Detection and Analysis of Bionic Motion Pose of Single Leg and Hip Joint Based on Random Process

Area of Science:

• Robotics and Biomechanics
• Stochastic Processes and Signal Processing

Background:

• Accurate simulation of non-Gaussian random processes is crucial for modeling complex systems.
• Understanding the dynamics of legged locomotion, particularly the role of joint flexibility, is essential for advanced robotics and biomechanics.

Purpose of the Study:

• To develop a method for generating stationary and non-stationary non-Gaussian random processes.
• To investigate the influence of a flexible rotary hip joint on the stability and energy conversion of a single-leg bouncing motion.
• To validate an experimental platform for simulating human hip joint dynamics and assess dynamic parameter identification methods.

Main Methods:

• Spectral representation method and nonlinear translation theory for random process generation.
• Analysis of a single-leg bouncing model with a flexible rotary hip joint, including inverse dynamic control.
• Experimental platform construction for kinematic and dynamic verification, including modal analysis and dynamic parameter identification.

Main Results:

• Successfully generated stationary and non-stationary non-Gaussian random processes conforming to target distributions.
• The flexible hip joint's influence on motion stability and energy conversion was analyzed, with inverse dynamic control improving fixed point distribution.
• The experimental platform demonstrated good agreement for trajectory prediction (X, Y axes) and validated the effectiveness of dynamic parameter identification for torque prediction, increasing confidence by 10-16%.

Conclusions:

• The developed methods effectively simulate target non-Gaussian random processes.
• Flexible hip joints significantly impact bouncing motion stability and energy dynamics, offering potential for improved robotic designs.
• The experimental platform is suitable for human hip joint simulation, and the dynamic parameter identification method provides reliable torque prediction.