From 7318f135c345c9acda4de5723e18e2f6e5cf0110 Mon Sep 17 00:00:00 2001 From: Gowtham Garimella Date: Fri, 15 Jun 2018 02:49:31 -0400 Subject: [PATCH] Update nn arch should be used with corresponding gcop branch --- .../joint_gains_kd_0 | 2 +- .../joint_gains_kp_0 | 2 +- ..._dynamics_dense_0_residual_dynamics_beta_0 | 2 +- ...dynamics_dense_0_residual_dynamics_gamma_0 | 2 +- ...cs_dense_0_residual_dynamics_moving_mean_0 | 2 +- ...ense_0_residual_dynamics_moving_variance_0 | 2 +- .../residual_dynamics_dense_0_weights_0 | 4 +- ..._dynamics_dense_1_residual_dynamics_beta_0 | 2 +- ...dynamics_dense_1_residual_dynamics_gamma_0 | 2 +- ...cs_dense_1_residual_dynamics_moving_mean_0 | 2 +- ...ense_1_residual_dynamics_moving_variance_0 | 2 +- .../residual_dynamics_dense_1_weights_0 | 2 +- .../residual_dynamics_dense_final_biases_0 | 2 +- .../residual_dynamics_dense_final_weights_0 | 2 +- .../rpy_gains_kd_0 | 2 +- .../rpy_gains_kp_0 | 2 +- param/mpc_controller_config.pbtxt | 20 +++--- scripts/analysis/compare_mpc_trajectories.py | 54 ++++++++++++++++ scripts/analysis/plot_mpc_trajectories.py | 64 +++++++++++++++++++ .../mpc_controller_tuner.cpp | 6 +- 20 files changed, 148 insertions(+), 30 deletions(-) create mode 100644 scripts/analysis/compare_mpc_trajectories.py create mode 100644 scripts/analysis/plot_mpc_trajectories.py diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/joint_gains_kd_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/joint_gains_kd_0 index 219f4a1d..62988156 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/joint_gains_kd_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/joint_gains_kd_0 @@ -1,3 +1,3 @@ 2 1 -1.489354,1.2015601, +1.2879044,1.1704179, diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/joint_gains_kp_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/joint_gains_kp_0 index 1c0210d4..e5aaafc1 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/joint_gains_kp_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/joint_gains_kp_0 @@ -1,3 +1,3 @@ 2 1 -11.780129,11.661143, +11.422288,11.645887, diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_0_residual_dynamics_beta_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_0_residual_dynamics_beta_0 index 2533bc53..34a437b5 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_0_residual_dynamics_beta_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_0_residual_dynamics_beta_0 @@ -1,3 +1,3 @@ 16 1 -0.27311215,-0.104236856,-0.025411092,0.29267374,-0.2025853,0.021320792,0.06885028,0.16424008,-0.015703725,-0.14184234,0.074954376,-0.36680064,0.45662504,-0.16305926,0.040393177,-0.04175968, 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diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_beta_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_beta_0 index 03fc37ff..7a58641b 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_beta_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_beta_0 @@ -1,3 +1,3 @@ 8 1 --0.074566334,0.04588292,0.013513993,-0.09232558,0.08453735,-0.08074734,-0.012946017,-0.049817093, +-0.00592649,-0.037208334,0.02271216,-0.13219856,0.0020418598,0.013361917,0.022096215,-0.045767006, diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_gamma_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_gamma_0 index 25a3846a..bd7e2777 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_gamma_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_gamma_0 @@ -1,3 +1,3 @@ 8 1 -0.407895,0.46385458,0.59804624,0.6940339,0.65389645,0.57341135,0.60178936,0.6238263, +0.3233974,0.55816483,1.7782717,0.8032256,0.2996349,0.3085106,0.39490306,0.4113996, diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_moving_mean_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_moving_mean_0 index 409af439..2b1c2d27 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_moving_mean_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_moving_mean_0 @@ -1,3 +1,3 @@ 8 1 --0.027100913,-0.08684619,0.07639614,-0.3055278,0.2933677,0.030098654,-0.24982458,-0.102109015, +-0.062266674,-0.02714833,-0.012357137,-0.0359934,-0.010994579,0.04012002,0.08387415,0.011114856, diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_moving_variance_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_moving_variance_0 index 1e95e4b8..72748cbc 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_moving_variance_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_residual_dynamics_moving_variance_0 @@ -1,3 +1,3 @@ 8 1 -0.117956616,0.29506895,0.41771913,0.2054379,0.07992752,0.20110367,0.6744197,0.57749856, +0.510723,0.59528226,1.2511085,0.55236363,0.5134672,0.52601063,0.44630972,0.34227136, diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_weights_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_weights_0 index c6aa9e50..fcdfa62d 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_weights_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_1_weights_0 @@ -1,3 +1,3 @@ 16 8 --0.3955174982547760,0.3475214540958405,0.1666727811098099,0.0723626837134361,0.2040266841650009,0.2776500284671783,-0.2691484093666077,-0.8262147903442383,-0.5009171366691589,0.3135701119899750,-0.1797807067632675,-0.0054747839458287,0.2813730835914612,0.0369706563651562,-0.2981808483600616,-0.0751455724239349,-0.3041052520275116,-0.2850141823291779,-0.1785960644483566,0.2523778676986694,0.3894436657428741,0.1003281250596046,0.4786004722118378,0.0696862637996674,-0.0341695286333561,-0.1455603092908859,0.0134820388630033,-0.1852132380008698,-0.0570538826286793,0.3001527190208435,-0.4711738228797913,0.3554453551769257,-0.3214689195156097,0.0352475531399250,-0.0016455277800560,0.0972204506397247,-0.3351288437843323,-0.2225704342126846,0.4871674180030823,0.3621132373809814,0.0729761421680450,-0.0597230568528175,0.0621601231396198,0.5811676383018494,0.3744303882122040,-0.6263814568519592,-0.0575744248926640,0.0956700667738914,0.1255384534597397,0.3155196011066437,0.2859912216663361,0.3514328002929688,0.3252155482769012,-0.4174808263778687,-0.3955832719802856,-0.2823237776756287,0.1657994985580444,-0.3833343088626862,0.4771501421928406,0.0193396937102079,-0.2951161265373230,-0.3858037889003754,-0.0931366756558418,0.0565191507339478,0.0573556944727898,0.2362638264894485,-0.6156558394432068,-0.2911368906497955,0.4094492197036743,-0.1326601803302765,-0.4948909878730774,0.0474076606333256,-0.3281344175338745,0.5307967066764832,-0.0575741119682789,0.2978443205356598,0.1407214701175690,-0.4407677352428436,0.1542461067438126,0.3518172204494476,-0.1211540848016739,0.2927716076374054,-0.7705510258674622,-0.2600173056125641,0.6238839626312256,-0.1764952242374420,0.4458127021789551,-0.0878090336918831,0.3454935252666473,0.1552325338125229,0.0573949143290520,0.1756333708763123,-0.3654964566230774,0.1241131350398064,-0.3488118350505829,-0.3709834516048431,-0.3155064284801483,-0.1541214436292648,-0.1467889100313187,-0.4616673588752747,0.3663932979106