Dimensionner et faire un tracker solaire photovolatïque low tech/en

Aurelpere

https://wiki.lowtechlab.org/wiki/Dimensionner_et_faire_un_tracker_solaire_photovolat%C3%AFque_low_tech/en

Dernière modification le 03/08/2024

Difficulté

Hard Difficile

Durée

4 hour(s) heure(s)

Coût

600 EUR (€)

Description

Building a photovoltaic tracker from a plate lifter

Sommaire

tracker, photovoltaique, phtv, dimensionnement

---

Matériaux

tracker:

BRD plate lifter: 150€

hydraulic cylinder "Actionneur linéaire 12V DC , 1320LBS(6000N) 20 pouces (500mm) moteur électrique"  : 69€ on aliexpress, available on amazon a bit more expensive

Hbridge L298n 7a: 11€ delivered on aliexpress ("Moteur d'entrainement PWM 160W 7A 12V 24V, Module de commande L298, Signal de commande logique, optocoupleur, frein")

2 chain cogs 92T: "Pignon arrière 25H JO98/108/138 maillons 55T 65T 68T 70T 80T 92T, pour 47CC 49CC Mini Moto RL facades D343 Pit Pocket Bike" : 40€

2 cogs "Pignon pour scooter électrique 8T 9T 11T 13T 25H 410 420, pour moteur à courant continu 25H JOMotor MY1020 BM1109 MY1016Z MY1018"

10€

2 cogs "Pignon de moteur électrique pour Pit-Bike, pignon de moteur à courant continu, pièces RL, D343, 9T, 11T, 13T, 25H, JOMotor 25H"

4€

Engine "Moteur à engrenages CC à vis sans fin autobloquant, couple de bain, moteur de boîte de vitesses turbo en métal, inversé, basse vitesse, DC 12V, 24V, 200kg.cm" 62€

Module

Photovoltaic module Voltech 2mx1m 375W: 200€

Control:

raspberry: environ 100€

Outils

facom measuring tape: 20€

sovietic mass kit: 60€

spirit level: 5€

bracket: 6€

grinder: 50€

drill: 50€

welding machine: 100€

Étape 1 - Sizing : moment of inertia measurement

We set two axis for our module:

one axis Oz perpendicular to the module plane (vertical when the module is flat) and one axis Ox paralelle to the module plane (horizontal when the module is flat)

Caractéristics of the module:

mass: m=21,2 kg

Length: L=1,8m

Width: 1m

Wikipedia theory tells us intertia moment along Oz axis is :

Jdelta=1/12*m*L

Jdelta=1/12*1,8*21,2=3,18Nm

Nb: we suppose the module is a bar because here the rotation axis is the axis paralell to the plane of the rectangle formed by the module and not the perpendicular axis

We now experimentally verify:

Center the module on the plate lifter and put it horizontally

verify there is no wind

check the level on which the mast of the plate lifter is put

fix a horizontal lanmark at the bottom of the module

set a 500g mass at the extremity of the module

measure the distance between the position at equilibrium and the position with the mass

We have:

Jdelta=d*F

with d distance in meter to the axis of rotation

F force applied to the solid (here mg with m solid mass in kg and g gravitational constant)

We then have

Jdelta=0,9*0,5*9,8=4,41Nm

We measure the rotation: in our case, the distance of rotation at the extremity of the module varies between 3 and 10cm. The important variations

are due to frictions on the axis that can force or slide more freely

The order of magnitude of the inertia momen is verified

For the moment of inertia along axis Ox, it is more difficult to verify experimentally, because the axise of the plate lifter doesnt allow

to have a position at equilibrium with the module mass (which will finish on the stop under its own weight)

Therefore, we will accept the theory:

The theory says

Jdelta=1/12*m(b²+c²) with m mass of the module, b lenght of the short side of the module and c length of the long side of the module

Jdelta=1/12*21,2*(1,8²+1²)=7,49NM

This theoretical result is strongly lower than the weigth of the module, so we will size based on the necessary force to lift the weigth of the module (the axis being submitted to the weight):

F=mg=21,2*9,8

In order of magnitude 200Nm (1m of lever arm in order of magnitude)

We have now the caracteristics to size the engines of our module along two rotation axis

But the trackers have a consequent wind exposure

If we want to take into account the resistance to the wind, we must measure the force applied to the module according to wind speed:

Fp​=1/2*ρ*v²*S*Cp

With ​ρ air density equal to 1,2 kg/m3 in order of magnitude for standard temperature and pressure conditions

v wind speed in m/s

S object surface in m²

Cp pressure coefficient without dimension égal to 2 for a rectangular metal plate

We have:

Fp=1/2*1,2*1,8*2*v²=2,16*v²

Chatgpt can give us the abacus of wind speeds in km/h and their conversions in m/2 and the generic name in meteorology

Calm: Less than 1 km/h (Less than 0.3 m/s)

Very light breeze: 1-5 km/h (0.3-1.5 m/s)

Light breeze: 6-11 km/h (1.6-3.0 m/s)

Gentle breeze: 12-19 km/h (3.4-5.4 m/s)

Moderate breeze: 20-28 km/h (5.5-7.9 m/s)

Fresh breeze: 29-38 km/h (8.0-10.7 m/s)

Strong breeze: 39-49 km/h (10.8-13.8 m/s)

Moderate wind: 50-61 km/h (13.9-16.9 m/s)

Near gale: 62-74 km/h (17.2-20.6 m/s)

Gale: 75-88 km/h (20.8-24.4 m/s)

Strong gale: 89-102 km/h (24.7-28.3 m/s)

Storm: 103-117 km/h (28.6-32.5 m/s)

Hurricane: At least 118 km/h (At least 32.8 m/s)

So we have a Force Fp that can vary from

An order of magnitude of 20N for a ligth breeze

to

An order of magnitude of 2000N for a storm

(NB: the gravity force of 1kg is approximately 10N so the force of 2000N corresponds in order of magnitude to the gravity force of 200kg).

The inertia moments on the axis are of the same order of magnitude (20Nm and 2000Nm) because the dimensions of the module are an order of magnitude of 1 m

If you want to build a tracker that can resist to storm conditions, it is advised to size the tracker consequently

On the one hand with sufficient ties in the ground, and on the other hand with an adapted frame -see caravan tips at the end of this stage-, and deactivate

when there are stronger winds.)

