bresch
3 years ago
committed by
Daniel Agar
4 changed files with 295 additions and 0 deletions
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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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""" |
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Copyright (c) 2022 PX4 Development Team |
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Redistribution and use in source and binary forms, with or without |
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modification, are permitted provided that the following conditions |
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are met: |
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|
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1. Redistributions of source code must retain the above copyright |
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notice, this list of conditions and the following disclaimer. |
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2. Redistributions in binary form must reproduce the above copyright |
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notice, this list of conditions and the following disclaimer in |
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the documentation and/or other materials provided with the |
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distribution. |
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3. Neither the name PX4 nor the names of its contributors may be |
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used to endorse or promote products derived from this software |
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without specific prior written permission. |
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|
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS |
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT |
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS |
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FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE |
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COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, |
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INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, |
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BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS |
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OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED |
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AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT |
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LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN |
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ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
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POSSIBILITY OF SUCH DAMAGE. |
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|
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File: frag_fusion_symbolic.py |
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Author: Mathieu Bresciani <mathieu@auterion.com> |
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License: BSD 3-Clause |
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Description: |
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""" |
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from sympy import * |
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V = Symbol("V", real=True) |
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rho = Symbol("rho", real=True) |
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rho_n = Symbol("rho_n", real=True) |
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Mcoef = Symbol("Mcoef", real=True) |
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Bcoef = Symbol("Bcoef", real=True) |
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a = Symbol("a", real=True) |
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f1 = 0.5 * rho / Bcoef * V**2 + rho_n * Mcoef * V - a |
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print("If Bcoef > 0 and Mcoef > 0") |
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print("V =") |
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res_V = solve(f1, V) |
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res_V = res_V[0] |
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pprint(res_V) |
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print("a_pred =") |
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pprint(solve(f1, a)) |
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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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""" |
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Copyright (c) 2022 PX4 Development Team |
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Redistribution and use in source and binary forms, with or without |
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modification, are permitted provided that the following conditions |
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are met: |
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|
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1. Redistributions of source code must retain the above copyright |
||||
notice, this list of conditions and the following disclaimer. |
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2. Redistributions in binary form must reproduce the above copyright |
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notice, this list of conditions and the following disclaimer in |
||||
the documentation and/or other materials provided with the |
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distribution. |
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3. Neither the name PX4 nor the names of its contributors may be |
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used to endorse or promote products derived from this software |
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without specific prior written permission. |
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|
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS |
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT |
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS |
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FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE |
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COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, |
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INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, |
||||
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS |
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OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED |
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AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT |
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LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN |
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ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
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POSSIBILITY OF SUCH DAMAGE. |
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|
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File: drag_replay.py |
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Author: Mathieu Bresciani <mathieu@auterion.com> |
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License: BSD 3-Clause |
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Description: |
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Find the best ballistic and momentum drag coefficients for wind estimation |
