edxmorgan

"""
Example of how to set target speed in guided mode with pymavlink
"""

import time
import math
# Import mavutil
from pymavlink import mavutil
# Imports for attitude
from pymavlink.quaternion import QuaternionBase

edxmorgan

# Import mavutil
from pymavlink import mavutil
from pymavlink.quaternion import QuaternionBase
import math
import time
import sys
ALT_HOLD_MODE = 2

def is_armed():
    try:

edxmorgan

#!/usr/bin/env python 

# Script to set target depth in Ardusub 3.6 beta
# requires this commit: https://github.com/williangalvani/ardupilot/commit/85a41d5771ba9d64e594dcc0856b6a3906767c3f

from pymavlink import mavutil

# There is very likely a builtin in pymavlink for this, but I didn't find it
ALT_HOLD_MODE = 2

edxmorgan

% MATLAB Script for Simulating PFC Controller from Example 4.4

% Clear workspace and command window
clear;
close all;
clc;

% Sampling time
Ts = 3;  % seconds

edxmorgan

#!/usr/bin/env python3
import cv2
import gi
import numpy as np
import json

gi.require_version('Gst', '1.0')
from gi.repository import Gst

class Video():

edxmorgan

% MATLAB Code for Offset-Free MPC Control of Heated Swimming Pool System

% Clear workspace and command window
clear; close all; clc;

% System Parameters
A = 0.7788;
B = 0.0442;
C = 1;       % Output matrix

edxmorgan

% MATLAB Code for Offset-Free MPC Control of Heated Swimming Pool System

% Clear workspace and command window
clear; close all; clc;

% System Parameters
A = 0.7788;
B = 0.0442;
C = 1;       % Output matrix

edxmorgan

#!/usr/bin/env python3
''' Mavlink telemetry (.tlog) file parser.

    Operates as a generator. Allows csv output or listing useful types/fields.
'''

import json
from pathlib import Path
from fnmatch import fnmatch
from pymavlink import mavutil

edxmorgan

I, i_X_p = rig_dyn.model()
Ic_i = []
for i in range(0, ss.n_joints):
    Ic_i.append(I[i])
for i in range(ss.n_joints-1, -1, -1):
    if i != 0:
        p_X_i_f = pluck.inverse_spatial_transform(i_X_p[i]).T
        Ic_i[i-1] = Ic_i[i-1] + p_X_i_f@Ic_i[i]@i_X_p[i]

edxmorgan

import numpy as np
import matplotlib.pyplot as plt

class BayesianParameterEstimator:
    def __init__(self, param_min=0.0, param_max=5.0, num_points=500, known_variance=1.0):
        """
        Initialize the Bayesian estimator.
        
        :param param_min: Minimum value of the parameter space
        :param param_max: Maximum value of the parameter space