Wind resistance sizing is one of the reasons trackers are expensive et less generalized than standard photovoltaic installations

The step motors and servomotors that have an adequate torke to resist to important winds are expensive, et that can be understandable for motors designed for step precision

(For example here: https://www.distrelec.fr/fr/automatisation/moteurs-et-entrainements/moteurs-pas-pas-et-servocommandes/c/cat-L3D_525513 )

For hydraulic cylinders, the driving force is generally in orders of magnitude fitting to resist to storms.

For our low-tech tutorial, we know that core drilling machines have torque in an order of magnitude fitting to resist to storms (1W corresponds to 1 N multiplying 1 meter per second, so a core drilling machine of 2000W should have an adequate torque of more or less 2000Nm).

After verification on the technical caracteristics of drills, impact screwdriver, and after a manual verification of resistances (brake,clutch) when the engine is not powered, this type of engine doesnt fit.

To size a resistance to winds between the storm and hurricane we will proceed differently:

We find on aliexpress a affordable prices (70€) step motors or endless screw motors with torque in an order of magnitude of 20Nm(200kgcm). We will use thi engine with two reductors 1:10 to get a resisting torque of 2000Nm.

Recall: the torque expressed in Nm is a rotation force produced by the motor that can be computed with the same principle of the inertia moment: the force in newton x the distance to the rotation axis

Belt or chain transmission permit to reduce this torque, and that can be calculated very easily

C1=(R1/R2)*C2

With C1 torque on pulley 1

R1 pulley 1 radius

C2 torque on pulley 2

R2 pulley 2 radius

The radius is directly proportional to the number of teeth of a gear wheel (a bike cog or a motorcycle cog for example), we can easily calculate the transmission reduction by dividing the number of teeth of the big cog by the number of teeth of the little cog (verify they have the same teeth standards)

Therefore a transmission 80 teeth/10 teeth (80T/10T) will produce a reduction of 1:8, like here on amazon for an electric bike ("Keenso Kit Chaîne et Pignon 80T 25H 34mm 3 Trous Pignon 10T H Trou Chaîne Pignon 146 Maillons Chaîne") for 30€.

We will notice that a standard bike transmission have a maximum transmission reduction of 50 teeth/11 teeth so a reduction of 1:5, which is not enough for efficient reduction witouth multiplying pulleys

It is difficult to find transmissions with reduction of 1:10, but we find some on aliexpress and we will easily adapt a bicycle drive on which we will weld cogs, which will allow us a reduction of 1:100 with only one axis added in addition to the principal axis and the engine axis

So we will use an engine step motor or an endless screw engine (resistance unpowered test at reception) commanded by a raspberry pi (but the plate lifter has wheels, so we will take the precaution to put away the tracker when there's a storm ;))

Carvan tips: to size the width of the metal frame, modern norms dont seem to be really made on scientific basis but on commercial basis. We will therefore measure the width of old material (made in the 70s). For example, the frame of this old caravan accredited on its registration document for a 750kg weigth has a metal frame of 5mm width. On can then make a rule of 3 to verify the width of our frame fits.

This is rustic style (strenght of material calculs is rigorous and complex), but it allows to have a firest approximation "a visto de nas" as we say in gascon

Étape 2 - Engine resistance test

See videos

The rotative engine ("Moteur à engrenages CC à vis sans fin autobloquant, couple de bain, moteur de boîte de vitesses turbo en métal, inversé, basse vitesse, DC 12V, 24V, 200kg.cm" on aliexpress) takes well 20Nm being powered and not being powered.

Recall: torque = moment of intertia generated by the motor

=force*distance to motor axis

here 4kg at 0,5m=9,8*4*0,5=20Nm (in order of magnitude)

Étape 3 - Sizing: measurement of angles and displacement

We will first look at the solar trajectory

We measure typically the sun position along two coordinate systems:

the equatorial system with coordinates expressed in:

right ascension equivalent to terrestrial longitude measured in hours minutes seconds

declination equivalent to terrestrial latitude measured in degrees minutes seconds

The horizontal system with coordinates expressed in:

azimut degree

altitude degree or height

The solar trajectory abacus (for example available here https://www.astrolabe-science.fr/diagramme-solaire-azimut-hauteur ) give us the sun trajectories in a day (generally several typical days of several seasons) expressed in horizontal degrees.

To read this type of grap

If we follow the graph inserted in this tutorial and from the link above, when we follow for example the red curb for paris, we can read :

The 21st of december, when we look at the south, the sun follows a trajectory that starts at -50° azimut (along the East on the horizontal axis) at dawn, then when the sun goes west during the day (we follow the red curb), it takes height until it reaches 17° height (on the vertical axis) at noon (position 0° azimut on the horizontal axis) and then goes down again to 0° height when it sets (along the West on the horizontal axis)

For Paris, we have:

One degree azimut varying from -130° to +130° according to the hour and the season

One degree altitude or height varying from 0° to 64° according to the hour and the season

For our tracker,

To calculate our horizontal displacement (along the Oz vertical axis if the module is put down on the ground), on don't really have constraints on the plate lifter used since the axis turns 360° without problems.So we can follow the sun from -130° azimut to +130° azimut without problem.

To calculate our vertical displacement (along the Ox horizontal axis if the module is put on the ground), we have a constraint on the maximal angle.

I dont have a decimeter at hand, so I will use pythagore (see photo):

64cm*150cm*134cm

socatoa:

sinus phi=opposé/hypothenus

sinus phi=134/150

sinus phi=134/150

phi=1,1046 rad

phi=1,1046*180/pi=63°

We have a constrainte for our plate lifter accepting angles along the Ox axis from 0° to 63°

When the tracker is at its maximum angle (63°), we are perpendicular to the sun when the sun is at an angle phi of phi=180-63-90=27°

When the sun has an angle lower than 27°, the tracker can not follow and keep being perpendicular to the sun

We see however that the stop is guaranteed by the spring (on the photo we sse the mark on the paint). We can win a bit of amplitude on the stop by drilling and making a notch in the jib).

The manual measurement of the displacement between the tube axis on which is fixed the crank handle and the back of the module when the plate lifter leans at its maximum angle gives us 42cm. (see photo)

Étape 4 - Install the hydraulic cylinder on the horizontal axis

We will fix a jib on the fix part of the plate lifter that turns with the vertical axis, so we can fix a rod on which we will fix the hydraulic cylinder which will be able to turn with the vertical axis so it can adjust the angle on the horizontal axis.

