Object Recognition and Collision Avoidance in Robotic Systems Using YOLO and HS-CLAHE Techniques

Navigation systems in mobile robots are dynamic and unpredictable. These systems will require complex real-time object recognition and collision avoidance methods to improve efficiency and safety in the manufacturing, healthcare, and aviation industries. Improvement in object recognition using an integrated framework developed using YOLOv7, HS-CLAHE, RRT, and DWA; verification of navigation efficiency in real environments; and improvement in path planning.The proposed system, which integrates RRT and DWA for adaptive path planning and motion control in dynamic environments, YOLOv7 for object recognition, and HS-CLAHE for picture enhancement, was tested with extensive simulations and real-world tests. The proposed approach outperforms the state-of-the-art methods for all the metrics, such as accuracy, which is 98%, precision, 97%, recall, 96%, F1 score, and RME, which is 2.5%. The integrated framework performs very well in complex scenarios and ensures real-time navigation and obstacle avoidance. Thus, it will present a scalable solution for accuracy and safety in autonomous robotic systems, which will be efficient.