903,-0.2733428180217743,-0.1969577670097351,-0.1497866809368134,-0.5888675451278687,-0.3959282934665680,-0.0771000683307648,0.6153556108474731,-0.3025265932083130,-0.1340731084346771,0.0317353010177612,-0.4254517853260040,0.2067295014858246,0.0715371966362000,-0.2630290091037750,0.2111856341362000,0.3259117305278778,-0.4022766649723053,-0.1947669535875320,-0.1605077385902405,-0.3210546076297760,-0.0752286165952682,-0.1233168765902519,-0.3483547270298004,-0.2834948003292084,-0.2165223062038422,0.3089371323585510,0.2668958306312561, 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diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_final_biases_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_final_biases_0 index b6e6436e..23f76ee7 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_final_biases_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_final_biases_0 @@ -1,3 +1,3 @@ 8 1 --0.0016912151,-0.099235654,0.04752723,0.238435,-0.16314504,-0.00993302,-0.23952977,0.0340867, +-0.012836409,-0.13071178,0.035183474,0.20557193,-0.18581991,0.0029871017,-0.09271976,-0.11484799, diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_final_weights_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_final_weights_0 index c8337bd6..4fee6c0e 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_final_weights_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/residual_dynamics_dense_final_weights_0 @@ -1,3 +1,3 @@ 8 8 --0.1304899752140045,0.5376957654953003,0.3657346963882446,0.0211216472089291,0.9303691983222961,-0.0230968613177538,-0.5883984565734863,-0.5699948072433472,0.0085330447182059,0.1501954346895218,-0.5975889563560486,0.4543600678443909,0.6764503121376038,-0.0355348400771618,-0.3476428389549255,-0.6163758635520935,-0.2919064462184906,-0.0622706785798073,-0.1573668867349625,-0.5903056859970093,-0.6360251307487488,0.1800285726785660,-0.7477556467056274,-0.6263556480407715,-0.1856515109539032,0.2501020729541779,-0.4835608303546906,-0.3755324184894562,-0.2784877717494965,0.1496413350105286,-1.4380418062210083,-0.2659917771816254,-0.0646716058254242,0.2803609371185303,-0.2114755064249039,-0.1652359962463379,0.4358581602573395,0.1223051920533180,1.4152486324310303,1.2322967052459717,0.4261342287063599,0.3276852071285248,-0.1587564945220947,0.4951451718807220,0.1598387360572815,-0.0454892069101334,0.3677675426006317,1.0028101205825806,-0.3015342056751251,-0.3436273932456970,0.0812027975916862,-0.0243031568825245,-0.1675346195697784,-0.1325416117906570,1.4216343164443970,1.7214468717575073,0.2044980525970459,0.3093000948429108,0.4273132383823395,0.1155300512909889,0.7266339659690857,0.0504091754555702,0.2010100781917572,0.6295794248580933, 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diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/rpy_gains_kd_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/rpy_gains_kd_0 index fe5a2bfa..79a7fc5f 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/rpy_gains_kd_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/rpy_gains_kd_0 @@ -1,3 +1,3 @@ 3 1 -8.323076,6.1193175,3.2318325, +8.173418,7.0072937,3.3337743, diff --git a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/rpy_gains_kp_0 b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/rpy_gains_kp_0 index 7ed039cb..b66b4d14 100644 --- a/neural_network_model_data/tensorflow_model_vars_16_8_tanh/rpy_gains_kp_0 +++ b/neural_network_model_data/tensorflow_model_vars_16_8_tanh/rpy_gains_kp_0 @@ -1,3 +1,3 @@ 2 1 -16.546741,17.375994, +16.51752,17.266687, diff --git a/param/mpc_controller_config.pbtxt b/param/mpc_controller_config.pbtxt index 4f92b532..b438a09f 100644 --- a/param/mpc_controller_config.pbtxt +++ b/param/mpc_controller_config.pbtxt @@ -1,17 +1,17 @@ ddp_config { # State cost # XYZ - Q: 1 - Q: 1 - Q: 1 + Q: 100 + Q: 100 + Q: 100 # RPY Q: 4 Q: 4 Q: 4 # Vxyz - Q: 4 - Q: 4 - Q: 4 + Q: 100 + Q: 100 + Q: 100 # RPYdot Q: 4 Q: 4 @@ -57,8 +57,8 @@ ddp_config { Qf: 100 Qf: 100 # Joint_cmd - Qf: 0.1 - Qf: 0.1 + Qf: 100 + Qf: 100 # Control cost # Thrust R: 6.0 @@ -76,9 +76,9 @@ ddp_config { # Min cost decrease min_cost_decrease: 1e-4 # Look ahead time - look_ahead_time: 0.02 + look_ahead_time: 0.04 # Max cost - max_cost: 100.0 + max_cost: 500.0 } weights_folder: "neural_network_model_data/tensorflow_model_vars_16_8_tanh/" diff --git a/scripts/analysis/compare_mpc_trajectories.py b/scripts/analysis/compare_mpc_trajectories.py new file mode 100644 index 00000000..5d781d72 --- /dev/null +++ b/scripts/analysis/compare_mpc_trajectories.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +import matplotlib.pyplot as plt +import argparse +import os +import pandas as pd +import seaborn as sns +import numpy as np +# Args: -f ../../log_mpc_June_13th_2018/data_18_06_12_23_16_26 ../../log_mpc_June_13th_2018/data_18_06_12_22_59_56 -l RNN FF -s ../../ +# %% Getting data +parser = argparse.ArgumentParser( + prog='plot_quad_data') +parser.add_argument('-f', '--folders', type=str,nargs='+', + help='Data folders to compare') +parser.add_argument('-l', '--legends', type=str, nargs='+', + help='Legends for each folder') +parser.add_argument('-s', '--save_folder', type=str, default='./', + help='Folder to save final plot') +parser.add_argument('--tStart', type=float, default=0.0, help='Start time') +parser.add_argument('--tEnd', type=float, default=1e3, help='End time') +args = parser.parse_args() +error_df_list = [] +assert(len(args.folders) == len(args.legends)) + +for iFolder, folder in enumerate(args.folders): + state_data = pd.read_csv(os.path.join(folder, 'mpc_state_estimator')) + error_data = pd.read_csv(os.path.join(folder, 'ddp_mpc_controller')) + ts = state_data['#Time'].values + ts1 = (error_data['#Time'].values - ts[0])/1e9 + ts = (ts - ts[0])/1e9 + iStart = np.argmin(np.abs(ts - args.tStart)) + iEnd = np.argmin(np.abs(ts - args.tEnd)) + interp_error_list = [] + labels = ['Errorx','Errory','Errorz','Errorja1','Errorja2'] + for label in labels: + interp_error_list.append(np.interp(ts, ts1, error_data[label].values)) + interp_errors = np.vstack(interp_error_list).T + abs_errors = np.abs(interp_errors) + folder_label = args.legends[iFolder] + readable_labels = ['X (m)', 'Y (m)', 'Z (m)', 'J1 (rad)', 'J2 (rad)'] + df = pd.DataFrame(abs_errors, columns=readable_labels) + df = df.stack().reset_index() + df.columns = ['Index', 'Sensor Channels', 'Mean Absolute Error'] + df['FolderLabel'] = [folder_label]*df.shape[0] + error_df_list.append(df) + +error_df = pd.concat(error_df_list) +# %% Plotting +sns.set_style('whitegrid') +sns.set(font_scale = 1.2) +plt.figure(1) +sns.barplot('Sensor Channels', 'Mean Absolute Error', + 'FolderLabel', data=error_df, ci=95) +plt.savefig(os.path.join(args.save_folder, 'mpc_error_plot.eps'), + bbox_inches='tight') \ No newline at end of file diff --git a/scripts/analysis/plot_mpc_trajectories.py b/scripts/analysis/plot_mpc_trajectories.py new file mode 100644 index 00000000..348e27d9 --- /dev/null +++ b/scripts/analysis/plot_mpc_trajectories.