tailieunhanh - Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 6

Tham khảo tài liệu 'machine learning and robot perception - bruno apolloni et al (eds) part 6', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 118 Y. Sun et al. To subband a signal Discrete Wavelet Transform is used. As shown in Fig. h n and g n are a lowpass filter and a highpass filter respectively. The two filters can halve the bandwidth of the signal at this level. Fig. also shows the DWT coefficients of the higher frequency components at each level. As a result the raw signal is preprocessed to have the desired low frequency components. The multiresolution approach from discrete wavelet analysis will be used to decompose the raw signal into several signals with different bandwidths. This algorithm makes the signal in this case the raw angular velocity signal passes through several lowpass filters. At each level it passes the filter and the bandwidth of the signal would be halved. Then the lower frequency component can be obtained level by level. The algorithm can be described as the following procedures a Filtering Passing the signal through a lowpass Daubechies filter with bandwidth which is the lower half bandwidth of the signal at the last level. Subsampling the signal by factor 2 then reconstructing the signal at this level b Estimating Using the RLSM to process the linear velocity signal and the angular velocity signal obtained from the step a to estimate the kinematic length of the cart. c Calculating Calculating the expectation of the length estimates and the residual. d Returning Returning to a until it can be ensured that is increasing. e Comparing Comparing the residual in each level take the estimate of length at a level which has the minimum residual over all the levels as the most accurate estimate. The block diagram of DWMI algorithm is shown in Fig. . 3. On-line Model Learning for Mobile Manipulations 119 Fig. . Block Diagram of Model Identification Algorithm Convergence of Estimation In this section the parameter estimation problem in time domain is analyzed in frequency domain. The estimation convergence means that the estimate of the parameter can approximately .