We begin by fixing the metal jib welding to the fix part regarding the vertical axis. It requires to sand the paint before the welding is done (see photo). We do arc welding here.

Then we drill a metal rod we will screw to the jib (see photo).

We drill et we fix a metal rod we screw to the mobile part that allows to adjust the angle on the horizontal axis, ie the arm that permits to carry the plate or the photovoltaic module (see photo).

We then fix the hydraulic cylinder to the two metal rods. The hydraulic cylinder is equiped with fixing that adjust to the angle taken by the rods on which it is fixed (see photo)

Notice we have a metal rod with a right angle that avoid this fix is totally free. It will stop on a part of the rod, which permits then to calibrate more precisely the hydraulic cylinder amplitude).

We then test the course of the hydraulic cylinder. We will pay attention, because the stop of the plate lifter corresponds to a 40cm course for the hyrdaulic cylinder and it can extend up to 50cm. (see photos)

Étape 5 - Raise the plate lifter maximum angle

After observing the critical elements, we will modify the jib to avoid a stop when the sun has a altitude degree lower than 27°.

To do so, we will:

-let the spring go by scooping the jib (to avoid the spring be a stop)

-raise the angle by lowering the fix of the spring drilling the jib

-raise the angle cutting the edges of the jib

See photos:

1/2: observation top/below

3/4: jib disassembly

4/5: obersvation below, maximum angle at square

We reach an maximum angle of almost 90° and we can completely follow the sun!

Étape 6 - Test the hydraulic cylinder and rotation engine commands

To control the hydraulic cylinder, we will use a rapsberry pi, the most widespread monocard computer. It is equiped with a serie of 40 pins, we can connect devices to, called GPIO controller.

A first reading of a few tutorials and available libraries to use it a some time taken to test wrong hypothesis to install correctly using the good versions leads me to talk a few possible options:

-operating system:

• dietpi images (debian) available here: https://dietpi.com/#download (see my other tutorial for how to activate wifi : https://wiki.lowtechlab.org/wiki/Serveur_orangepi-raspberry_nextcloud_en_photovolta%C3%AFque_autonome)

• rapsberry pi os (default) images available here: https://www.raspberrypi.com/software/operating-systems/

Archives and operating systems versions usefull for retrocompatibility (every reader caring for low tech computer is encouraged to keep local copies of these archives and to share them in torrent!):

*dietpi:

https://dietpi.com/downloads/images/

*raspberry pi os:

https://downloads.raspberrypi.org/raspbian/images

si vous utilisez wheezy,

echo "deb http://legacy.raspbian.org/raspbian/ wheezy main contrib non-free rpi" >> /etc/apt/sources.list

NB: ChatGPT gives us the following rapsberry release dates:

1. Raspberry Pi Model B : 29 february 2012
2. Raspberry Pi Model A : 4 february 2013
3. Raspberry Pi Model B+ : 14 july 2014
4. Raspberry Pi Model A+ : 10 november 2014
5. Raspberry Pi 2 Model B : 2 february 2015
6. Raspberry Pi Zero : 30 november 2015
7. Raspberry Pi 3 Model B : 29 february 2016
8. Raspberry Pi Zero W : 28 february 2017
9. Raspberry Pi 3 Model B+ : 14 march 2018
10. Raspberry Pi 3 Model A+ : 15 november 2018
11. Raspberry Pi 4 Model B : 24 june 2019
12. Raspberry Pi 400 : 2 november 2020
13. Raspberry Pi Pico : 21 january 2021
14. Raspberry Pi Zero 2 W : 28 october 2021

We hope this is true (it comes from chatgpt), but you can verify on what's written on the pcb of your card.

To verify the version of your raspberry under raspberry pi os if you dont know what version it is (see photo):

sudo usermod -a G gpio pi
pinout

Adjust with the operating system that you think is relevant based on your relevancy filters. You have a list of old os versions of dietpi and raspberry pi os in case new versions would now ork anymore (and i repeat: every reader caring of low tech computer is encouraged to download and keep local copies of these archives and share them in torrent!)

To install, as usually, download balenaetcher, flash a usb key with the donwloaded image, boot. The default login/password on dietpi are root/dietpi and for raspberry pi os pi/raspberry (take care to the default qwerty on raspberry pi os at boot). To configure keyboard, locales, timezone and wifi, do:

sudo dietpi-config

under dietpi and

sudo raspi-config

under raspberry pi.

-driver used to control the gpio

• RPi.GPIO(independant dev) or RPi.GPIO2(redhat dev, recent repo)

Failure with pip and pypi repository (compilation errors etc.). Install as root with command

sudo -s 

The simpler is to install a precompiled version with apt:

sudo apt install python3-rpi.gpio

do a test to see if that works well (as root):

python3
import RPi.GPIO

See the sourceforge documentation, more explicit than the pypi one: http://sourceforge.net/p/raspberry-gpio-python/wiki/Home/

• gpiozero

under dietpi do

sudo apt install python3 python3-venv python3-pip
python3 -m venv venv
source venv/bin/activate
pip install lgpio gpiozero

The first point to understand is the numbering of the pins: The pins have all a number that goes from 1 to 40 following an order from bottom to top and from left to right, and each pin has also a gpiio number which is different from the pin number.

To do so, we can find information on internet: https://wiki.lowtechlab.org/wiki/Serveur_orangepi-raspberry_nextcloud_en_photovolta%C3%AFque_autonome or type the command under raspberry pi os (see image)

sudo usermod -aG gpio votre_user
pinout

In this tutorial, wether for gpiozero or RPi.GPIO, we will use the GPIO numbering and not the pin numbering

We plug the GPIO 2 and 4 (+5V) to the +5V pins of the HBridge We plug the GPIO 6 and 7 (Ground) to the GND pin of the HBridge We plug the GPIO 23 and 16 to the switch 3 of the HBridge To test the "enable" of the HBridge, we plug the GPIO 20 and 21 to EAN of HBridge

