Improving the performance of Heuristic Path Planning Algorithms by a Computer Vision Obstacle Detection Method

In this article, the authors explore how well-known computer vision methods can support path planning in static environments. Such methods can play an important role in real-time industrial applications, providing accurate and efficient results while ensuring a high level of safety. The proposed solution uses a computer vision algorithm for boundary detection, supported by a convolutional neural network (CNN), to identify obstacles in an input image. The grid map is then updated with obstacle edge information obtained from the computer vision algorithm before the path is recalculated using heuristic planning algorithms. This method was tested in a simulated environment with various numbers and locations of obstacles. The performance of each case was evaluated by calculating the path length, planning time, and the number of nodes explored. The results suggest that using a computer vision-based obstacle detection algorithm to prepare the map improves the overall efficiency and accuracy of the planning process. Although the environment remains static, using visual data makes the setup more realistic and closer to real-world conditions.