5.10. Regulator

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From the course by Moscow Institute of Physics and Technology

Building Arduino robots and devices

135 ratings

Moscow Institute of Physics and Technology

Building Arduino robots and devices

135 ratings

For many years now, people have been improving their tools, studying the forces of nature and bringing them under control, using the energy of the nature to operate their machines. Last century is noted for the creation of machines which can operate other machines. Nowadays the creation of devices that interact with the physical world is available to anyone. Our course consists of a series of practical problems on making things that work independently: they make their own decisions, act, move, communicate with each other and people around, and control other devices. We will demonstrate how to assemble such devices and programme them using the Arduino platform as a basis. After this course, you will be able to create devices that read the data about the external world with a variety of sensors, receive and forward this data to a PC, the Internet and mobile devices, and control indexing and the movement. The creation of such devices will involve design, the study of their components, the assemblage of circuit boards, coding and diagnostics. Along with the creation of the devices themselves, you will perform visualization on a PC, create a web page that will demonstrate one of your devices, and figure out how an FDM 3D-printer is configured and how it functions. Besides those keen on robotics or looking to broaden their horizons and develop their skills, the course will also be useful to anyone facing the task of home and industrial automation, as well as to anyone engaged in industrial design, advertising and art. The course does not require any special knowledge from the participants and is open even to students of upper secondary school. Programming skills and the level of English allowing to read technical documentation would be an advantage, but this is not obligatory. The entire course is dedicated to practice, so the best way for you would be to get hold of some electronics, follow the illustrated examples and experiment on your own. The kits can be purchased here: kits.cyberphysica.ru. Taught by: Alexey Perepelkin, head of Robotics department in the Laboratory of innovative educational technologies at MIPT Taught by: Dmitry Savitsky, researcher in the Laboratory of innovative educational technologies at MIPT

From the lesson

Week 5.

Let’s turn one wheel and then two wheels at once, and the robot car will start moving. It’ll be moving along the line or under your control. It could as well be just messing with your hand with which you are trying to control it.

Meet the Instructors

Алексей Перепелкин
Руководитель направления развития цифрового творчества
Центр инновационных образовательных технологий МФТИ
Дмитрий Савицкий
Научный сотрудник
Лаборатория инновационных образовательных технологий МФТИ

Let’s consider the robot’s movement from a different point of view.

Which parameter of the robot’s movement is the key one?

Naturally, this is the velocity at the turning point.

This means that at every moment the difference between the speed of both wheels has to be

optimal.

If we try to picture the relay algorithm in a diagram,

we will get something like this.

If we lay off the difference of the speed along the vertical axis,

we will have three possible situations:

two sensors with the white background, and white and black or black and white

backgrounds respectively.

Thus, there will be only three possible options of the difference in the speed of the wheels.

It can be zero, positive or

negative.

Of course, in such a case all the turns will be sharp.

What can we do to make them less sharp?

Obviously, we can regulate the speed of the wheels more swiftly.

You remember that we are using an analog line sensor,

which means that apart from black and white, we can also see all their shades.

Let’s look at one of the sections of our track.

We need to install the sensor above

the borderline.

Thus, if we imagine the patch of IR light created by the sensor,

it will appear like this.

We can see that the circle contains half of the black and half of the white backgrounds, but in terms of

the sensor, it will give us the “grey” value, as it sees

the circle as one big dot.

Therefore, if the circle moves a bit to the left,

the sensor will receive absolute white.

Vice versa, if the circle moves to the right, the sensor will be getting absolute black.

Besides, it will receive a multitude of grey values, where, according to the sensor,

the intensity of grey will always differ.

Thus, we can keep in mind the intensity of

grey that we need to achieve when we work with the sensors.

Now let’s picture this in a diagram.

We shall once again lay off the difference in the speed of the wheels on the vertical axis,

and the difference between the current value of the sensor and the desired one,

which we measured at the beginning, on the horizontal axis.

I’ll call it δS, for instance.

We have a straight line passing through the 0 point.

Thus, if there is no difference between the current and the desired values of the sensor,

we don’t need this difference between the speed of the wheels.

If we have this difference then our

wheels will be turning at different speeds.

By the way, negative values are possible here, because if we, say,

extract the value that we are getting on the black background (700, for instance) from the desired

value of 300, we will get a negative value of δS.

The difference in the speed of the wheels will also be negative.

To count this difference, we need to extract the speed of the right

wheel from the speed of the left wheel.

