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https://github.com/PX4/PX4-Autopilot.git
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Tools: add drag fusion tuning script
This commit is contained in:
bresch
Daniel Agar
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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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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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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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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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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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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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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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@@ -0,0 +1,6 @@
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matplotlib==3.5.1
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numpy==1.22.2
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pyulog==0.9.0
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quaternion==3.5.2.post4
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scipy==1.8.0
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sympy==1.10.1
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