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Obstacle Avoidance in Mobile Robots in RGB-D Images Using Deep Neural Network and Semantic Segmentation

Automating industrial and commercial activities through the use of manipulative robots capable of movement and carrying a variety of products is gaining popularity. When it comes to mobile robots, their operating systems are more complex, which means they are more in demand due to their use in research, which increases their price. These devices run on a variety of operating systems. Obstacle avoidance, line tracking, and mobile explorers are just a few examples. As a result of our findings, we propose a vision system that utilizes a feature extraction and classification algorithm DNN for obstacles in RGB-D images in order to detect and avoid obstacles in known environments.