Xu, Yan; Inalhan, Gokhan; Vidal-Franco, Ignacio; Hardt, MichaelContingency management in aviation is a vital concept that ensures safety, security, and efficiency in operations. To fully benefit from the envisioned Advanced Air Mobility system, the need of a structured and system-wide contingency planning will be even more important since the air transportation system paradigm will shift into a highly automated system that includes high-density traffic. The complexity will increase considerably by enlarging the operations to the underserved urban areas. Therefore, the new system needs to provide a more agile, accessible, and flexible environment. In this paper, the need of a contingency management from a holistic approach is described and the base requirements to build such a system are defined by considering the roles and responsibilities of each stakeholder that are defined for the U-space system. Alongside the defined requirements, the tasks of the stakeholders and the expected main contingency sources are explained to have a better understanding of the system. The objective of this work is to provide the base guidelines that help to set appropriate actions by relevant stakeholder under an adverse condition which might compromise the safety and security of the operations within the air traffic network.Item Open Access Cooperative planning for an unmanned combat aerial vehicle fleet using reinforcement learning(American Society of Mechanical Engineers, 2021-07-07) Yuksek, Burak; Demirezen, Mustafa Umut; Inalhan, Gokhan; Tsourdos, AntoniosIn this study, reinforcement learning (RL)-based centralized path planning is performed for an unmanned combat aerial vehicle (UCAV) fleet in a human-made hostile environment. The proposed method provides a novel approach in which closing speed and approximate time-to-go terms are used in the reward function to obtain cooperative motion while ensuring no-fly-zones (NFZs) and time-of-arrival constraints. Proximal policy optimization (PPO) algorithm is used in the training phase of the RL agent. System performance is evaluated in two different cases. In case 1, the warfare environment contains only the target area, and simultaneous arrival is desired to obtain the saturated attack effect. In case 2, the warfare environment contains NFZs in addition to the target area and the standard saturated attack and collision avoidance requirements. Particle swarm optimization (PSO)-based cooperative path planning algorithm is implemented as the baseline method, and it is compared with the proposed algorithm in terms of execution time and developed performance metrics. Monte Carlo simulation studies are performed to evaluate the system performance. According to the simulation results, the proposed system is able to generate feasible flight paths in real-time while considering the physical and operational constraints such as acceleration limits, NFZ restrictions, simultaneous arrival, and collision avoidance requirements. In that respect, the approach provides a novel and computationally efficient method for solving the large-scale cooperative path planning for UCAV fleets.Item Open Access Data-driven synthetic air data estimation system development for a fighter aircraft(AIAA, 2023-06-08) Karali, Hasan; Uzun, Mevlut; Yuksek, Burak; Inalhan, GokhanIn this paper, we propose an AI-based methodology for estimating angle-of-attack and angle-of-sideslip without the need for traditional vanes and pitot-static systems. Our approach involves developing a custom neural-network model to represent the input-output relationship between air data and measurements from various sensors such as inertial measurement units. To generate the training data required for the neural network, we use a 6-degrees-of-freedom F-16 simulator, which is further modified to simulate more realistic flight data. The training data covers the full flight envelope, allowing the neural network to generate accurate predictions in all feasible flight conditions. Our methodology achieves high-accuracy estimations of angle-of-attack and angle-of-sideslip, with mean absolute errors of 0.534 deg and 0.247 deg, respectively, during the test phase. The results demonstrate the potential of the proposed methodology to accurately estimate important flight parameters without the need for complex and costly instrumentation systems. The proposed methodology could have significant practical applications in the aviation industry, particularly in next-generation aircraft instrumentation and control. Future research could focus on further refining the neural-network model and exploring its application in other aircraft systems to improve safety and reduce costs.Item Open Access Demonstrating advanced U-space services for urban air mobility in a co-simulation environment(2022-10-08) Fremond, Rodolphe; Tang, Yiwen; Bhundoo, Prithiviraj; Su, Yu; Tutku, Arinc; Xu, Yan; Inalhan, GokhanThe