Vectorized Visibility Graph Planning with Neural Polygon Extraction

Abstract

This article proposes an approach for path planning in environments with obstacles. The methodology integrates recent neural obstacle polygon extraction with visibility graph path planning, complemented by the integration of vectorization techniques. That significantly enhances path planning efficiency. The modular design of the neural network method, encompassing contour detection, vertex identification, and polygon approximation modules, facilitates improved performance compared to traditional methods. Furthermore, the investigation into vectorization’s impact on the intersection operation accelerates algorithm speed, contributing to faster path planning processes. Experimental results validate the efficacy of the integrated approach, showcasing notable improvements in path planning efficiency, especially with the utilization of vectorization techniques. The study systematically addresses challenges such as slow graph construction and inaccurate obstacle detection, providing a robust solution for optimizing path planning processes. Moreover, the implementation of multiple modules in the methodology enables its versatility for testing with various environments. This versatility allows researchers to assess the method’s performance across diverse scenarios and visualize the results effectively. Overall, the integrated approach offers a comprehensive solution for optimizing path planning in complex environments, demonstrating its potential to streamline path planning processes and improve mobile robot navigation.