The Multi-Modal Robot Perception, Language Information, and Environment Prediction Model Based on Deep Learning

Yangchao Cai, Yiming Zhao, Wenkai Zhang, Nian Xing

OAI: oai:igi-global.com:349987 • DOI: 10.4018/JOEUC.349987

This paper presents a deep learning-based model to enhance robot perception and prediction in complex environments. The model utilizes CNN, LSTM, and attention mechanisms to address multimodal perception and environment prediction tasks. The introduction provides background on the importance of robots in various fields and emphasizes the need for multimodal perception. The methodology section explains the working principles of CNN, LSTM, and attention mechanisms. The experimental results demonstrate significant improvements in robot perception and prediction, with low errors and high accuracy. The proposed model supports robot applications in complex environments such as autonomous navigation and human-robot interaction. It effectively handles multimodal data and enhances robot perception and prediction capabilities. The experimental results validate the model's effectiveness, providing important support for robot decision-making in complex environments.