摘要 :
This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environme...
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This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environment. Objects are 2.5D extruded polygons representing buildings or other structures. A visibility volume defines the set of admissible (occlusion-free) viewing locations for each target that satisfy feasible airspace and imaging constraints. The Dubins traveling salesperson problem with neighborhoods (DTSPN) is extended to three dimensions with visibility volumes that are approximated by triangular meshes. Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN. Additionally, a heuristic approach is proposed to improve computation time by approximating edge costs of the 3D Dubins airplane with a lower bound that is used to solve for a sequence of viewing locations. The viewing locations are then assigned pitch and heading angles based on their relative geometry. The proposed sampling methods and heuristics are compared through a Monte-Carlo experiment that simulates view planning tours over a realistic urban environment.
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摘要 :
This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environme...
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This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environment. Objects are 2.5D extruded polygons representing buildings or other structures. A visibility volume defines the set of admissible (occlusion-free) viewing locations for each target that satisfy feasible airspace and imaging constraints. The Dubins traveling salesperson problem with neighborhoods (DTSPN) is extended to three dimensions with visibility volumes that are approximated by triangular meshes. Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN. Additionally, a heuristic approach is proposed to improve computation time by approximating edge costs of the 3D Dubins airplane with a lower bound that is used to solve for a sequence of viewing locations. The viewing locations are then assigned pitch and heading angles based on their relative geometry. The proposed sampling methods and heuristics are compared through a Monte-Carlo experiment that simulates view planning tours over a realistic urban environment.
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Computational investigation of the influence of a multi-compression scramjet inlet geometry curvature on the separation bubble formed by the impinging shock wave on the boundary layer has been performed. The modifications to the c...
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Computational investigation of the influence of a multi-compression scramjet inlet geometry curvature on the separation bubble formed by the impinging shock wave on the boundary layer has been performed. The modifications to the compression ramp angle have been kept consistent with the base model used in the experimental work by Fischer et all 2009 [1]. The inlet isolator geometry curvature was modified with higher order polynomial fitting computationally simulated to determine the influence of these geometric changes on the internal flow field of the scramjet. Results from numerical simulations of the modified two-dimensional scramjet geometry using the unstructured finite volume-based solver STAR-CCM+ are presented. Two computational approaches were utilized, which included the conventional turbulence models in an unsteady Reynolds averaged Navier-Stokes (RAINS) approach. A novel turbulence model free computational approach was then implemented which solved the governing equations for the two-dimensional hypersonic flow field directly and without any turbulence modeling. The results were compared with available experimental data performed in a Mach 7.7 tunnel at the Shock Wave Laboratory, RWTH Aachen University, Germany. A comparative analysis of computational results with and without the turbulence models showed that the skin friction coefficient, pressure coefficient, and characteristics of the separation bubble were strongly affected by the RANS turbulence model and more resolved with the model-free approach.
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The demand for highway vehicles that can autonomously navigate has increased in the past few years. These vehicles typically rely on predefined maps, roadway markings, GPS, and a wide array of sensors to accomplish this feat. Thes...
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The demand for highway vehicles that can autonomously navigate has increased in the past few years. These vehicles typically rely on predefined maps, roadway markings, GPS, and a wide array of sensors to accomplish this feat. These techniques have proven themselves robust for highway navigation, however, these same sensors and navigation techniques do not convert well to off-road autonomous driving where visual cues and landmarks are fewer and the feat of terrain movement is more taxing on the vehicle. The authors have proposed a novel topology classification technique based on location data received by a wireless sensor network. The topology classification is used to aid in the navigation of treacherous terrain to allow an All-Terrain vehicle to navigate a set path.
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摘要 :
The demand for highway vehicles that can autonomously navigate has increased in the past few years. These vehicles typically rely on predefined maps, roadway markings, GPS, and a wide array of sensors to accomplish this feat. Thes...
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The demand for highway vehicles that can autonomously navigate has increased in the past few years. These vehicles typically rely on predefined maps, roadway markings, GPS, and a wide array of sensors to accomplish this feat. These techniques have proven themselves robust for highway navigation, however, these same sensors and navigation techniques do not convert well to off-road autonomous driving where visual cues and landmarks are fewer and the feat of terrain movement is more taxing on the vehicle. The authors have proposed a novel topology classification technique based on location data received by a wireless sensor network. The topology classification is used to aid in the navigation of treacherous terrain to allow an All-Terrain vehicle to navigate a set path.
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