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1	A Parallel Algorithm For Flight Route Planning On Gpu Using Cuda Sanci, Seckin 01 May 2010 (has links) (PDF) Aerial surveillance missions require a geographical region known as the area of interest to be inspected. The route that the aerial reconnaissance vehicle will follow is known as the flight route. Flight route planning operation has to be done before the actual mission is executed. A flight route may consist of hundreds of pre-defined geographical positions called waypoints. The optimal flight route planning manages to find a tour passing through all of the waypoints by covering the minimum possible distance. Due to the combinatorial nature of the problem it is impractical to devise a solution using brute force approaches. This study presents a strategy to find a cost effective and near-optimal solution to the flight route planning problem. The proposed approach is implemented on GPU using CUDA.
2	Exploring feasibility of reinforcement learning flight route planning / Undersökning av använding av förstärkningsinlärning för flyruttsplannering Wickman, Axel January 2021 (has links) This thesis explores and compares traditional and reinforcement learning (RL) methods of performing 2D flight path planning in 3D space. A wide overview of natural, classic, and learning approaches to planning s done in conjunction with a review of some general recurring problems and tradeoffs that appear within planning. This general background then serves as a basis for motivating different possible solutions for this specific problem. These solutions are implemented, together with a testbed inform of a parallelizable simulation environment. This environment makes use of random world generation and physics combined with an aerodynamical model. An A* planner, a local RL planner, and a global RL planner are developed and compared against each other in terms of performance, speed, and general behavior. An autopilot model is also trained and used both to measure flight feasibility and to constrain the planners to followable paths. All planners were partially successful, with the global planner exhibiting the highest overall performance. The RL planners were also found to be more reliable in terms of both speed and followability because of their ability to leave difficult decisions to the autopilot. From this it is concluded that machine learning in general, and reinforcement learning in particular, is a promising future avenue for solving the problem of flight route planning in dangerous environments. SAAB flight route planning autorouting auto-routing auto routing AI machine learning fighter jet convolution PPO DQN Astar A* C++ Python LibTorch PyTorch multi threading multi-threading simulation aerodynamics world generation Perlin noise investigation reward Flygplanering flygruttsplannering maskininlärning AI SAAB faltning faltningslager belöning

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A Parallel Algorithm For Flight Route Planning On Gpu Using Cuda

Exploring feasibility of reinforcement learning flight route planning / Undersökning av använding av förstärkningsinlärning för flyruttsplannering