We then use the following scripts:

import time
import RPi.GPIO as GPIO
import gpiozero

def forwardzero(wait):
    led16=gpiozero.LED(16) #motor1
    led23=gpiozero.LED(23) #motor1
    led20=gpiozero.LED(20) #enable
    led21=gpiozero.LED(21) #enable
    led1=gpiozero.LED(1)
    led7=gpiozero.LED(7)
    try:
        print("forwardzero")
        #case where Hbridge needs a enable signal
        led21.off()
        led20.on()
        #zero signal off on switch 2
        led1.off()
        led7.off()
        #positive signal off on switch 1
        led23.on()
        led16.off()
        #let signal active during wait time
        for k in range(wait):
            print(k)
            time.sleep(1)
        #put signal off on the switch
        led23.off()
        led20.off()
    except KeyboardnInterrupt:
        print("keyboard interrupt")
    except Exception as err:
        print(err)
    finally:
        print("zero")
        #zero signal off on switch 2
        led1.off()
        led7.off()
        #signal off on switch 1
        led16.off()
        led23.off()
        #signal off on enable pin
        led20.off()
        led21.off()
def backwardzero(wait):
    led1=gpiozero.LED(1)
    led7=gpiozero.LED(7)
    led16=gpiozero.LED(16) #motor1
    led23=gpiozero.LED(23) #motor1
    led20=gpiozero.LED(20) #enable
    led21=gpiozero.LED(21) #enable
    try:
        print("backwardzero")
        #signal off on switch 1
        led16.on()
        led23.off()
        #case where Hbridge needs a enable signal
        led20.off()
        led21.on()
        #signal on switch 2
        led1.off()
        led7.off()
        #wait
        for k in range(wait):
            print(k)
            time.sleep(1)
        #signal off on switches
        led16.off()
        led21.off()
    except KeyboardInterrupt:
        print("keyboardinterrupt")
    except Exception as err:
        print(err)
    finally:
        print("zero")
        #signal off on switch 1
        led16.off()
        led23.off()
        #signal off on switch 2
        led1.off()
        led7.off()
        #signal off on enable pin
        led20.off()
        led21.off()

#pin16 gpio23
#pin36 gpio25

def forward(wait):
    GPIO.setmode(GPIO.BCM)
    GPIO.setup(16,GPIO.OUT) #motor1
    GPIO.setup(23,GPIO.OUT) #motor1
    GPIO.setup(20,GPIO.OUT) #enable
    GPIO.setup(21,GPIO.OUT) #enable
    GPIO.setup(1,GPIO.OUT)  #motor2
    GPIO.setup(7,GPIO.OUT)  #motor2
    try:
        print("forward")
        GPIO.output(1,GPIO.HIGH)
        GPIO.output(7,GPIO.LOW)
        GPIO.output(21,GPIO.HIGH)
        GPIO.output(20,GPIO.LOW)
        GPIO.output(23,GPIO.LOW)
        GPIO.output(16,GPIO.LOW)
        for k in range(wait):
            print(k)
            time.sleep(1)
        GPIO.output(1,GPIO.LOW)
        GPIO.output(21,GPIO.LOW)
    except KeyboardInterrupt:
        print("keyboard interrupt")
    except Exception as err:
        print(err)
    finally:
        print("zero")
        GPIO.output(1,GPIO.LOW)
        GPIO.output(7,GPIO.LOW)
        GPIO.output(20,GPIO.LOW)
        GPIO.output(21,GPIO.LOW)
        GPIO.output(23,GPIO.LOW)
        GPIO.output(16,GPIO.LOW)
        #GPIO.cleanup() on my sbc it doesnt remove the +3V
def backward(wait):
    GPIO.setmode(GPIO.BCM)
    GPIO.setup(20,GPIO.OUT) #enable
    GPIO.setup(21,GPIO.OUT) #enable
    GPIO.setup(1,GPIO.OUT)  #motor2
    GPIO.setup(7,GPIO.OUT)  #motor2
    GPIO.setup(16,GPIO.OUT) #motor1
    GPIO.setup(23,GPIO.OUT) #motor1
    try:
        print("backward")
        GPIO.output(21,GPIO.HIGH)
        GPIO.output(20,GPIO.LOW)
        GPIO.output(16,GPIO.LOW)
        GPIO.output(23,GPIO.LOW)
        GPIO.output(7,GPIO.HIGH)
        GPIO.output(1,GPIO.LOW)
        for k in range(wait):
            print(k)
            time.sleep(1)
        GPIO.output(7,GPIO.LOW)
        GPIO.output(21,GPIO.HIGH)
    except KeyboardInterrupt:
        print("keyboardinterrupt")
    except Exception as err:
        print(err)
    finally:
        print("zero")
        GPIO.output(20,GPIO.LOW)
        GPIO.output(21,GPIO.LOW)
        GPIO.output(16,GPIO.LOW)
        GPIO.output(23,GPIO.LOW)
        GPIO.output(1,GPIO.LOW)
        GPIO.output(7,GPIO.LOW)
        #GPIO.cleanup() on my sbc it doesnt remove the +3V
forward(2) #rotation horary motor2
backward(2) #rotation antihorary motor2
forwardzero(10) #hydraulic cylinder extension motor1 111 max
backwardzero(10) #hydraulic cylinder retractation motor1 107 max

GPIO.BCM allows to use GPIO numbering GPIO.HIGH sends a +3V signal in the concerned pin GPIO.LOW sets the signal to 0V (GND) gpiozero does the same with .on() and .off() methods

Update 31.05.24: you were waiting for it, here's today update with the HBridge made in Europe. It works, see video! Update of calibration quickly at recepetion of something to measure angles a bit more handy.

Notice the pins 20 and 21 are not plugged to the HBridge. The "PWM" (pulse width modulation) is not used to "activate" (enable in the code) the engine because jumbers on ENA pins of the Hbridge are enough (here we dont need to adjust motor speed)

Étape 7 - Hydraulic cylinder for vertical axis rotation

Fix pulleys and 1:100 transmission for rotation motor

We begin by recycling two bycicle drives from second hand bikes we cut with a grinder taking care to keep the metal rod of the frame.

we will weld 92T cogs on it.

We then weld the 8T cogs on it.

We weld a 8T cog on a locked cog adapted to the motor axis.

We cut a metal rod and we weld a flat metal plate along the axis of the plate lifter on which we will screw the bike metal rods attached to the bike drives and alos a metal rod on which we will fix the motor.

We assemble and we screw.

Update when receiving the 3rd chain and waiting how to fix the chain tensions.

Update of 11.6.24: the constraints of the axis of the plate lifter make it difficult to fix a big cog to get a good reduction on the last transmission chain. Even we have several reductions with other pulleys, the plate lifter is not rotating (see video).

update of 16.6.24: additional hydraulic cylinder ordered. Estimated delivery 28 juin-2juillet

We will make the rotation with two hydraulic cylinders. Update when received at the end of june (not available these next weeks anyway).

