dayjaby

#include <iostream>

template<bool B, class T, class F>
struct conditional { typedef T type; };
 
template<class T, class F>
struct conditional<false, T, F> { typedef F type; };

template <typename... Ts>
struct largest_type;

dayjaby

# Mount Mixer (e.g. Gimbal, servo-controlled gimbal, etc...)


# pitch
M: 1
S: 2 1  10000  10000      0 -10000  10000

# roll
M: 1
S: 2 0  10000  10000      0 -10000  10000

dayjaby

#!/usr/bin/env python

import sys, os
from optparse import OptionParser
import time
from datetime import datetime
import calendar

# tell python where to find mavlink so we can import it
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../mavlink'))

dayjaby

# BabyShark VTOL mixer for PX4FMU
# 1-4 (main): Ailerons (Y-cable), A-tail left, Pusher, A-tail right
# 5-8 (main): quad motors
#=============================

Quad motors 1 - 4
-----------------
R: 4x 10000 10000 10000 0

Aileron mixer (roll)

dayjaby

python-dict_to_protobuf:
  ubuntu:
    bionic:
      pip:
        packages: [dict_to_protobuf]
    focal:
      pip:
        packages: [dict_to_protobuf]
python-huawei-modem-api-client:
  ubuntu:

dayjaby

import rospy
import numpy as np
from mavros_msgs.msg import ActuatorControl
from geometry_msgs.msg import PoseStamped
from tf.transformations import euler_from_quaternion

class Gimbal_test():

    def __init__(self,
                 ):

dayjaby

tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a

dayjaby

libqt4:
  debian: [libqtcore4]
python-neo4j:
  debian:
    pip:
      packages: [neo4j]
  ubuntu:
    pip:
      packages: [neo4j]

dayjaby

r'''
==================================
Kalman Filter tracking a sine wave
==================================

This example shows how to use the Kalman Filter for state estimation.

In this example, we generate a fake target trajectory using a sine wave.
Instead of observing those positions exactly, we observe the position plus some
random noise.  We then use a Kalman Filter to estimate the velocity of the

dayjaby

def distance_in_direction(origin, distance, direction):
    lat1, lon1 = origin
    lat1 = math.radians(lat1)
    lon1 = math.radians(lon1)
    r = 6371000.0
    lat2 = math.asin(math.sin(lat1)*math.cos(distance/r)+math.cos(lat1)*math.sin(distance/r)*math.cos(math.radians(direction)))
    lon2 = lon1 + math.atan2(math.sin(direction)*math.sin(distance/r)*math.cos(lat1), math.cos(distance/r)-math.sin(lat1)*math.sin(lat2))
    return math.degrees(lat2), math.degrees(lon2)