Research Progress on the Integration of Robot Vision, Computer Vision and Machine Learning: Technological Evolution, Challenges and Industrial Applications

The purpose of this project is to perform a systematic review of the current state of technology pertaining to the development of robotic perception and decision-making capabilities through the integration of robotic systems with computer vision (CV) and machine learning (ML) technologies. The project examines a variety of different design approaches for global vision systems, local embedded vision systems, and hybrid cloud-edge systems. The project examines advanced algorithms in CV for target detection, 3D modeling, and dynamic scene analysis, as well as the underlying algorithmic innovations that drive advancements in these areas. The project aims to identify the most prevalent and propose innovative solutions to the most significant challenges, including

the lack of sufficient data, real-time processing, and the challenges associated with multi-modal data fusion. Proposed solutions utilize emerging technologies such as lightweight models for data processing, federated learning for distributed data processing, and neural symbolic systems for advanced scene interpretation. The objective of the project is to review the most current academic literature and industry publications, to develop a comprehensive academic structure in order to provide theoretical and practical guidance for the intelligent enhancement of robotic systems, and to propose, based on this assessment, future directions for industrial applications and research.

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