The chain tension is bad, we replace the tranmissions with a hydraulic cylinder allowing a rotation but with a reduced angle (about 70° because it is not a 2 or 3 parts telescopic cylinder)

Étape 8 - Engine calibration

The updated code is as follow: we define dictionnaries associating each sought angle to an activation time of the engine (each must be manually tested and measured)

#Calibration
#dict_angle_rotation= sun_degre_azimut:motoractivationtime
dict_angle_verin={1:0,
                2:0,
                3:0,
                4:0,
                5:0,
                6:0,
                7:0,
                8:0,
                9:0,
                10:0,
                11:1,
                12:1,
                13:2,
                14:3,
                15:4,
                16:5,
                17:6,
                18:7,
                19:8,
                20:9,
                21:10,
                22:11,
                23:12,
                24:13,
                25:14,
                26:15,
                27:16,
                28:16,
                29:17,
                30:18,
                31:19,
                32:20,
                33:21,
                34:22,
                35:23,
                36:24,
                37:25,
                38:27,
                39:28,
                40:29,
                41:30,
                42:32,
                43:33,
                44:34,
                45:35,
                46:37,
                47:38,
                48:39,
                49:41,
                50:42,
                51:44,
                52:45,
                53:47,
                54:48,
                55:50,
                56:51,
                57:52,
                58:54,
                59:56,
                60:58,
                61:59,
                62:59,
                63:59,
                64:60,
                65:63,
                66:64,
                67:65,
                68:66,
                69:68,
                70:68,
                71:71,
                72:73,
                73:75,
                74:77,
                75:79,
                76:81,
                77:82,
                78:83,
                79:85,
                80:86,
                81:87,
                82:89,
                83:91,
                84:94,
                85:96,
                86:98,
                87:100}
dict_angle_rotation={
                0:41,
                1:42,
                2:43,
                3:44,
                4:45,
                5:46,
                6:47,
                7:48,
                8:49,
                9:50,
                10:52,
                11:53,
                12:54,
                13:55,
                14:56,
                15:57,
                16:58,
                17:59,
                18:60,
                19:61,
                20:63,
                21:64,
                22:65,
                23:66,
                24:67,
                25:68,
                26:69,
                27:70,
                28:71,
                29:72,
                30:73,
                31:75,
                32:76,
                33:77,
                34:78,
                35:79,
                36:82,
                37:82,
                38:82,
                39:82,
                40:82,
                41:82,
                42:82,
                43:82,
                44:82,
                45:82,
                46:82,
                47:82,
                -1:39,
                -2:37,
                -3:35,
                -4:33,
                -5:32,
                -6:30,
                -7:29,
                -8:27,
                -9:26,
                -10:25,
                -11:23,
                -12:22,
                -13:19,
                -14:18,
                -15:17,
                -16:16,
                -17:15,
                -18:14,
                -19:6,
                -20:2,
                -21:1,
                -22:0,
                }

Étape 9 - Hard coding the tracking

The updated code is as follows:

import time
import RPi.GPIO as GPIO
import gpiozero

def forwardzero(wait):
    led16=gpiozero.LED(16) #motor1
    led23=gpiozero.LED(23) #motor1
    led20=gpiozero.LED(20) #enable
    led21=gpiozero.LED(21) #enable
    led1=gpiozero.LED(1)
    led7=gpiozero.LED(7)
    try:
        print("forwardzero")
        #case where Hbridge needs a enable signal
        led21.off()
        led20.on()
        #zero signal off on switch 2
        led1.off()
        led7.off()
        #positive signal off on switch 1
        led23.on()
        led16.off()
        #let signal active during wait time
        for k in range(wait):
            print(k)
            time.sleep(1)
        #put signal off on the switch
        led23.off()
        led20.off()
    except KeyboardnInterrupt:
        print("keyboard interrupt")
    except Exception as err:
        print(err)
    finally:
        print("zero")
        #zero signal off on switch 2
        led1.off()
        led7.off()
        #signal off on switch 1
        led16.off()
        led23.off()
        #signal off on enable pin
        led20.off()
        led21.off()
def backwardzero(wait):
    led1=gpiozero.LED(1)
    led7=gpiozero.LED(7)
    led16=gpiozero.LED(16) #motor1
    led23=gpiozero.LED(23) #motor1
    led20=gpiozero.LED(20) #enable
    led21=gpiozero.LED(21) #enable
    try:
        print("backwardzero")
        #signal off on switch 1
        led16.on()
        led23.off()
        #case where Hbridge needs a enable signal
        led20.off()
        led21.on()
        #signal on switch 2
        led1.off()
        led7.off()
        #wait
        for k in range(wait):
            print(k)
            time.sleep(1)
        #signal off on switches
        led16.off()
        led21.off()
    except KeyboardInterrupt:
        print("keyboardinterrupt")
    except Exception as err:
        print(err)
    finally:
        print("zero")
        #signal off on switch 1
        led16.off()
        led23.off()
        #signal off on switch 2
        led1.off()
        led7.off()
        #signal off on enable pin
        led20.off()
        led21.off()

