The document discusses setting up a simulated drone flying championship to evolve effective drone controllers using evolutionary algorithms. It proposes using a drone simulation environment like UAV Playground to test controller designs in silico rather than with real drones. Competitors would develop controllers using machine learning techniques like genetic algorithms and submit them to the simulation to accomplish tasks like navigation. Evolving controllers in simulation allows many designs to be tested cheaply and safely before deploying to real drones.

1. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
THE SIMULATED DRONE FLYING
CHAMPIONSHIP
Muhammad Adil Raja
Roaming Researchers
cbna

2. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
OUTLINE
1 INTRODUCTION
2 EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP
3 HOW TO EVOLVE CONTROLLERS
4 EVOLUTIONARY ALGORITHM
5 SIMULATION ENVIRONMENTS

3. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
OUTLINE
1 INTRODUCTION
2 EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP
3 HOW TO EVOLVE CONTROLLERS
4 EVOLUTIONARY ALGORITHM
5 SIMULATION ENVIRONMENTS

4. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
OUTLINE
1 INTRODUCTION
2 EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP
3 HOW TO EVOLVE CONTROLLERS
4 EVOLUTIONARY ALGORITHM
5 SIMULATION ENVIRONMENTS

5. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
OUTLINE
1 INTRODUCTION
2 EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP
3 HOW TO EVOLVE CONTROLLERS
4 EVOLUTIONARY ALGORITHM
5 SIMULATION ENVIRONMENTS

6. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
OUTLINE
1 INTRODUCTION
2 EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP
3 HOW TO EVOLVE CONTROLLERS
4 EVOLUTIONARY ALGORITHM
5 SIMULATION ENVIRONMENTS

7. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
INTRODUCTION
Drone planes are becoming common by the day.
They have a wide variety of applications.
Applications domains range from security apparatus to
precision agriculture.
There should be a way to design efficient, smart and
human-competitive, self-coordinating, intelligent
cooperative drones.
Such drones should be present in real-world as well as in
artificial reality environments.
And there should be ways to design, test and evolve such
drones in simulators.
One of the ways to develop such technology is design a
competition for drone planes.
In this competitors may be solicited to submit novel
designs of drones (or fleets of drones) that perform certain
user specified tasks.

8. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP I
My inspiration for the simulated drone flying championship
ensues from my interest in the simulated car racing
championship.
The latter competition is overseen and managed by the
GECCO (Genetic and Evolutionary Computation
Conference) and the wider community of evolutionary
algorithms practitioners.
The simulated car racing championship provides its
participants a software which is essentially a simulation
environment for racing cars, or car racing.

9. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP II
The simulation environment has everything in it including
environments for car racing, such as different types of laps
on different types of terrains, different types of cars with
different specifications etc.
It also has a so called physics engine which emulates
other environmental factors for the car racing simulator.
The physics engine emulates real life factors such as aerial
drag, road friction etc.
Read the TORCS manual.
It is important to take such factors into account so as to be
able to emulate the car racing competition as close as
possible to the reality.

10. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP III
The simulator can also be integrated with third-party
software through its programming interfaces.
It is an open source project.
And this is where the fun begins.
Competitors are invited to plug in and test their own
software controllers for the racing cars.
Design parameters for the software controllers are
somewhat easy to understand conceptually as well.
As a matter fact the simplest design goal is to come up
with controllers that can help a car to win a race.
That is quite simple to state and understand at this level.
And this is where the whole competition becomes a lot
more fun.

11. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP IV
Machine learning, and specially chauvinists of evolutionary
algorithms, try to solve this problem from a totally different
perspective.
And that is their perspective.
And in order to understand their perspective you would
have to understand either one of these disciplines in a bit
more detail.
Stated shortly, the idea is to evolve a set of optimum
controllers for the racing cars that would help the car win
the competition.
People have tried plenty of algorithms.
One may as well cook up new algorithms along the way to
design newer controllers.

12. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP V
I shall explain how a set of controllers can be precisely
evolved using an evolutionary algorithm in a subsequent
article.
Suffice it say for now that if you have understood the basic
working of an evolutionary algorithm, you would not find it
very hard to learn the whole idea behind the competition.
The simulated drone flying championship can also be
designed in a similar way.
There are plenty of simulators for drone flying available
online.
You can try either one of them.
I have particularly liked UAV Playground.
This is written in Java and can also be found on google.

13. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP VI
This is open source and quite modular.
It also allows integration with third party software as well.
It also emulates virtual reality quite well.
You can integrate it with a machine learning package and
try to design controllers for drone planes with simpler
objectives.
The objectives could be to fly a drone all over a place and
perform some simple navigation.
You can also use this package for genetic programming
that I wrote myself for symbolic regression.
This is written in java and works pretty well.
Of course you are free to use your own software package
and run it.

14. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
EXPLAINING THE SIMULATED DRONE FLYING
CHAMPIONSHIP VII
And obviously everyone would have to try something
different for this championship to work.
If it all goes well and many people participate in it, the state
of the art in drone industry could evolve pretty fast and
develop quite sophisticated drone systems.

15. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
HOW TO EVOLVE CONTROLLERS FOR SIMULATED
DRONES I
Why do we need to evolve controllers?
Consider that if you are trying to replace a human pilot in
an aircraft with some sort of artificial intelligence that would
fly the plane as well as a human being would.
This can be a great idea.
This is also a central theme behind designing drones.
And in order to accomplish this task you would either have
to develop a background in machine learning or artificial
intelligence.
And this also answers the question on as to why do we
need to evolve controllers.
Now let us answer one more question:

16. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
HOW TO EVOLVE CONTROLLERS FOR SIMULATED
DRONES II
Why evolve controllers for simulated drones?
The answer for this question is simple, although there
could be quite a few reasons.
And this is an extremely important question.
The answer lies in the question that why do we need to
evolve simulated drones in the first place?
The reasons we would prefer to design drones in
simulation lies in the expenditure it may require to test, try,
design and evolve controllers for drones while employing
real drones.
Most of the machine learning algorithms employ hit and
trial methods.

17. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
HOW TO EVOLVE CONTROLLERS FOR SIMULATED
DRONES III
This is quite natural to suppose and understand as well
that as new algorithms are designed, it is done so at the
expense of bad algorithms at times.
And bad algorithms and controllers can result in a lot of
crashes, thus making employment of real drones for design
of their controllers a very expensive expedition to
undertake.
So as a result controllers for drones have to be designed in
simulation.
Whether or not the simulated controllers would be good
enough for deployment in real drones depends partly on
the quality of the controllers that have been designed and
also on the ability of the simulation environment to mimic
most types of real environments.

18. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
HOW TO EVOLVE CONTROLLERS FOR SIMULATED
DRONES IV
If you want to design controllers that do other complex
tasks besides ordinary flying, such as extinguishing fires or
coordinate with other drones as they perform complex
activities, you would have to develop simulation
environments that can allow your drones to do exactly that.
How to evolve controllers for simulated drones then?
This is our final question.
I would like to draw your attention to the tutorials about
genetic algorithms and genetic programming.
Both of them are population based algorithms.
The latter is a lot more powerful as it allows whole
computer programs to be evolved.

19. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
HOW TO EVOLVE CONTROLLERS FOR SIMULATED
DRONES V
Both algorithms generate a huge population of individuals
as they start.
Then they evolve newer populations of individuals using
genetic operators of crossover and mutation.
They test each individual for its fitness to solve the
underlying problem.
In this problem a fitness score could be based on how well
the set of controllers evolved allow the drone to perform
the prescribed tasks of coordination while flying and
carrying out the tasks.
Once all the individuals of the population have been
assigned fitness, a certain number of good individuals are
kept and bad ones are littered.

20. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
HOW TO EVOLVE CONTROLLERS FOR SIMULATED
DRONES VI
The good ones are used to make a new parent population
of individuals. And a new evolutionary cycles begins.
At this stage it must be fairly intuitive for you to imagine for
you that in the beginning the algorithm would generate a
lot of bad and naive controllers.
And they might result in a lot of crashes if real drones were
employed.
So we need nice simulation environments.
It is only when a certain number of generations have
elapsed, the search process may begin to find better
individuals.

21. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
HOW TO EVOLVE CONTROLLERS FOR SIMULATED
DRONES VII
And eventually, as we can hope, it would find an individual
set of controllers that has all the dexterity of an adept
human pilot in flying the drone.
The controller can be bench-marked at this stage and
employed in real drones.

22. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
AN EVOLUTIONARY ALGORITHM IN A NUTSHELL
FIGURE : Breeding Cycles of a Typical EA

23. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
FLIGHT GEAR I
FIGURE :

24. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
FLIGHT GEAR II
FIGURE :

25. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
FLIGHT GEAR III
FIGURE :

26. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
UAV PLAYGROUND I
FIGURE :

27. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
UAV PLAYGROUND II
FIGURE :

28. Introduction Explaining The Simulated Drone Flying Championship How to Evolve Controllers Evolutionary Algorithm S
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