CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning
This work addresses motion planning inefficiencies for applications like autonomous cars and social apps, though it appears incremental as it combines a neural network with an existing algorithm.
The paper tackled the bottleneck of path planning in large dynamic environments by proposing a CNN encoder to eliminate useless routes before applying A*, reducing the number of iterations needed to find the shortest path by over 60% in all tested scenarios.
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\%.