#pin16 gpio23
#pin36 gpio25

def forward(wait):
    GPIO.setmode(GPIO.BCM)
    GPIO.setup(16,GPIO.OUT) #motor1
    GPIO.setup(23,GPIO.OUT) #motor1
    GPIO.setup(20,GPIO.OUT) #enable
    GPIO.setup(21,GPIO.OUT) #enable
    GPIO.setup(1,GPIO.OUT)  #motor2
    GPIO.setup(7,GPIO.OUT)  #motor2
    try:
        print("forward")
        GPIO.output(1,GPIO.HIGH)
        GPIO.output(7,GPIO.LOW)
        GPIO.output(21,GPIO.HIGH)
        GPIO.output(20,GPIO.LOW)
        GPIO.output(23,GPIO.LOW)
        GPIO.output(16,GPIO.LOW)
        for k in range(wait):
            print(k)
            time.sleep(1)
        GPIO.output(1,GPIO.LOW)
        GPIO.output(21,GPIO.LOW)
    except KeyboardInterrupt:
        print("keyboard interrupt")
    except Exception as err:
        print(err)
    finally:
        print("zero")
        GPIO.output(1,GPIO.LOW)
        GPIO.output(7,GPIO.LOW)
        GPIO.output(20,GPIO.LOW)
        GPIO.output(21,GPIO.LOW)
        GPIO.output(23,GPIO.LOW)
        GPIO.output(16,GPIO.LOW)
        #GPIO.cleanup() on my sbc it doesnt remove the +3V
def backward(wait):
    GPIO.setmode(GPIO.BCM)
    GPIO.setup(20,GPIO.OUT) #enable
    GPIO.setup(21,GPIO.OUT) #enable
    GPIO.setup(1,GPIO.OUT)  #motor2
    GPIO.setup(7,GPIO.OUT)  #motor2
    GPIO.setup(16,GPIO.OUT) #motor1
    GPIO.setup(23,GPIO.OUT) #motor1
    try:
        print("backward")
        GPIO.output(21,GPIO.HIGH)
        GPIO.output(20,GPIO.LOW)
        GPIO.output(16,GPIO.LOW)
        GPIO.output(23,GPIO.LOW)
        GPIO.output(7,GPIO.HIGH)
        GPIO.output(1,GPIO.LOW)
        for k in range(wait):
            print(k)
            time.sleep(1)
        GPIO.output(7,GPIO.LOW)
        GPIO.output(21,GPIO.HIGH)
    except KeyboardInterrupt:
        print("keyboardinterrupt")
    except Exception as err:
        print(err)
    finally:
        print("zero")
        GPIO.output(20,GPIO.LOW)
        GPIO.output(21,GPIO.LOW)
        GPIO.output(16,GPIO.LOW)
        GPIO.output(23,GPIO.LOW)
        GPIO.output(1,GPIO.LOW)
        GPIO.output(7,GPIO.LOW)
        #GPIO.cleanup() on my sbc it doesnt remove the +3V

#Etalonnage
#dict_angle_verin= sun_degre_horizontal:motoractivationtime

#dict_angle_rotation= sun_degre_azimut:motoractivationtime
dict_angle_verin={1:0,
                2:0,
                3:0,
                4:0,
                5:0,
                6:0,
                7:0,
                8:0,
                9:0,
                10:0,
                11:1,
                12:1,
                13:2,
                14:3,
                15:4,
                16:5,
                17:6,
                18:7,
                19:8,
                20:9,
                21:10,
                22:11,
                23:12,
                24:13,
                25:14,
                26:15,
                27:16,
                28:16,
                29:17,
                30:18,
                31:19,
                32:20,
                33:21,
                34:22,
                35:23,
                36:24,
                37:25,
                38:27,
                39:28,
                40:29,
                41:30,
                42:32,
                43:33,
                44:34,
                45:35,
                46:37,
                47:38,
                48:39,
                49:41,
                50:42,
                51:44,
                52:45,
                53:47,
                54:48,
                55:50,
                56:51,
                57:52,
                58:54,
                59:56,
                60:58,
                61:59,
                62:59,
                63:59,
                64:60,
                65:63,
                66:64,
                67:65,
                68:66,
                69:68,
                70:68,
                71:71,
                72:73,
                73:75,
                74:77,
                75:79,
                76:81,
                77:82,
                78:83,
                79:85,
                80:86,
                81:87,
                82:89,
                83:91,
                84:94,
                85:96,
                86:98,
                87:100}
dict_angle_rotation={
                0:41,
                1:42,
                2:43,
                3:44,
                4:45,
                5:46,
                6:47,
                7:48,
                8:49,
                9:50,
                10:52,
                11:53,
                12:54,
                13:55,
                14:56,
                15:57,
                16:58,
                17:59,
                18:60,
                19:61,
                20:63,
                21:64,
                22:65,
                23:66,
                24:67,
                25:68,
                26:69,
                27:70,
                28:71,
                29:72,
                30:73,
                31:75,
                32:76,
                33:77,
                34:78,
                35:79,
                36:82,
                37:82,
                38:82,
                39:82,
                40:82,
                41:82,
                42:82,
                43:82,
                44:82,
                45:82,
                46:82,
                47:82,
                -1:39,
                -2:37,
                -3:35,
                -4:33,
                -5:32,
                -6:30,
                -7:29,
                -8:27,
                -9:26,
                -10:25,
                -11:23,
                -12:22,
                -13:19,
                -14:18,
                -15:17,
                -16:16,
                -17:15,
                -18:14,
                -19:6,
                -20:2,
                -21:1,
                -22:0,
                }
def sun_position(time_now,lat,lon):
    now_here = ephem.Observer()
    now_here.lat = lat
    now_here.lon = lon
    #PyEphem only processes and returns dates that are in Universal Time (UT), which is simliar to Standard Time in Greenwich, England, on the Earth's Prime Meridian
    # Europe/Paris is GMT+2
    utc_now=datetime.datetime.utcnow()
    #is_dst=datetime.datetime(year=utc_now.year,month=utc_now.month,day=utc_now.day).dst()
    #time_diff=datetime.timedelta(hours=(1 if not is_dst else 2))
    now_here.date = time_now #+datetime.timedelta(hours=time_diff) #'2007/10/02 00:50:22'
    sun.compute(now_here)
    sun_degre_azimut=int(sun.az*180/3.141592653589793)
    sun_degre_horizontal=int(sun.alt*180/3.141592653589793)
    return(sun_degre_horizontal,sun_degre_azimut)

tracker_degré_horizontal=0
tracker_degré_azimut=0
def init():
    global tracker_degré_horizontal
    forwardzero(111)
    tracker_degré_horizontal=0
def track(time_now,lat,lon):
    global tracker_degré_horizontal
    global tracker_degré_azimut
    init()
    (sun_degre_horizontal,sun_degre_azimut)=sun_position(time_now,lat,lon)
    sun_degre_azimut=min(sun_degre_azimut,87)
    sun_degre_horizontal=max(sun_degre_horizontal,-22)
    sun_degre_horizontal=min(36,sun_degre_horizontal)
    backwardzero(dict_angle_verin[sun_degre_horizontal])
    tracker_degré_horizontal=sun_degre_horizontal
    backward(82)
    forward(dict_angle_rotation[sun_degre_azimut-tracker_degré_azimut])
    tracker_degré_azimut=sun_degre_azimut
#test Agen
lat=44.2
lon=0.6
backward(4)
forward(4) #placer le tracker direction sud angle horizontal
backwardzero(4)
forwardzero(41)
#time.sleep(100)
while True:
    track(datetime.datetime.now(),lat,lon)
    time.sleep(20*60) #activer le tracking toutes les 20 minutes

