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Segmentation-Based Angular Position Estimation Algorithm for Dynamic Path Planning by a Person-Following Robot
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AI
DOI：10.1109/ACCESS.2023.3269796.png)
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Abstract
En 中文
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.
Keywords:
Robots
Navigation
Robot kinematics
Robot vision systems
Image segmentation
Heuristic algorithms
Cameras
Human-robot interaction
image segmentation
object detection
path planning