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

• Published in: IEEE access Vol. 11; pp. 41034 - 41053
• Main Authors: Asante, Isaac, Theng, Lau Bee, Tsun, Mark Tee Kit, Jo, Hudyjaya Siswoyo, McCarthy, Chris
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Angular position; Cameras; Collision avoidance; Computer vision; Heuristic algorithms; Human-robot interaction; Image segmentation; Indoor navigation; Machine learning; Navigation; Neural networks; object detection; Obstacle avoidance; Path planning; Robot dynamics; Robot kinematics; Robot vision systems; Robotics; Robots; Scene analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3269796

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.