Étape 10 - AI Coding the tracking

We will now code a lowtech AI for the educational part (Machine Learning here, as massively used since about fifteen years in many industries, ie no AI in the chatgpt sense as it is usually understood in 2024). We could say lowtech AI is AI with results not being magic, where data and code are open source, not stolen, where data belong to us, by us and for us, and on which we are in control of play (this last point is essential but the experience to step out of the line drives me to pessimism on this matter because testing if there is interference in an machine learning result is difficult to detect)

We plug a webcam, we record the images as input data, we process the image to make a digit table corresponding to variables with which on wee seek to correlate with a positive or negative signal (turn the engine in one direction, or stop)

Here, the input data are only the 640*480*3=921600 variables of pixels of the images in the video (921600 colunns/variables per line, from which we seek to correlate with a positive signal 1 or 0 of the last column).

To have the tracker work, this method will not fits well, it would require to do "feature engineering" (complicated name to say add columns of variables more probably correlated to the positive signal) by adding luminosity and/or date and time, on video samples covering all seasons on many years.

If you want to learn the basics of AI on which this code relies, I recommand the course "Applied Data Science with Python" of michigan university in which you will learn python, pandas and machine learning basics "no bullshit".

import cv2
import time
import numpy as np
import os
import argparse
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import sklearn.model_selection
import sklearn.metrics
import sklearn.decomposition
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier

def enregistrer_video(out_file,fps,capture_duration):
    # Open the webcam
    cap = cv2.VideoCapture(0)  # Use 0 for default webcam

    fourcc = cv2.VideoWriter_fourcc(*'XVID')  # Codec (e.g., XVID)

    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))  # Get webcam frame width
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))  # Get webcam frame height

    # Check if webcam opened successfully
    if not cap.isOpened():
        print("Error opening webcam")
        exit()

    # Create the VideoWriter object
    out = cv2.VideoWriter(out_file, fourcc, fps, (frame_width, frame_height))

   # Start time for tracking duration
    start_time = time.time()
    while time.time() - start_time < capture_duration:
        # Capture frame-by-frame
        ret, frame = cap.read()

       # Check if frame captured successfully
        if not ret:
            print("Error capturing frame")
            break

        # Write the frame to the video file
        out.write(frame)
    
        # Display the captured frame (optional)
        cv2.imshow('Webcam Video', frame)
    
        # Check if the user wants to quit (press 'q')
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

        # Close resources
    cap.release()
    out.release()
    cv2.destroyAllWindows()

    print(f"1 minute video saved successfully as {out_file}!")

def charger_images_video(video_filename):
  """
  Load video images from a file

  Args:
      video_filename: Le chemin d'accès au fichier vidéo.

   Returns:
      a numpy table with video images (3D array: frames, rows, cols).
  """

  # Ouvrir la vidéo avec OpenCV
  cap = cv2.VideoCapture(video_filename)

  # Vérifier l'ouverture réussie
  if not cap.isOpened():
      print("Erreur d'ouverture du fichier vidéo:", video_filename)
      return None

  # Liste vide pour stocker les images vidéo
  images_list = []

  # Lire les images vidéo image par image
  while True:
      ret, frame = cap.read()

      # Vérifier la lecture de l'image
      if not ret:
          break

          
          # Convertir l'image en nuance de gris (optionnel pour la normalisation)
      # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Décommenter si nécessaire

      # Ajouter l'image à la liste
      images_list.append(frame)

      # Fermer la capture vidéo
  cap.release()

  return images_list

def flatten_images_video(images_array):
    
    # Initier une liste de resultat
    result=[]
    
    # Iterer sur le numpy array en input
    for k in images_array:
        result.append(k.flatten())
    # convertir la liste np.array
    result=np.asarray(result)

    return result

# Exemple d'utilisation
# Define video parameters
out_file = "webcam_video_1min.mp4"  # Output video filename
fps = 20.0  # Frames per second
capture_duration = 60  # Seconds
#enregistrer_video(out_file,fps,capture_duration)
images_video = charger_images_video("webcam_video_1min.mp4")  # Fonction pour charger les images vidéo
print(images_video)
print(len(images_video))

# Flattening and normalizing
images_video = flatten_images_video(images_video)
print(images_video[0])
print(len(images_video[0]))

# compression/normalisation
scaling_factor = 1.0 / 255.0  # Divide by 255 to normalize between 0 and 1
images_video = images_video * scaling_factor
print(images_video[0])
print(len(images_video[0]))

# checking data shape
#np.set_printoptions(threshold=np.inf)  # Set threshold to infinity
#print(np.array2string(images_video[0], suppress_small=True))
#print(len(images_video[0]))

# Get the array shape
image_shape = images_video.shape

# Print the dimensions
print("Image shape", image_shape)
print("Number of dimensions:", len(image_shape))
print("Image height:", image_shape[0])   
print("Image width:", image_shape[1])
print("Number of color channels:", image_shape[2])

# Total number of elements (height x width x color channels)
total_elements = image_shape[0] * image_shape[1] * image_shape[2]
print("Total elements:", total_elements)

# dataset creation to say "yes" to turn left (for example)
# NB: it's at this stage that tech giants employ kenyans at very low wages
# in a form of modern slavery
# it's about defining, for each image, if we must activate the motor 
# on the loeft (ie defining a positive signal for the machine to correlate positively with this image)

positives=np.zeros_like(images_video)
#If first 14 images define a positive signal, we will do:
#in reality it would require to process video intervals positively defining 
#for each image those where we associate a positive signal 
positives[0:14] = 1

class ML():
    @staticmethod
    def ml(X,y,classifier):
        "process machine learning on X data set, y yes/no data with classifier"
        # dataset
        #X = images_video
        #y = positives
        # classifier model training
        clf = ML.dico_classifier[classifier]()
        X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
            X, y, random_state=0)
        clf.fit(X_train, y_train)
        # predictions
        _predicted = clf.predict(X_test)
        # scores
        _accuracy, _precision, _recall = ML.compute_scores(y_test, _predicted,
                                                        classifier)
        # confusion matrix
        ML.compute_confusion_matrix(y_test, _predicted, _accuracy, classifier)
        # courbes precision-recall
        ML.plot_precision_recall(clf, X_test, y_test, classifier, _predicted)
        # roc
        roc_auc_clf = ML.plot_roc(clf, X_test, y_test, classifier)[0]
        return pd.DataFrame(data=(_accuracy, _precision, _recall, roc_auc_clf),
                            index=['accuracy', 'precision', 'recall', 'AUC'],
                            columns=[classifier])

