53 lines
1.6 KiB
Matlab
53 lines
1.6 KiB
Matlab
function test_base_params()
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% ---------------------------------------------------------------------
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% Test if base parameters are found correctly by comparing
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% torque prediction of the model with standard parameters with
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% the torque prediction of the model with base parameters
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% ----------------------------------------------------------------------
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path_to_urdf = 'ur10e.urdf';
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ur10 = parse_urdf(path_to_urdf);
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no_links = 6;
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% Perform QR decompostions
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include_motor_dynamics = 1;
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[~, baseQR] = base_params_qr(include_motor_dynamics);
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bb = baseQR.numberOfBaseParameters;
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E = baseQR.permutationMatrix;
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beta = baseQR.beta;
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includeMotorDynamics = baseQR.motorDynamicsIncluded;
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if includeMotorDynamics
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no_link_params = 11;
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ur10.pi(end+1,:) = rand(1,no_links);
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else
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no_link_params = 10;
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end
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ur10.pi = reshape(ur10.pi,[no_link_params*no_links, 1]);
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% Position, velocity and acceleration limits
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q_min = -pi*ones(6,1);
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q_max = pi*ones(6,1);
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qd_max = 3*pi*ones(6,1);
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q2d_max = 6*pi*ones(6,1);
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% On random positions, velocities, aceeleations
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for i = 1:100
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q_rnd = q_min + (q_max - q_min).*rand(6,1);
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qd_rnd = -qd_max + 2*qd_max.*rand(6,1);
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q2d_rnd = -q2d_max + 2*q2d_max.*rand(6,1);
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if includeMotorDynamics
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Yi = regressorWithMotorDynamics(q_rnd,qd_rnd,q2d_rnd);
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else
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Yi = standard_regressor_UR10E(q_rnd,qd_rnd,q2d_rnd);
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end
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tau_full = Yi*ur10.pi;
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pi_lgr_base = [eye(bb) beta]*E'*ur10.pi;
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Y_base = Yi*E(:,1:bb);
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tau_base = Y_base*pi_lgr_base;
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assert(norm(tau_full - tau_base) < 1e-6);
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end
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fprintf("Rigid Body Base Dynamics Test - OK!\n"); |