Obstacle Avoidance Strategy for Mobile Robot Based on Monocular Camera

This research paper proposes a real-time obstacle avoidance strategy for mobile robots with monocular camera. The approach uses binary semantic segmentation FCN-VGG-16 to extract features from images captured by the camera and estimate position distance of obstacles in robot’s environment. Segmented are used create frontal view robot. Then, optimized path planning based on enhanced A* algorithm set weighted factors, such as collision, path, smooth cost improves performance path. In addition, collision-free will be devised optimizing functions. Lastly, results our evaluation show that successfully detects avoids static dynamic real time high accuracy, efficiency, steering low angle changes. Our offers potential solution both global local planning, addressing challenges complex environments while minimizing need expensive complicated sensor systems.