    @staticmethod
    def compute_scores(y_test, _predicted, classifier):
        "compute machine learning scores"
        _accuracy = sklearn.metrics.accuracy_score(y_test, _predicted)
        _precision = sklearn.metrics.precision_score(y_test, _predicted)
        _recall = sklearn.metrics.recall_score(y_test, _predicted)
        print(str(classifier) + ' Accuracy: {:.2f}'.format(_accuracy))
        print(str(classifier) + ' Precision: {:.2f}'.format(_precision))
        print(str(classifier) + ' Recall: {:.2f}'.format(_recall))
        return (_accuracy, _precision, _recall)

    @staticmethod
    def compute_confusion_matrix(y_test, _predicted, _accuracy, classifier):
        "compute confusion matrix"
        confusion_clf = sklearn.metrics.confusion_matrix(y_test, _predicted)
        df_clf = pd.DataFrame(confusion_clf,
                              index=list(range(0, 2)),
                              columns=list(range(0, 2)))
        plt.figure(figsize=(5.5, 4))
        ax_heatmap=sns.heatmap(df_clf, annot=True, vmin=0, vmax=11, cmap="Blues")
        plt.title(str(classifier) + ' \nAccuracy:{0:.3f}'.format(_accuracy))
        plt.ylabel('True label')
        plt.xlabel('Predicted label')
        return df_clf,ax_heatmap
    @staticmethod
    def plot_precision_recall(clf, X_test, y_test, classifier, _predicted):
        "plot precision recall curve"
        _precision = sklearn.metrics.precision_score(y_test, _predicted)
        _recall = sklearn.metrics.recall_score(y_test, _predicted)
        y_score_clf = clf.predict_proba(X_test)
        y_score_df = pd.DataFrame(data=y_score_clf)
        precision, recall, thresholds = sklearn.metrics.precision_recall_curve(
            y_test, y_score_df[1])
        closest_zero = np.argmin(np.abs(thresholds))
        closest_zero_p = precision[closest_zero]
        closest_zero_r = recall[closest_zero]
        plt.figure()
        plt.xlim([0.0, 1.01])
        plt.ylim([0.0, 1.01])
        result,=plt.plot(precision, recall)
        plt.title(
            str(classifier) +
            ' Precision-Recall Curve \nprecision :{:0.2f}'.format(_precision) +
            ' recall: {:0.2f}'.format(_recall))
        plt.plot(closest_zero_p,
                 closest_zero_r,
                 'o',
                 markersize=12,
                 fillstyle='none',
                 c='r',
                 mew=3)
        plt.xlabel('Precision', fontsize=16)
        plt.ylabel('Recall', fontsize=16)
        plt.show()
        return result
    @staticmethod
    def plot_roc(clf, X_test, y_test, classifier):
        "plot roc curve"
        y_score_clf = clf.predict_proba(X_test)
        y_score_df = pd.DataFrame(data=y_score_clf)
        fpr_clf, tpr_clf, _ = sklearn.metrics.roc_curve(y_test, y_score_df[1])
        roc_auc_clf = sklearn.metrics.auc(fpr_clf, tpr_clf)
        plt.figure()
        plt.xlim([-0.01, 1.00])
        plt.ylim([-0.01, 1.01])
        result,=plt.plot(fpr_clf,
                 tpr_clf,
                 lw=3,
                 label=str(classifier) +
                 ' ROC curve (area = {:0.2f})'.format(roc_auc_clf))
        plt.xlabel('False Positive Rate', fontsize=16)
        plt.ylabel('True Positive Rate', fontsize=16)
        plt.title('ROC curve ' + str(classifier) +
                  ' \nAUC:{0:.3f}'.format(roc_auc_clf),
                  fontsize=16)
        plt.legend(loc='lower right', fontsize=13)
        plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')
        plt.show()
        return roc_auc_clf,result

        dico_classifier = { 'knn': KNeighborsClassifier,
                        'naiveb': GaussianNB,
                        'randomforest': RandomForestClassifier,
                        'gtree': GradientBoostingClassifier,
                        'neural': MLPClassifier}

    @staticmethod
    def plot_heatmap(dataframe):
        "plot heatmap of accuracy, precision, recall, AUC"
        plt.figure()
        sns.heatmap(dataframe, annot=True, vmin=0, vmax=1, cmap="Blues")
        plt.title('scores des classifiers ')
        plt.ylabel('scores')
        plt.xlabel('modeles')
        plt.show()

#process machine learning for all classifiers in dico_classifier
#and plot a heatmap of their accuracy, precision, recall, AUC
df_result = pd.DataFrame(data=(0, 0, 0, 0),
                                 columns=['init'],
                                 index=['accuracy', 'precision', 'recall', 'AUC'])
for clf in ML.dico_classifier:
    print(clf)
    result_ml = ML.ml(images_video, positives, clf)
    df_result = pd.merge(df_result,
                                 result_ml,
                                 right_index=True,
                                 left_index=True)
df_result.drop('init', axis=1, inplace=True)
ML.plot_heatmap(df_result)
return df_result

Conclusion: Here, now you know how to code using AI, you can critize it better et promote low tech adequately

You will notice AI algorithms are open source and easy to use as simple "dev user".

And also that without data, AI is absolutely useless.

That's the reason why big tech giants want more and more data and employ peolple in slavery conditions in many countries to process these data before training their models.

Just like for "personal data", the key question here with AI relies on data.

See the excellent conference of Benjamin Bayart "Geopolitique de la data (Benjamin BAYART)" on youtube or in video in this tutorial.

You can make a lowtech tracker, but also adapt this code to create an autonomous vehicle lowtech with 4 distinct datasets/positive signals to train activation of "turn left", "accelerate", "brake", "turn right". This is what George Hotz did claiming it required only 30 hours of videos of driving and recorded sensors to have the needed positives signals correlated with the recorded images to have autonomous driving work.

Of course, we strongly hope there won't be hack or that the system doesnt have a distant command and control on this type of algorithm.

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