Browsing by Author "Hasanzade, Mehmet"

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    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, Ana
    Advanced 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.
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    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, Ana
    The 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).
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    Explainability of AI-driven air combat agent
    (IEEE, 2023-08-02) Saldiran, Emre; Hasanzade, Mehmet; Inalhan, Gokhan; Tsourdos, Antonios
    In 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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    Towards global explainability of artificial intelligence agent tactics in close air combat
    (MDPI, 2024-05-21) Saldiran, Emre; Hasanzade, Mehmet; Inalhan, Gokhan; Tsourdos, Antonios
    In this paper, we explore the development of an explainability system for air combat agents trained with reinforcement learning, thus addressing a crucial need in the dynamic and complex realm of air combat. The safety-critical nature of air combat demands not only improved performance but also a deep understanding of artificial intelligence (AI) decision-making processes. Although AI has been applied significantly to air combat, a gap remains in comprehensively explaining an AI agent’s decisions, which is essential for their effective integration and for fostering trust in their actions. Our research involves the creation of an explainability system tailored for agents trained in an air combat environment. Using reinforcement learning, combined with a reward decomposition approach, the system clarifies the agent’s decision making in various tactical situations. This transparency allows for a nuanced understanding of the agent’s behavior, thereby uncovering their strategic preferences and operational patterns. The findings reveal that our system effectively identifies the strengths and weaknesses of an agent’s tactics in different air combat scenarios. This knowledge is essential for debugging and refining the agent’s performance and to ensure that AI agents operate optimally within their intended contexts. The insights gained from our study highlight the crucial role of explainability in improving the integration of AI technologies within air combat systems, thus facilitating more informed tactical decisions and potential advancements in air combat strategies.