Yazar "Ebrahimi, Benyamin" seçeneğine göre listele

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    Öğe
    A unified and experimentally validated design framework for long-endurance solar UAVS using model-based multi-objective multidisciplinary optimization
    (Springer, 2025) Khaneghaei, Mohammad; Asadi, Davood; Ebrahimi, Benyamin; Hazeri, Majid; Farsadi, Touraj; Nabavi Chashmi, Yaser; Durhasan, Tahir
    Designing long-endurance, solar-powered unmanned aerial vehicles (UAVs) requires careful coordination across aerodynamic, structural, and energy subsystems, particularly when targeting flexible, high-aspect-ratio configurations. This paper presents a mission-driven design and optimization framework for solar-powered long-endurance UAVs, tailored to post-disaster urban surveillance scenarios. A modular, multidisciplinary approach is adopted to account for the coupled effects of structural deformation and solar energy availability, both of which critically affect flight endurance. A key feature of the framework is the simultaneous integration of aeroelastic constraints and a time-dependent solar power and battery model, capturing realistic energy generation and storage behavior over diurnal cycles. This energy model is experimentally validated using a custom-built testbed and incorporated directly into the design loop. The framework is implemented using a Multidisciplinary Design Optimization (MDO) architecture that employs a coupling strategy to effectively manage interdependencies among subsystems. A comprehensive sensitivity analysis using Latin Hypercube Sampling highlights key performance-driving parameters. The final UAV design is fabricated and flight-tested, demonstrating the satisfaction of mission-level requirements derived from a simulated post-earthquake damage assessment in Adana, T & uuml;rkiye. Battery state-of-charge, trajectory, and attitude data collected during flight tests demonstrate that the UAV operates in accordance with design predictions, despite environmental variability. The study highlights how the integration of validated subsystem models within an established optimization process can lead to reliable, application-specific solar UAV designs suitable for real-world deployment.
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    Öğe
    Bi-level Voronoi strategy for cooperative search and coverage
    (Elsevier, 2025) Ebrahimi, Benyamin; Bataleblu, Ali Asghar; Roshanian, Jafar
    In this paper, a bi-level Voronoi-based path planning strategy is proposed to address the challenge of cooperative multi-agent search and coverage in uncertain environments. While traditional Voronoi-based coverage control is commonly utilized for optimal path planning, its limitations, such as agents' premature convergence to Voronoi centroids, leading to reduced exploration and lack of incentive to move, can hinder system efficiency. The proposed bi-level strategy provides a framework to overcome such limitations while ensuring a more balanced and adaptive allocation of the environment among agents, thereby enhancing overall performance in terms of environmental mean uncertainty reduction and target detection. This framework utilizes a primary Voronoi diagram based on agent positions for initial spatial partitioning. To enhance exploration efficiency, a secondary Voronoi tessellation is applied, integrating probabilistic information about the target's existence. The bi-level framework enables agents to achieve purposeful coverage by employing an efficient Voronoi partition allocation that integrates both the agents' positions and the probability of target existence. To this end, a novel allocation approach is employed to assign Voronoi neighbors to agents, ensuring that common cells within each agent's region are allocated to the most deserved agent. This mechanism promotes proportional contributions to uncertainty reduction, ensuring that each agent prioritizes areas of higher uncertainty or greater target likelihood. By doing so, agents operate efficiently, effectively reducing environmental uncertainty and improving target detection. Simulation results and comparative analyses validate the proposed strategy, demonstrating its superiority over conventional methods and highlighting its significance in multi-agent cooperative missions.