(Peer-Reviewed) Application of Optimal-Jerk Trajectory Planning in Gait-balance Training Robot
Fu Yuan 袁福, Diansheng Chen 陈殿生, Chenghang Pan 潘成行, Jun Du 杜俊, Xiaodong Wei 魏晓东, Min Wang 王敏
Robotics Institute, Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China
北京航空航天大学机械工程及自动化学院 机器人研究所
Abstract
To accommodate the gait and balance disorder of the elderly with age progression and the occurrence of various senile diseases, this paper proposes a novel gait balance training robot (G-Balance) based on a six degree-of-freedom parallel platform. Using the platform movement and IMU wearable sensors, two training modes, i.e., active and passive, are developed to achieve vestibular stimulation. Virtual reality technology is applied to achieve visual stimulation.
In the active training mode, the elderly actively exercises to control the posture change of the platform and the switching of the virtual scene. In the passive training mode, the platform movement is combined with the virtual scene to simulate bumpy environments, such as earthquakes, to enhance the human anti-interference ability. To achieve a smooth switching of the scene, continuous speed and acceleration of the platform motion are required in some scenarios, in which a trajectory planning algorithm is applied.
This paper describes the application of the trajectory planning algorithm in the balance training mode and the optimization of jerk (differential of acceleration) based on cubic spline planning, which can reduce impact on the joint and enhance stability.
Multiplexed stimulated emission depletion nanoscopy (mSTED) for 5-color live-cell long-term imaging of organelle interactome
Yuran Huang, Zhimin Zhang, Wenli Tao, Yunfei Wei, Liang Xu, Wenwen Gong, Jiaqiang Zhou, Liangcai Cao, Yong Liu, Yubing Han, Cuifang Kuang, Xu Liu
Opto-Electronic Advances
2024-07-05