NUAV Testbed Develop Autonomous Drones

NUAV – A Testbed for Development of Unmanned
Aerial Vehicles
Saleh Habib Mahgul Malik Shams Ur Rahman Muhammad Adil
Raja
Namal College, Mianwali
Pakistan
cbna
May 6, 2017
Muhammad Adil Raja ( Namal College, Mianwali Pakistan cbna)NUAV – A Testbed for Development of Autonomous Unmanned Aerial VehiclesMay 6, 2017 1 / 16

Overview
1 Introduction
2 Our Methodology
Communications Module
ML Algorithms
NUAV Testbed
3 Experimental Setup
A Scheme for Reducing the Simulation Time
A Simple Experiment
On Fitness Functions
4 Results and Analysis
5 Conclusion

Introduction
Contemporary models of Unmanned Aerial Vehicles (UAVs) are
largely developed using simulators.
In a typical scheme, a ﬂight simulator is dovetailed with a machine
learning (ML) algorithm. A good simulator provides a realistic
environment for simulated aircraft.
The process of development can be accelerated largely by employing a
testbed that allows a seamless dovetailing between the ﬂight
simulator and an ML algorithm of choice.
However, an extensive testbed is largely missing from the academic
landscape both in terms of implementation and technical details.
This papers proposes a new testbed for the development of fully
autonomous UAVs.
The proposed system allows researchers to simulate UAVs for various
scenarios.

Our Methodology I
Figure: A simple schematic diagram of NUAV testbed.

Our Methodology II
NUAV Testbed has three modules to it.
Communications Module
In order to develop the communications module we have used the
APIs of FlightGear and taken inspiration from GaTAC [Sonu, 2012].
GaTAC accomplishes its communication with FlightGear by using
UDP sockets.
ML Algorithms
Currently NUAV makes use of two ML algorithms. One is a simple
feed forward error back propagation artiﬁcialn neural network (ANN)
[Bishop, 1995][Mitchell, 1997].
The other is a renowned genetic algithm for multi-objective
optimization known as NSGA-II [Pratap, 2002].

Our Methodology III
NUAV Testbed
The main code of NUAV testbed basically integrates FlightGear with
an ML algorithm and serves as a data tunnel between the two.
In its simple scheme, it fetches data from FlightGear and presents it
to the ML algorithm.

Experimental Setup
A Simple Experiment
In our scheme, we create a neural network structure, and optimize its
weights with NSGA-II.
A single run was performed. We start oﬀ with a population size of
150 and gradually decrease it to a size of 50 over 50 generations.
Similarly, we start oﬀ with a simulation time of 1 second for every
individual, and increase it to up to 20 seconds.
The purpose of reporting this simple experiment is more to deliver a
proof of concept of the working of NUAV testbed at this stage, as
opposed to exhibiting wondrous marvels of ML.

On Reducing Simulation Time
A Scheme for Reducing the Simulation Time
We performed a handful of simple experiments.
The main hurdle we had to overcome in performing a full fledged
experiment involving a GA, containing multiple runs of evolution, was
simulation time.
A simple experiment with a single run of GA up to fifty generations,
requiring a plane to be flown for as small a duration as thirty seconds
requires a simulation time in order of days.

Our first fitness function is to minimize the absolute difference
between the altitude of the plane and a target altitude value of 2500
ft.
The second objective is to minimize the Euclidean distance between
the orientation of the plane against a reference value of zero.
To this end, we provide the values of roll, pitch and yaw as a vector
that serves collectively for orientation.
The third objective is to minimize the absolute difference between the
calibrated air speed of the plane and a target calibrated air speed of
75 knots.
Our final objective minimizes the absolute difference between the
climb rate of the plane with a target value of 7.5 ft/s.
The goal for the GA is to minimize all of these objectives.

Results and Analysis I
At the end of training, we tested all the 50 individuals of the final
generation produced by GA.
We plugged them in one by one in the ANN and flew the plane with
them. From these 50 flights, we scavenged 14 which seemed well
visually.
Graphical results of two of these flights are shown in Figure(s): 2– ??.

Results and Analysis II
(a) (b)
Figure: Values of roll, pitch (degrees) and altitude (%) for two individuals of the
GA run.

Results and Analysis III
(a) (b)
Figure: Trajectories of the corresponding individuals shown in Figure: 2.

Results and Analysis IV
(a) (b)
Figure: Calibrated air speeds of the corresponding individuals shown in Figure: 2.

Conclusion
In this paper we introduced a testbed for development of UAVs.
We call it NUAV testbed.
Our testbed allows modeling autonomous drones that can perform a
user defined task.
We have developed this testbed by integrating FlightGear flight
simulator with NSGA-II, a well known GA.
In future we look forward to extending our testbed to be able to train
UAVs that can perform a plethora of user defined tasks.
In particular, we are eager to develop a capacity to develop fully
autonomous, cooperative and self-coordinating fleets of UAVs.
We also want our testbed to be a distributed and scalable system.

References
E. Sonu and P. Doshi (2012)
Gatac: A scalable and realistic testbed for multiagent decision.
11th International Conference on Autonomous Agents and Multiagent Systems
1507 – 1508.
C. M. Bishop (1995)
Neural Networks for Pattern Recognition.
Oxford University Press, Inc
T. Mitchell (1997)
Machine Learning.
New York: McGraw Hill
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan (2002)
A fast and elitist multiobjective genetic algorithm: NSGA-II.
Evolutionary Computation, IEEE Transactions on, vol. 6, no. 2, pp. 182197.

The End

NUAV Testbed Develop Autonomous Drones

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