present paper formalises the development of a co-simulation environment aimed at demonstrating a number of advanced U-space services for the Air Mobility Urban - Large Experimental Demonstrations (AMU-LED) project. The environment has a visionary build that addresses Urban Air Mobility (UAM) challenges to support the High/Standard Performance Vehicles (HPV/SPV) operations within a complex urban environment by proposing an integrated solution that packages advanced services from the pre-flight to the in-flight phase in line with ongoing UAM Concept of Operations (ConOps). This setup opts for a holistic approach by promoting intelligent algorithmic design, artificial intelligence, robust serviceability through either virtual and live elements, and strong cooperation between the different services integrated, in addition to sustain interoperability with external U-space Service providers (USSP), Common Information Service providers (CISPs), and Air Traffic Controllers. The prototype has been recently showcased through the AMU-LED Cranfield (UK) demonstration activities.Item Open Access Design of a deep learning based nonlinear aerodynamic surrogate model for UAVs(AIAA, 2020-01-05) Karali, Hasan; Demirezen, Mustafa U.; Yukselen, Mahmut A.; Inalhan, GokhanIn this paper, we present a deep learning based surrogate model to determine non-linear aerodynamic characteristics of UAVs. The main advantage of this model is that it can predict the aerodynamic properties of the configurations very quickly by using only geometric configuration parameters without the need for any special input data or pre-process phase. This provides a crucial and explicit design and synthesis tool for mini and small UAVs. To achieve this goal, a large data set, which includes thousands of wing-tail configurations geometry parameters and performance coefficients, was generated using the previously developed and computationally very efficient non-linear lifting line method. This data is used for training the artificial neural network model. The preliminary results show that the neural network model has generalization capability. The aerodynamic model predictions show almost 1-1 coincidence with the numerical data even for configurations with different 2D profiles that are not used in model training. Specifically, the results of test cases are found to capture both the linear and non-linear region of the lift curves, by predicting the maximum lift coefficient, the stall angle of attack, and the characteristics of post-stall region correctly. Similarly, total drag and pitching moment coefficients are predicted successfully. The developed methodology provides the basis for bidirectional design optimization and offers insight for an inverse tool that can calculate geometry parameters for a given design condition.Item Open Access The development of an advanced air mobility flight testing and simulation infrastructure(MDPI, 2023-08-17) Altun, Arinc Tutku; Hasanzade, Mehmet; Saldiran, Emre; Guner, Guney; Uzun, Mevlut; Fremond, Rodolphe; Tang, Yiwen; Bhundoo, Prithiviraj; Su, Yu; Xu, Yan; Inalhan, Gokhan; Hardt, Michael W.; Fransoy, Alejandro; Modha, Ajay; Tena, Jose Antonio; Nieto, Cesar; Vilaplana, Miguel; Tojal, Marta; Gordo, Victor; Mendendez, Pablo; Gonzalez, AnaThe emerging field of Advanced Air Mobility (AAM) holds great promise for revolutionizing transportation by enabling the efficient, safe, and sustainable movement of people and goods in urban and regional environments. AAM encompasses a wide range of electric vertical take-off and landing (eVTOL) aircraft and infrastructure that support their operations. In this work, we first present a new airspace structure by considering different layers for standard-performing vehicles (SPVs) and high-performing vehicles (HPVs), new AAM services for accommodating such a structure, and a holistic contingency management concept for a safe and efficient traffic environment. We then identify the requirements and development process of a testing and simulation infrastructure for AAM demonstrations, which specifically aim to explore the decentralized architecture of the proposed concept and its use cases. To demonstrate the full capability of AAM, we develop an infrastructure that includes advanced U-space services, real and simulated platforms that are suitable for future AAM use cases such as air cargo delivery and air taxi operations, and a co-simulation environment that allows all of the AAM elements to interact with each other in harmony. The considered infrastructure is envisioned to be used in AAM integration-related efforts, especially those focusing on U-space service deployment over a complex traffic environment and those analyzing the interaction between the operator, the U-space service provider (USSP), and the air traffic controller (ATC).Item Open Access Development of reinforcement learning based mission planning method for