Research on Gait Planning for Elderly Biomimetic Robot Dogs

This study conducts an in-depth exploration of the gait issues of bionic robotic dogs for elderly care. The main objective of this research is to improve the model predictive control (MPC) algorithm through deep learning technology to enhance the gait control capabilities of bionic robotic dogs. Specifically, we aim to address the limitations of traditional control methods when dealing with dynamic environments. enable robots to walk more naturally in complex scenarios and interact effectively with humans. To this end, we will explore the combination of deep learning and MPC algorithms, aiming to achieve greater flexibility and adaptability. A deep neural network is used for learning and predicting gait data, which is integrated with the MPC algorithm to adjust gait control strategies through real-time feedback. Through a large number of simulation experiments and practical tests, we have verified the effectiveness of the proposed method. The contribution of this research lies in the proposal of a deep learning-based MPC algorithm, which significantly enhances the gait control performance of bio-mimetic robotic dogs.