Estimation of robot states with poisson process based on EKF approximate of Kushner filter: a completely coordinate free Lie group approach

Meccanica - In this paper, a Lie coordinate-free torque based Euler–Lagrange equations of motion are developed for a 3-D link (3-DOF) robot. Intentional torque and jerky torque...     

Parameter estimation,data compression and stochastic noise elimination in robotics: a wavelet domain-based integrated approach

Accurate parameter estimation in presence of stochastic noise is the essential part of almost all control hardware of the plants. However, the optimal design of the control hardware depends on processing power and installed memory. The proposed research investigation focuses on precise parameter estimation from compressed temporal data of error dynamics with exiguous susceptibility to the robot’s controllability. Instead of using Maximum Likelihood estimation (MLE) and Least Squares (LS) estimation in the time domain, the proposed method exploits the recursive wavelet domain’s properties to selectively store the error data coefficients negating the data related to noise. As a result, data compression is achieved. The proposed algorithm may be directly implemented on any scalable “Very Large Scale Integration” (VLSI) circuit due to the recursive implementation. For the evaluation of robustness, dynamic parameter variation is considered. The variation in scalar & vector-valued error is considered to evaluate the performance of the stochastic system. The proposed algorithm implementation is demonstrated experimentally on commercially available Omni Bundle robot.