active off-board decoys on naval platforms(AIAA, 2021-12-29) Bildik, Enver; Yuksek, Burak; Tsourdos, Antonios; Inalhan, GokhanIn this paper, a reinforcement learning-based decoy deployment strategy is proposed to protect naval platforms against radar seeker-equipped anti-ship missiles. The decoy system consists of a rotary-wing unmanned aerial vehicle (UAV) and an integrated onboard jammer. This decoy concept enables agility which is quite critical for jamming operations against a high-speed anti-ship missile. There are two main purposes of the developed jamming strategy; a) flying in the field of view of the anti-ship missile to conceal the naval platform, and b) flying away from the target ship to increase the miss distance between the anti-ship missile and naval platform. Here, it is aimed to meet these requirements simultaneously. Kinematics models are used to represent missile, decoy UAV, and target motion. Jammer and seeker signal strengths are modeled and the radar-cross section of a frigate is utilized to increase the realism of the simulation environment. Deep Deterministic Policy Gradient (DDPG) algorithm is applied to train an actor-critic agent which maps the observation parameters to decoy’s lateral acceleration. A heuristic way is chosen to create an appropriate reward function to solve the decoy guidance problem. Finally, simulations studies are performed to evaluate the system performance.Item Open Access Development of UCAV fleet autonomy by reinforcement learning in a wargame simulation environment(AIAA, 2021-01-04) Yuksek, Burak; Demirezen, Umut M.; Inalhan, GokhanIn this study, we develop a machine learning based fleet autonomy for Unmanned Combat Aerial Vehicles (UCAVs) utilizing a synthetic simulation-based wargame environment. Aircraft survivability is modeled as Markov processes. Mission success metrics are developed to introduce collision avoidance and survival probability of the fleet. Flight path planning is performed utilizing the proximal policy optimization (PPO) based reinforcement learning method to obtain attack patterns with a multi-objective mission success criteria corresponding to the mission success metrics. Performance of the proposed system is evaluated by utilizing the Monte Carlo analysis in which a wider initial position interval is used when compared to the defined interval in the training phase. This provides a preliminary insight about the generalization ability of the RL agentItem Open Access Enabling UAVs night-time navigation through mutual information-based matching of event-generated images(IEEE, 2023-11-10) Escudero, Naiara; Hardt, Michael W.; Inalhan, GokhanAdvanced Air Mobility is expected to revolutionize the future of general transportation. However, to make it a reality, significant challenges arise requiring technologies to ensure the expected attributes in these scenarios: resilience, robustness, large operational range, high accuracy, low SWaP equipment, and real-time processing. Although existing visual-based navigation solutions for aerial applications provide outstanding results under nominal conditions, their performance is highly constrained by the lighting conditions, making them infeasible for real operations. With the main focus of addressing this limitation, and expanding the current operational range to include extreme low-illuminated environments, this paper presents a solution which leverages one of the most powerful properties of event cameras: their high dynamic range. Thus, data provided by an event camera (also called dynamic vision sensor) is used to estimate the relative displacement of a flying vehicle during night-time conditions. To that end, two different threads running in parallel have been developed: a reference map generator, operating at low frequency, focused on reconstructing a 2-D map of the environment, and a localization thread, which matches, at high frequency, real-time event-generated images against the reference map by applying Mutual Information to estimate the aircraft’s relative displacement.Item Open Access Explainability of AI-driven air combat agent(IEEE, 2023-08-02) Saldiran, Emre; Hasanzade, Mehmet; Inalhan, Gokhan; Tsourdos, AntoniosIn safety-critical applications, it is crucial to verify and certify the decisions made by AI-driven Autonomous Systems (ASs). However, the black-box nature of neural networks used in these systems often makes it challenging to achieve this. The explainability of these systems can help with the verification and certification process, which will speed up their deployment in safety-critical applications. This study investigates the explainability of AI-driven air combat agents via semantically grouped reward decomposition. The paper presents two use cases to demonstrate how this approach can help AI and non-AI experts to evaluate and debug the behavior of RL agents.
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