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python3 +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import argparse +import os +import pandas as pd +import seaborn as sns +import numpy as np +# %% Getting data +parser = argparse.ArgumentParser( + prog='plot_quad_data') +parser.add_argument('folder', type=str, help='Data folder') +parser.add_argument('--tStart', type=float, default=0.0, help='Start time') +parser.add_argument('--tEnd', type=float, default=1e3, help='End time') +args = parser.parse_args() +state_data = pd.read_csv(os.path.join(args.folder, 'mpc_state_estimator')) +error_data = pd.read_csv(os.path.join(args.folder, 'ddp_mpc_controller')) +ts = state_data['#Time'].values +ts1 = (error_data['#Time'].values - ts[0])/1e9 +ts = (ts - ts[0])/1e9 +iStart = np.argmin(np.abs(ts - args.tStart)) +iEnd = np.argmin(np.abs(ts - args.tEnd)) +interp_error_list = [] +for label in ['Errorx','Errory','Errorz','Errorja1','Errorja2']: + interp_error_list.append(np.interp(ts, ts1, error_data[label].values)) +interp_errors = np.vstack(interp_error_list).T +xyz_ja = state_data[['x','y','z','ja1','ja2']].values +ref_xyz_ja = xyz_ja - interp_errors +# %% Plotting +sns.set_style('whitegrid') +sns.set(font_scale = 1.2) +plt.figure(1) +labels = ['X', 'Y', 'Z', 'Ja1', 'Ja2'] +units = ['m','m','m','rad', 'rad'] +legend = ['Tracked', 'Reference'] +ts_sub = ts[iStart:iEnd] +for i in range(5): + plt.figure(i+1) + plt.plot(ts_sub, xyz_ja[iStart:iEnd, i]) + plt.plot(ts_sub, ref_xyz_ja[iStart:iEnd, i]) + plt.ylabel(labels[i]+' ('+units[i]+')') + plt.xlabel('Time (seconds)') + plt.legend(legend) + plt.savefig(os.path.join(args.folder,labels[i]+'.eps'), + bbox_inches='tight') + +fig = plt.figure(6) +ax = fig.add_subplot(111, projection='3d') +ax.plot(xyz_ja[iStart:iEnd,0], xyz_ja[iStart:iEnd,1], xyz_ja[iStart:iEnd,2]) +ax.plot(ref_xyz_ja[iStart:iEnd,0], ref_xyz_ja[iStart:iEnd,1], ref_xyz_ja[iStart:iEnd,2]) +ax.legend(legend) +ax.set_xlabel('X (m)') +ax.set_ylabel('Y (m)') +ax.set_zlabel('Z (m)') +rms_errors = np.sqrt(np.mean(np.square(interp_errors[iStart:iEnd, :]), axis=0)) +np.set_printoptions(precision=2, suppress=True) +np.savetxt(os.path.join(args.folder, 'rms_errors.csv'), + rms_errors[:,np.newaxis].T, fmt='%.2f', + delimiter=',', + header='RMSX, RMSY, RMSZ, RMSJ1, RMSJ2') +print("RMS ERRORS: ", rms_errors) +# %%For 3d plot save it yourself +plt.savefig(os.path.join(args.folder,'trajectory.eps'), + bbox_inches='tight') \ No newline at end of file diff --git a/src/controller_tuners/mpc_controller_tuner.cpp b/src/controller_tuners/mpc_controller_tuner.cpp index 2e85315d..e0c90e5e 100644 --- a/src/controller_tuners/mpc_controller_tuner.cpp +++ b/src/controller_tuners/mpc_controller_tuner.cpp @@ -108,9 +108,9 @@ int main(int argc, char **argv) { visualizer_config.mutable_trajectory_color()->set_r(0.0); visualizer_config.mutable_desired_trajectory_color()->set_a(0.5); MPCTrajectoryVisualizer visualizer(controller_connector, visualizer_config); - auto reference_ptr = - createWayPoint(PositionYaw(0.2, 0.2, 0.2, 0.0), -0.8, 1.4); - // auto reference_ptr = createSpiralReference(drone_hardware); + // auto reference_ptr = + // createWayPoint(PositionYaw(0.2, 0.2, 0.2, 0.0), -0.8, 1.4); + auto reference_ptr = createSpiralReference(drone_hardware); controller_connector.usePerfectTimeDiff( 0.02); ///\todo Remove this flag business // Start drone