Development and Evaluation of a Computer Vision System for Robot Navigation and Object Recognition in Real-World Environments

Abstract

The article discusses the vision framework for computing that includes image recognition, classification, prioritization, and navigation control modules. In this framework, a user model is used to feed the robotic controllers, whose performance improves in dynamic virtual contexts. In contrast, the vision module uses a multi-level perceptual neural network capable of efficient image segmentation, object recognition, and color segmentation, using the control module Position-Based Vision Serving (PBVS) and actions such as Avoid Collision (), Go-Ahead (), and Follow( ). It controls the motion of the robot, so the system successfully tested and met the requirements of the Antimedia Robotics Pioneer I robot. In addition, it was consistent with real life. The results show the effectiveness of the system in providing effective guidance and avoiding obstacles. Furthermore, the study investigates the use of artificial neural networks for image recognition and classification. In addition, it requires the use of SpCoMapping to add language maps to useful information. In summary, studies have emphasized the potential of computer vision and neural networks to improve robotic communication and language learning.