  Currently, path planning algorithms are used in many daily tasks. They are
relevant to find the best route in traffic and make autonomous robots able to
navigate. The use of path planning presents some issues in large and dynamic
environments. Large environments make these algorithms spend much time finding
the shortest path. On the other hand, dynamic environments request a new
execution of the algorithm each time a change occurs in the environment, and it
increases the execution time. The dimensionality reduction appears as a
solution to this problem, which in this context means removing useless paths
present in those environments. Most of the algorithms that reduce
dimensionality are limited to the linear correlation of the input data.
Recently, a Convolutional Neural Network (CNN) Encoder was used to overcome
this situation since it can use both linear and non-linear information to data
reduction. This paper analyzes in-depth the performance to eliminate the
useless paths using this CNN Encoder model. To measure the mentioned model
efficiency, we combined it with different path planning algorithms. Next, the
final algorithms (combined and not combined) are checked in a database that is
composed of five scenarios. Each scenario contains fixed and dynamic obstacles.
Their proposed model, the CNN Encoder, associated to other existent path
planning algorithms in the literature, was able to obtain a time decrease to
find the shortest path in comparison to all path planning algorithms analyzed.
the average decreased time was 54.43 %.
