61 lines
1.5 KiB
Mathematica
61 lines
1.5 KiB
Mathematica
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clear all; close all; clc;
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% Define path to a urdf file
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path_to_urdf = 'ur10e.urdf';
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% Generate functions for dynamics based on Lagrange method
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% Note that it might take some time
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% generate_rb_dynamics(path_to_urdf);
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generate_friction_eq();
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% Generate regressors for inverse dynamics of the robot, friction and load
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% Note that it might take some time
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% generate_rb_regressor(path_to_urdf);
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% generate_load_regressor(path_to_urdf);
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% Run tests
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test_rb_inverse_dynamics()
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test_base_params()
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% Perform QR decompostion in order to get base parameters of the robot
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include_motor_dynamics = 1;
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[pi_lgr_base, baseQR] = base_params_qr(include_motor_dynamics);
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% Estimate drive gains
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drive_gains = estimate_drive_gains(baseQR, 'PC-OLS');
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% Or use those found in the paper by De Luca
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% drive_gains = [14.87; 13.26; 11.13; 10.62; 11.03; 11.47];
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% Estimate dynamic parameters
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path_to_est_data = 'ur-20_02_10-30sec_12harm.csv'; idxs = [635, 3510];
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% path_to_data = 'ur-20_02_12-40sec_12harm.csv'; idxs = [500, 4460];
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% path_to_data = 'ur-20_02_05-20sec_8harm.csv'; idxs = [320, 2310];
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% path_to_data = 'ur-20_02_12-50sec_12harm.csv'; idxs = [355, 5090];
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sol = estimate_dynamic_params(path_to_est_data, idxs, ...
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drive_gains, baseQR, 'PC-OLS');
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% Validate estimated parameters
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path_to_val_data = 'ur-20_01_17-ptp_10_points.csv'; idxs = [700, 4200];
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rre = validate_dynamic_params(path_to_val_data, idxs, ...
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drive_gains, baseQR, sol.pi_b, sol.pi_fr)
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