AAI_2025_Capstone_Chronicles_Combined

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Draw, Detect, Navigate ​

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destination. The algorithm calculates a score that combines the distance traveled from the

starting point with an estimate of the remaining distance to the destination, allowing it to

prioritize nodes that are closer to the goal. This approach makes A* particularly effective in

applications where efficient route planning navigation is required, such as robotics and video

games where there are often scenarios requiring rapid and efficient routing between two points,

as well flight path planning for challenging environments (Luo et al., 2023; Rubio, 2023).

​ CNNs are a standard choice for many image classification tasks. One widely used

dataset is MNIST, which evaluates CNN performance in classifying handwritten digits. While

MNIST demonstrates the potential of CNNs in classification tasks, our project involves

recognizing freehand doodles from Quick, Draw!, which presents a more complex classification

challenge. CNN-based models have proliferated across image classification research, but the

architectures of YOLO and Faster R-CNN have dominated applications targeting real-time use

due to their speed and performance.

​ The machine learning model Faster Regional Convolutional Neural Network (Faster

R-CNN) creates region proposals from the image, computes the CNN features, and then

classifies these regions (Ren et al., 2017). Given correctly identified regions or bounding boxes

coupled with a label, while simplistic, facilitates a dense possibility space of conveyances given

labeled objects in various proximities to one another such as strategic planning or rescue

planning, quick rapid spatial conveyance, wargaming, or concept conveyance. As a

demonstration of capabilities that become available with pictogram positioning coupled with

identification, a pathfinding implementation using the A* algorithm was developed to route a

starting object, a helicopter, to an objective, a hospital, that recommends the most efficient

path avoiding detected obstacle pictograms. Other approaches evaluated during explorative

phases include YOLO and custom convolutional neural network implementations.

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