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Many real-world applications need path planning algorithms to solve tasks indifferent areas, such as social applications, autonomous cars, and trackingactivities. And most importantly motion planning. Although the use of pathplanning is sufficient in most motion planning scenarios, they representpotential bottlenecks in large environments with dynamic changes. To tacklethis problem, the number of possible routes could be reduced to make it easierfor path planning algorithms to find the shortest path with less efforts. Antraditional algorithm for path planning is the A*, it uses an heuristic to workfaster than other solutions. In this work, we propose a CNN encoder capable ofeliminating useless routes for motion planning problems, then we combine theproposed neural network output with A*. To measure the efficiency of oursolution, we propose a database with different scenarios of motion planningproblems. The evaluated metric is the number of the iterations to find theshortest path. The A* was compared with the CNN Encoder (proposal) with A*. Inall evaluated scenarios, our solution reduced the number of iterations by morethan 60\%.

CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning