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In order to design behaviors or controllers for, for robots, we inevitably

need models of how the robots actually behave. And we're going to start with one

of the most common models out there, which is the model of a differential drive

mobile robot. So, differential drive wheeled mobile robot has two wheels and

the wheels can turn at different rates and by turning the, the wheels at different

rates, you can make the robot move around. So, this is the robot we are going to

start with and the reason for it is because it is extremely common. In fact,

the Khepera 3, which is the robot that we are going to be using quiet a lot in this

course is a differential drive wheeled mobile robot. But a lot of them out there

are, in fact, differential drive robots. Typically, they have the two wheels and

then a caster wheel in the back. and the way these robots work is you have the

right wheel velocity that you can control and the left wheel with velocity that you

can't control. So, for instance, if they're turning at the same rate, the

robot is moving straight ahead. If one wheel is turning slower than another, then

you're going to be turning towards the direction in which the slower wheel is.

So, this a way of actually being able to, to make the robot more round. So, let's

start with this kind of robot and see what does a robot model actually look like.

Well, here's my cartoon of the robot. The circle is the robot and the black

rectangles are supposed to be the wheels. The first thing we need to know is what

are the dimensions of the robot. And I know I've said that a good controller

shouldn't have to know exactly what particular parameters are because

typically dont know what the friction coeficcient is. Well, in this case, you

are going to need to know two parameters. And one parameter you need to know is the

wheel base, meaning how far away are the wheels from each other? We're going to

call that L. So, L is the wheel base of the robot. You're also going to need to

know the radius of the wheel, m eaning how big are the wheels? We call that capital

R. Now, luckily for us, these are parameters that are inherently easy to

measure. You take out the ruler and you measure it on your robot. But these

parameters will actually play a little bit of a role when we're trying to, to design

controllers for these robots. Now, that's the cartoon of the robot. What is it about

the robot that we want to be able to control? Well, we want to be able to

control how the robot is moving. But, at the end of the day, the control signals

that we have at our disposal are v sub r, which is the rate at which the right wheel

is turning. And v sub l, which is the rate at which the left wheel is turning. And

these are the two inputs to our system. So, these are the inputs, now, what are

the states? Well, here's the robot. Now, I've drawn it as a triangle because I want

to stress the fact that the things that we care about, typically, for a robot is,

where is it, x and y. It's the position. And which direction is it heading in? So,

phi is going to be the heading or the orientation of the robot. So, the things

that we care about are where is the robot, and in which direction is it going? So,

the robot model needs to connect the inputs, which is v sub l and v sub r, to

the states, somehow. So, we need some way of doing this transition. Well, this is

not a course on kinematics. So, instead of me spending 20 minutes deriving this,

voila, here it is. This is the differential drive robot model. It tells

me how vr and vl translates into x dot, which is, how does the x position of the

robot change? Or to y dot, which is how is the y position, or phi dot, meaning how is

the robot turning? So, this is a model that gives us what we need in terms of

mapping control inputs onto states. The problem is, that it's very cumbersome and

unnatural to think in terms of rates of various wheels. If I asked you, how should

I drive to get to the door, you probably not going to tell me how what v sub l and

v sub r are, your probably g oing to tell me don't drive too fast and turn in this

direction. Meaning, you're giving me instructions that are not given in terms

of v sub l and v sub r, which is why this model is not that commonly used when

you're designing controllers. However, when you implement them, this is the model

you're going to have to use. So, instead of using the differential drive model

directly, we're going to move to something called the unicycle model. And the

unicycle model overcomes this issue of dealing with unnatural or unintuitive

terms, like wheel velocities. Instead, what it's doing is it's saying, you know

what, I care about position. I care about heading, why don't I just control those

directly? In the sense that, let's talk about the speed of the robot. How fast is

it moving? And how quickly is it turning, meaning the angular velocity? So,

translational velocity, speed, and angular velocity is how quickly is the robot

turnings. If I have that my inputs are going to be v, which is speed, and omega,

which is angular velocity. So, these are the two inputs. They're very natural in

the sense that we can actually feel what they're doing which, we typically can't

when we have vr and vl. So, if we have that, how do we map them on to the actual

robot. Well, the unicycle dynamics looks as follows, x dot is v cosine phi. The

reason this is right is, if you put phi equal to 0, then cosine phi is 1. In this

case, x dot is equal to v, which means that your moving in a straight line, in

the x-direction, which makes sense. Similarly for y, so y dot is v sine phi

and phi dot is omega because I'm controlling the heading directly or the,

the, the, the rate at which the heading is changing directly. So, this model is

highly useful, we're going to be using it quite a lot which is why it deserves one

of the patented sweethearts. Okay, there is a little bit of problem though because

this is the model we're going to design our controllers for, the unicycle model.

Now, this model is not the differential drive wheele d model, this is. So, we're

going to have to implement it on this model and now, here we have v and omega.

These are our, the, the control inputs we're going to design for. But here, v sub

r and v sub l are the actual control parameters that we have. So, we somehow

need to map them together. Well, the trick to doing that is to find out that this x

dot, that's the same as this x dot, right? They're the same thing. This y dot is the

same as the other y dot. So, if we just identify the two x dots together, then

divide it by cosine 5, we actually get that the velocity v is simply r over 2, v

sub r plus v sub l or 2v over r is vr plus vl. So, this is an equation that connects

v, which is the translational velocity or the speed, to these real velocities. And

we do the same thing for omega. We get this equation. So, only l over r is vr

minus vl. Now, these are just two linear equations, we can actually solve these

explicitly for v sub r and v sub l and if we do that, we get that v sub r is this

thing and v sub l is this other thing. But the point now is, this is what I designed

for, this is what I designed for. So, v and omega are design parameters. l and r

are my known measured parameters for the robot, the base of the robot, meaning how

far the wheels are apart, and the radius of the wheel. And with these parameters,

you can map your designed inputs, v and omega, onto the actual inputs that are

indeed running on the robot. So, this is step 1, meaning we have a model. Now, step

2 is, okay, how do we know anything about the world around us?