Robot path planning and obstacle avoidance algorithm based on visual perception

A CNN-VSLAM framework that combines convolutional neural networks with visual simultaneous localization and mapping methods in robotics is proposed, enabling robots to independently traverse complex situations, avoid collisions with objects, and improve their ability to move and adapt to changing environments.