Research on Bottleneck Identification and Breakthrough Path of Industrial Cooperative Robots Enabled by Intelligent Algorithm

Abstract

With the development of intelligent manufacturing, industrial collaborative robots face bottlenecks such as insufficient perception
accuracy, low decision-making efficiency, and poor human-machine collaborative security in flexible manufacturing. This study focuses on
the bottleneck identification and breakthrough path under the empowerment of intelligent algorithms, systematically combs the development
status and core bottlenecks, and constructs the identification framework from three dimensions of technology, application and security. The
bottleneck feature extraction method based on multi-dimensional data fusion, intelligent prediction model for dynamic environment, real-time
optimization and adaptive mechanism are proposed to improve the recognition accuracy and real-time performance. From the three levels
of autonomous decision-making, human-machine collaboration and system optimization, the planning optimization based on reinforcement
learning, human-machine intelligent interaction and safety control, and system-level collaborative efficiency improvement path are proposed.
Case verification shows that intelligent algorithm empowerment can significantly improve bottleneck identification accuracy and breakthrough
efficiency, and provide theoretical support and practical path for intelligent upgrading of collaborative robots.