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

TL;DR

This paper introduces a CNN encoder that reduces the search space for motion planning, significantly decreasing the number of iterations needed for A* to find the shortest path in various scenarios.

Contribution

A novel CNN encoder is proposed to eliminate irrelevant routes, enhancing the efficiency of traditional A* path planning in dynamic environments.

Findings

Reduced iterations by over 60% in all tested scenarios.

Effective combination of CNN encoder with A* improves planning speed.

Database of diverse motion planning scenarios used for evaluation.

Abstract

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\%.