Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model

TL;DR

This paper demonstrates that integrating a CNN Encoder with path planning algorithms significantly reduces computation time, averaging a 54.43% decrease, especially in large and dynamic environments.

Contribution

It introduces a CNN Encoder model to enhance path planning efficiency by reducing dimensionality and eliminating useless paths in complex environments.

Findings

Average time reduction of 54.43% in path planning.

CNN Encoder outperforms traditional linear methods.

Effective in both static and dynamic obstacle scenarios.

Abstract

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 %.