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using EKF2 replay data. |
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NOTE: this script currently assumes no wind. |
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""" |
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import matplotlib.pylab as plt |
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from pyulog import ULog |
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from pyulog.px4 import PX4ULog |
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import numpy as np |
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import quaternion |
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from scipy import optimize |
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def getAllData(logfile): |
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log = ULog(logfile) |
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v_local = np.matrix([getData(log, 'vehicle_local_position', 'vx'), |
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getData(log, 'vehicle_local_position', 'vy'), |
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getData(log, 'vehicle_local_position', 'vz')]) |
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t_v_local = ms2s(getData(log, 'vehicle_local_position', 'timestamp')) |
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accel = np.matrix([getData(log, 'sensor_combined', 'accelerometer_m_s2[0]'), |
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getData(log, 'sensor_combined', 'accelerometer_m_s2[1]'), |
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getData(log, 'sensor_combined', 'accelerometer_m_s2[2]')]) |
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t_accel = ms2s(getData(log, 'sensor_combined', 'timestamp')) |
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q = np.matrix([getData(log, 'vehicle_attitude', 'q[0]'), |
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getData(log, 'vehicle_attitude', 'q[1]'), |
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getData(log, 'vehicle_attitude', 'q[2]'), |
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getData(log, 'vehicle_attitude', 'q[3]')]) |
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t_q = ms2s(getData(log, 'vehicle_attitude', 'timestamp')) |
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dist_bottom = getData(log, 'vehicle_local_position', 'dist_bottom') |
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t_dist_bottom = ms2s(getData(log, 'vehicle_local_position', 'timestamp')) |
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(t_aligned, v_body_aligned, accel_aligned) = alignData(t_v_local, v_local, t_accel, accel, t_q, q, t_dist_bottom, dist_bottom) |
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t_aligned -= t_aligned[0] |
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return (t_aligned, v_body_aligned, accel_aligned) |
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def alignData(t_v, v_local, t_accel, accel, t_q, q, t_dist_bottom, dist_bottom): |
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len_accel = len(t_accel) |
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len_q = len(t_q) |
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len_db = len(t_dist_bottom) |
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i_a = 0 |
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i_q = 0 |
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i_db = 0 |
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v_body_aligned = np.empty((3,0)) |
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accel_aligned = np.empty((3,0)) |
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t_aligned = [] |
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for i_v in range(len(t_v)): |
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t = t_v[i_v] |
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accel_sum = np.zeros((3,1)) |
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accel_count = 0 |
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while t_accel[i_a] < t and i_a < len_accel-1: |
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accel_sum += accel[:, i_a] # Integrate accel samples between 2 velocity samples |
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accel_count += 1 |
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i_a += 1 |
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while t_q[i_q] < t and i_q < len_q-1: |
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i_q += 1 |
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while t_dist_bottom[i_db] < t and i_db < len_db-1: |
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i_db += 1 |
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# Only use in air data |
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if dist_bottom[i_db] < 1.0 or accel_count == 0: |
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continue |
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qk = np.quaternion(q[0, i_q],q[1, i_q],q[2, i_q],q[3, i_q]) |
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q_vl = np.quaternion(0, v_local[0, i_v], v_local[1, i_v], v_local[2, i_v]) |
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q_vb = qk.conjugate() * q_vl * qk # Get velocity in body frame |
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vb = quaternion.as_float_array(q_vb)[1:4] |
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v_body_aligned = np.append(v_body_aligned, [[vb[0]], [vb[1]], [vb[2]]], axis=1) |
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accel_aligned = np.append(accel_aligned, accel_sum / accel_count, axis=1) |
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t_aligned.append(t) |
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return (t_aligned, v_body_aligned, np.asarray(accel_aligned)) |
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def getData(log, topic_name, variable_name, instance=0): |
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variable_data = np.array([]) |
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for elem in log.data_list: |
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if elem.name == topic_name: |
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if instance == elem.multi_id: |
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variable_data = elem.data[variable_name] |
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break |
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return variable_data |
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def ms2s(time_ms): |
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return time_ms * 1e-6 |
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def run(logfile): |
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(t, v_body, a_body) = getAllData(logfile) |
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rho = 1.15 # air densitiy |
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rho15 = 1.225 # air density at 15 degC |
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# x[0]: momentum drag, scales with v |
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# x[1]: inverse of ballistic coefficient (X body axis), scales with v^2 |
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# x[2]: inverse of ballistic coefficient (Y body axis), scales with v^2 |
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predict_acc_x = lambda x: -v_body[0] * x[0] - 0.5 * rho * v_body[0]**2 * np.sign(v_body[0]) * x[1] |
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predict_acc_y = lambda x: -v_body[1] * x[0] - 0.5 * rho * v_body[1]**2 * np.sign(v_body[1]) * x[2] |
