  Many real-world applications need path planning algorithms to solve tasks in
different areas, such as social applications, autonomous cars, and tracking
activities. And most importantly motion planning. Although the use of path
planning is sufficient in most motion planning scenarios, they represent
potential bottlenecks in large environments with dynamic changes. To tackle
this problem, the number of possible routes could be reduced to make it easier
for path planning algorithms to find the shortest path with less efforts. An
traditional algorithm for path planning is the A*, it uses an heuristic to work
faster than other solutions. In this work, we propose a CNN encoder capable of
eliminating useless routes for motion planning problems, then we combine the
proposed neural network output with A*. To measure the efficiency of our
solution, we propose a database with different scenarios of motion planning
problems. The evaluated metric is the number of the iterations to find the
shortest path. The A* was compared with the CNN Encoder (proposal) with A*. In
all evaluated scenarios, our solution reduced the number of iterations by more
than 60\%.
