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來自 University of Pennsylvania 的課程

Robotics: Aerial Robotics

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University of Pennsylvania

Robotics: Aerial Robotics

1853 個評分

課程 1（共 6 門，Specialization Robotics）

How can we create agile micro aerial vehicles that are able to operate autonomously in cluttered indoor and outdoor environments? You will gain an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three dimensional environments. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. Finally, you will gain insights through seeing real world examples of the possible applications and challenges for the rapidly-growing drone industry. Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations. Programming prerequisites: Some experience programming with MATLAB or Octave is recommended (we will use MATLAB in this course.) MATLAB will require the use of a 64-bit computer.

從本節課中

Introduction to Aerial Robotics

Welcome to Week 1! In this week, you will be introduced to the exciting field of Unmanned Aerial Robotics (UAVs) and quadrotors in particular. You will learn about their basic mechanics and control strategies and realize how careful component selection and design affect the vehicles' performance. This week also provides you with instructions on how to download and install Matlab. This software will be used throughout this course in exercises and assignments, so it is strongly recommended to familiarize yourself with Matlab soon. Tutorials to help you get started are also provided in this week.

Basic Mechanics4:27

Dynamics and 1-D Linear Control10:29

Design Considerations3:37

Design Considerations (continued)4:33

Agility and Maneuverability7:00

Component Selection4:24

Effects of Size4:38

Supplementary Material: Introduction3:19

Supplementary Material: Dynamical Systems3:15

Supplementary Material: Rates of Convergence3:20

與講師見面

Vijay Kumar
Nemirovsky Family Dean of Penn Engineering and Professor of Mechanical Engineering and Applied Mechanics
School of Engineering and Applied Science

In the previous segment,

we looked at control using a very simple idealized model.

What's wrong with that idealized model?

The biggest thing that's wrong is that we assumed that the motors were capable of

producing whatever thrust the controller required.

So in reality, if you look at this model, the motor thrusts are limited,

because the motors have a limited capacity.

So if I write down this equation again, and look at forces in the vertical

direction, clearly the thrusts have to compensate for the weight, and

the thrust that exceeds the weight, will produce a quadratic acceleration.

But the thrust that the motor can produce is limited by the peak torque.

So let's assume that this peak torque is known to us, and

that in turn determines the maximum thrust we can produce, T sub-max.

This in turn determines the maximum acceleration.

If you look at that model, u, the control input,

is now determined by the sum of the motor thrust and the weight.

And of course, this has to be a vector sum.

You have to remember that the thrust points in the opposite direction

to the weight.

Assuming you know what Tmax is, you can calculate umax

by simply taking the maximum thrust and adding it to the weight.

Again, remember that the thrust is in the vertical direction pointing up.

The weight is in the vertical direction pointing down.

And you really need to take the vector sum to get umax.

So now when we do PD control, u(t)

is determined not just by the proportion of the derivative control law.

But also the maximum thrust that can be applied.

So if you take the minimum of these two functions,

that'll give you the true value of the controlled input that can be applied.

The same for the PID control.

I'm gonna now show you two videos.

The only thing that's different in the two videos

is the assumed value of maximum thrust.

On the left side, the maximum thrust to weight ratio is 2.

And on the right side the maximum thrust to weight ratio is 1.2.

So these two videos or

simulations illustrate the differences between using two different motors.

Or you could ask the question,

what happens if you keep the motors the same, but change the payload?

Again, it's only the thrust to weight ratio that changes.

And you qualitatively get different performances,

as you can see in these simulations.

Now, what I want you to do is to use the same simulator,

using the control law we had before.

And study how changing the thrust to weight ratio

effects the response of the quadrotor.

Change the mass or the payload of the robot and see how the response changes.

Using this simulation,

you should also be able to determine the maximum payload that the robot can carry.

Such that the performance is within acceptable levels.

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