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J = lambda x: np.sum(np.power(abs(a_body[0]-predict_acc_x(x)), 2.0) + np.power(abs(a_body[1]-predict_acc_y(x)), 2.0)) # cost function |
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x0 = [0.15, 1/100, 1/100] # initial conditions |
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res = optimize.minimize(J, x0, method='nelder-mead', bounds=[(0,1),(0,10),(0,10)], options={'disp': True}) |
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# Convert results to parameters |
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innov_var = J(res.x) / (len(v_body[0]) + len(v_body[1])) |
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mcoef = res.x[0] / np.sqrt(rho / rho15) |
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bcoef_x = 0.0 |
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bcoef_y = 0.0 |
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if res.x[1] > 1/200: |
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bcoef_x = 1/res.x[1] |
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if res.x[2] > 1/200: |
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bcoef_y = 1/res.x[2] |
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print(f"param set EKF2_BCOEF_X {bcoef_x:.1f}") |
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print(f"param set EKF2_BCOEF_Y {bcoef_y:.1f}") |
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print(f"param set EKF2_MCOEF {mcoef:.2f}") |
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print(f"/!\EXPERIMENTAL param set EKF2_DRAG_NOISE {innov_var:.2f}") |
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# Plot data |
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plt.figure(1) |
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plt.suptitle(logfile.split('/')[-1]) |
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ax1 = plt.subplot(2, 1, 1) |
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ax1.plot(t, v_body[0]) |
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ax1.plot(t, v_body[1]) |
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ax1.set_xlabel("time (s)") |
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ax1.set_ylabel("velocity (m/s)") |
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ax1.legend(["forward", "right"]) |
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ax2 = plt.subplot(2, 1, 2, sharex=ax1) |
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ax2.set_title(f"BCoef_x = {bcoef_x:.1f}, BCoef_y = {bcoef_y:.1f}, MCoef = {mcoef:.4f}", loc="right") |
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ax2.plot(t, a_body[0]) |
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ax2.plot(t, a_body[1]) |
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ax2.plot(t, predict_acc_x(res.x)) |
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ax2.plot(t, predict_acc_y(res.x)) |
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ax2.set_xlabel("time (s)") |
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ax2.set_ylabel("acceleration (m/s^2)") |
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ax2.legend(["meas_forward", "meas_right", "predicted_forward", "predicted_right"]) |
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plt.show() |
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if __name__ == '__main__': |
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import os |
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import argparse |
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# Get the path of this script (without file name) |
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script_path = os.path.split(os.path.realpath(__file__))[0] |
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# Parse arguments |
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parser = argparse.ArgumentParser( |
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description='Estimate mag biases from ULog file') |
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# Provide parameter file path and name |
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parser.add_argument('logfile', help='Full ulog file path, name and extension', type=str) |
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args = parser.parse_args() |
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logfile = os.path.abspath(args.logfile) # Convert to absolute path |
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run(logfile) |
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# PX4 Drag fusion parameter tuning algorithm |
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In PX4, drag fusion can be enabled in order to estimate the wind when flying a multirotor, assuming that the body vertical acceleration is produced by the rotors and that the lateral forces are produced by drag. |
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The model assumes a combination of: |
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1. momentum drag: created by the rotors changing the direction of the incoming air, linear to the relative airspeed. Parameter `EKF2_MCOEF` |
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2. bluff body drag: created by the wetted area of the aircraft, quadratic to the relative airspeed. Parameters `EKF2_BCOEF_X` and `EKF2_BCOEF_Y` |
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The python script was created to automate the tuning of the aforementioned parameters using flight test data. |
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## How to use this script |
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First, a flight log with enough information is required in order to accurately estimate the parameters. |
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The best way to do this is to fly the drone in altitude mode, accelerate to a moderate-high speed and let the drone slow-down by its own drag. |
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Repeat the same maneuver in all directions and several times to obtain a good dataset. |
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/!\ NOTE: the current state of this script assumes no wind. Some modifications are required to estimate both the wind and the parameters at the same time. |
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Then, install the required python packages: |
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``` |
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pip install -r requirements.txt |
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``` |
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and run the script and give it the log file as an argument: |
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``` |
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python drag_replay.py <logfilename.ulg> |
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``` |
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The estimated parameters are displayed in the console and the fit quality is shown in a plot: |
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``` |
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param set EKF2_BCOEF_X 0.0 |
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param set EKF2_BCOEF_Y 62.1 |
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param set EKF2_MCOEF 0.16 |
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/!\EXPERIMENTAL param set EKF2_DRAG_NOISE 0.31 |
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``` |
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 |
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