基于粒子滤波的Stewart平台正运动学求解

Key Points

• The aim is to enhance the stability and accuracy of kinematic solutions for the Stewart platform.
• Developed a state space model integrating pose and rod length parameters
• Applied particle filtering for probabilistic state estimation
• Conducted MATLAB simulations to compare performance against traditional methods
• Achieved an 85.35% to 99.43% reduction in mean absolute error
• Reduced root mean square error by 86.98% to 99.79%
• Demonstrated superior convergence performance and adaptability under complex conditions