Author(s)

Yuting Wang, Haifeng Wang

Corresponding Author

Yuting Wang

Abstract

This paper focuses on the innovative application of multimodal artificial intelligence-generated content (AIGC) in robot environmental perception and behavior planning. By systematically combining the theoretical framework and evolution path of multimodal AIGC technology, its transformative effect on traditional robot perception and planning methods is deeply analyzed. Studies have shown that multimodal AIGC technology effectively solves the perception limitations of robots in complex environments and improves planning efficiency through data fusion, generative model construction, and transfer learning optimization. Combined with the actual cases of intelligent warehousing robots and guide robots, this study verifies the remarkable effectiveness of this technology in improving the robot's environmental understanding ability and decision-making intelligence. It provides new theories and practices for the development of intelligent robots.

Keywords

Multimodal AIGC; Robot Environmental Perception; Behavior Planning; Generative Model; Deep Learning; Data Fusion; Transfer Learning; Intelligent Robots