Therefore, if the speed of the right wheel is higher than the speed of the left wheel,

the difference will be negative.

And there we go, we have achieved what we wanted.

The speed is regulated in proportion to the changing of the intensity of grey

under the sensor, i.e. the difference in the speed of the wheels.

How can we describe it in our algorithm?

Let’s have a look at the code.

Firstly, at the beginning of the program,

in the setup, we will remember the desired value – the intensity of grey, which

we will be trying to achieve, and then save it in the “grey” value.

Then, every time we run the main loop, we will be

calculating the so-called error,

which consists of the difference between the current value under the sensor and

the desired value that we saved.

This difference is denoted in the δS variable, which we saw in the previous graph.

In fact, errors are usually denoted as “e”.

When we know that there is an error, we need to calculate the control action and find out

what effect this is going to have on the axis of our graph.

Control action is usually denoted as “u”

and in our case, it consists solely of an error

taken with some coefficient, i.e. the slope angle of the line.

This is obvious, because we read off line sensor values in standard units,

which are in the same range.

At the same time, we control our motors by using other standard units from a different range, that’s why we need a

certain coefficient to compare and contrast

the units of the measurement of “grey” and the units of the speed of the wheels.

This is that K, which we defined at the beginning of the program.

Next, we are going to call the “drive” function.

We can see a certain macrodefinition called BASE_SPEED.

This is the desired speed which we will be trying to reach.

We want both our wheels to have this speed.

Consequently, when the robot needs to turn,

we will have to add the control action to the speed of one wheel,

and extract it from the speed of the other wheel.

There is nothing strange in that, because, as we remember,

control action can take negative values.

This is when the current value of the sensor is higher that the desired average value.

Let’s fast forward a bit and imagine

what else we might need.

As we know, when we turn the robot on by connecting the power,

it starts moving forward.

We will need to add a button, by pressing which we will start the robot.

This is the first thing.

The second thing is that we would

better choose the average value of grey for our robot when we run the algorithm.

Therefore, it would be good to see what values the sensor is reading off.

For this purpose, we are going to add a 4-digit display to our robot.

Before we start the robot, we will need to see to which position it is better to set the sensor.

How will the code change after all this?

Firstly, we can’t forget about the library, which would allow us to work with the display.

Secondly, you might have noticed that we only have one sensor left now.

We actually don’t need the second one in our algorithm.

We have connected the button and the display,

set the base speed for the motors, and

introduced the K coefficient with a

certain conditional value of 0.2.

I will explain later how to choose these values.

So how do we start our robot now?

Let’s access the “while” loop in the setup.

The loop will be running until we press the button.

In other words, the robot will not move until we start it.

While the cycle is running, we will be outputting the values that the

sensor captures on our display.

Me might want to add a short delay,

so that the digits on the display wouldn’t change that fast, and we would have enough time to read them.

We are also going to read off the value that we will be trying to

reach while the program is running.

When we leave the loop, the value will be saved in the “grey” variable.

Now our robot looks this this.

It has a display, which outputs the readings of the sensor.

We have left only one sensor and placed it on the right side.

For this reason, the code has changed a bit, but we will talk about it later.

We also have the above-mentioned button, with the help of which we can start the robot.

Let me say a couple of words about the sensor and why it is installed this way.

I have initially placed it a bit lower, but it dawned upon me that it was a bad solution, since

the circle that the sensor captures is too small.

That’s why all the changes that happen under the sensor are

happening too fast according to the sensor itself.

Thus, the black has appeared and it has filled the whole circle.

This doesn’t allow us to solve the problem of fine balancing.

For this reason, I have placed the sensor a bit higher so that it would have more space underneath.

There is one more problem though concerning the additional lighting in our studio,

which is quite abundant and which clutters the sensor.

That’s why we need to create some sort of a shield for it in order to get rid

of this irritating illumination.

Now let’s have a look at the changes in our code.

First thing, I’ve substituted all the references to the left sensor and its

pin with the same information, but on the right sensor.

In other words, all LEFT_SENSOR_PIN and other similar things become RIGHT_SENSOR_PIN.

It also seems plausible to add a delay at the end of the setup so that the

robot takes some time before it starts moving and doesn’t rip our fingers out with its promptitude.

Most importantly, our error character has changed.

We used to extract the objective value from the value output by the left sensor,

and now we have to extract it from the value of the right sensor.

That’s why our signs will change when we call the “drive”.

Thus, the robot will be able to react to these changes.

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