SH
S. Hwang
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Osprey Simulator
A Simulator Framework for Fixed-Wing UAV Updraft Localization
he real-world application of Micro Air Vehicles (MAVs) is often constrained by their limited flight range and endurance, primarily due to battery limitations. One way to overcome this challenge is by leveraging naturally occurring vertical air currents, a technique inspired by soaring birds. This paper introduces Osprey Simulator, a framework designed to simulate and test energy-efficient soaring flight strategies for fixed-wing drones. Built on Isaac Sim, Osprey Simulator enables the creation of both randomly generated urban environments and real-world environments, such as the TU Delft campus. These environments are paired with accurate wind field simulations using OpenFOAM, providing a robust platform for studying aerodynamic interactions in diverse settings. This functionality allows for the generation of scalable synthetic datasets, including depth images and wind field information, enabling comprehensive exploration of soaring potential across various structural geometries and wind conditions. Using generated synthetic data, a neural network was trained to predict optimal soaring regions by analyzing depth images and wind field information. The network demonstrates the ability to identify updraft and downdraft regions, enabling more efficient path planning for drones in urban environments. By integrating realistic simulations and advanced predictive models, Osprey Simulator serves as a powerful tool for advancing autonomous soaring and extending the operational range of fixed-wing MAVs.
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he real-world application of Micro Air Vehicles (MAVs) is often constrained by their limited flight range and endurance, primarily due to battery limitations. One way to overcome this challenge is by leveraging naturally occurring vertical air currents, a technique inspired by soaring birds. This paper introduces Osprey Simulator, a framework designed to simulate and test energy-efficient soaring flight strategies for fixed-wing drones. Built on Isaac Sim, Osprey Simulator enables the creation of both randomly generated urban environments and real-world environments, such as the TU Delft campus. These environments are paired with accurate wind field simulations using OpenFOAM, providing a robust platform for studying aerodynamic interactions in diverse settings. This functionality allows for the generation of scalable synthetic datasets, including depth images and wind field information, enabling comprehensive exploration of soaring potential across various structural geometries and wind conditions. Using generated synthetic data, a neural network was trained to predict optimal soaring regions by analyzing depth images and wind field information. The network demonstrates the ability to identify updraft and downdraft regions, enabling more efficient path planning for drones in urban environments. By integrating realistic simulations and advanced predictive models, Osprey Simulator serves as a powerful tool for advancing autonomous soaring and extending the operational range of fixed-wing MAVs.
Prolonging the endurance of fixed-wing UAVs is crucial for achieving complex missions, yet their limited battery life poses a significant challenge. In response, this research proposes a novel approach to extend the endurance of fixed-wing UAVs by enabling autonomous soaring in an orographic wind field. The goal of our research is to develop a controller that can identify feasible soaring regions and autonomously maintain position control without using any throttle. Soaring flight is desirable as it results in a low energy cost with zero throttle usage. However, without throttle usage, the longitudinal motion of the UAV is an under-actuated system, presenting control challenges. The concept of a target gradient line (TGL) is introduced as part of the control algorithm that addresses these challenges and autonomously finds the equilibrium soaring position where sink rate and updraft are in equilibrium. Experimental tests showed promising results, demonstrating the controller’s effectiveness in maintaining autonomous soaring flight in a non-static wind field. We also demonstrate a single degree of control freedom in the soaring position through manipulation of the TGL.
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Prolonging the endurance of fixed-wing UAVs is crucial for achieving complex missions, yet their limited battery life poses a significant challenge. In response, this research proposes a novel approach to extend the endurance of fixed-wing UAVs by enabling autonomous soaring in an orographic wind field. The goal of our research is to develop a controller that can identify feasible soaring regions and autonomously maintain position control without using any throttle. Soaring flight is desirable as it results in a low energy cost with zero throttle usage. However, without throttle usage, the longitudinal motion of the UAV is an under-actuated system, presenting control challenges. The concept of a target gradient line (TGL) is introduced as part of the control algorithm that addresses these challenges and autonomously finds the equilibrium soaring position where sink rate and updraft are in equilibrium. Experimental tests showed promising results, demonstrating the controller’s effectiveness in maintaining autonomous soaring flight in a non-static wind field. We also demonstrate a single degree of control freedom in the soaring position through manipulation of the TGL.