Segmentation-Based Angular Position Estimation Algorithm for Dynamic Path Planning by a Person-Following Robot.

This study designed, developed, and evaluated a deep-learning-based companion robot prototype for indoor navigation and obstacle avoidance using an RGB-D camera as the sole input sensor. This study proposed a dynamic path planning (DPP) method that combines instance image segmentation and elementary matrix calculations to enable a robot to identify the angular position of entities in its surroundings. The DPP method fuses visual and depth information for scene understanding and path estimation with reduced computation resources. A simulated environment assessed the robot's path-planning ability through computer vision. The DPP method enables the person-following robot to perform intelligent curve manipulation for safe path planning to avoid objects in the initial trajectory. The approach offers a unique and straightforward technique for scene understanding without the burden of extensive neural network configuration. Its modular architecture and flexibility make it a promising candidate for future development and refinement in this domain. Its effectiveness in collision prevention and path planning has potential implications for various applications, including medical robotics.