This document describes the design and development of an intelligent navigation control system for autonomous robots using neural networks. It proposes using a neural network approach to allow robots to learn how to navigate autonomously and avoid obstacles. The system uses ultrasonic sensors and an artificial neural network trained with backpropagation to allow the robot to detect obstacles and learn navigation over time. The neural network controller was simulated in MATLAB Simulink and implemented on an Arduino platform. Test results demonstrated the robot's ability to intelligently navigate different environments using the neural network approach.

1. International Journal of Excellence Innovation and Development
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Design and Development of Intelligent
Navigation Control Systems for Autonomous
Robots Using Neural Network
Viji R
Dept. of Mechatronics, Thiagarajar College of Engineering, Madurai, India
Abstract—Navigation and obstacle avoidance are most
important problems in developing and designing the
intelligent mobile robots. In this work proposing a neural
network-based navigation for intelligent autonomous
mobile robots. Neural Networks (NN) deal with
cognitive tasks such as learning, adaptation. In designing
a Neural Networks navigation approach, the ability of
learning must provide robots with capacities to
successfully navigate in the proposed maze environment.
Also, robots learn during the navigation process, from
sensors, update this one and use it for intelligently
planning and controlling the navigation through Back
propagation Neural Network. Controller is simulated
first in MATLAB® using the Simulink Toolbox. An
Arduino embedded platform is used to implement the
developed neural controller for field results. The results
display the ability of the neural network-based approach
providing autonomous mobile robots with capability to
intelligently navigate in several environments.
Moreover, this method contributes positively to reducing
the time required for training the networks. The NN
controller is best for learning in real time application in
overall navigation and intelligence.
Keywords—Intelligent controller, autonomous robot,
ultrasonic sensors, neural network, MATLAB®, arduino.
INTRODUCTION
A self-sufficient robot is a sort of robot that utilization
insightful calculations and in addition sensors so it can
detect and identify the outside world. The robots which
are planned with knowledge have the capacity of route.
This capacity can be watched even in unique and
obscure condition without help of any person.
There are two noteworthy strides in a self-ruling
impediment avoider automated route framework. Firstly,
a complex environment with several obstacles will be
introduced in front of the robot. The robot must be able
to avoid obstacles and complete the navigation process.
Furthermore, the robot must have its own learning
capacities. It is necessary to observe and learn the
positions of the obstacles and overcome the collision
with the obstacles and navigate in an environment full of
dynamic and static obstacles.
The preparation ability as indicated by the tangible info
and its reaction to the obstructions are engaged. To train
the robot, several training samples are introduced to find
its route without colliding with any obstacle [1]. Using a
vision-based surveillance robot that can be run
autonomously and able to acquire images from its
dynamic environment. The robot that can avoid
obstacles using an ultrasonic distance sensor based on
back propagation neural network and a camera for face
recognition and transmitter for transmitting video [2].
Robots must learn during the navigation process, build a
map representing the knowledge from sensors, update
and use it for intelligently planning and controlling the
navigation through maze [3]. Execution of neural control
frameworks in versatile robots in impediment evasion
progressively utilizing ultrasonic sensors with complex
techniques of basic leadership being developed
(MATLAB and Processing). An Arduino embedded
platform is used to implement the neural control for field
results [4]. The real-time optimal robot motion is
planned through the dynamic activity landscape
framework of the neural network that represents the
dynamic environment [5].
The robot to navigate through flat surface among static
and moving obstacles, from any starting point to any
endpoint trained online with an extended back
propagation through time algorithm, which uses
potential fields for obstacle avoidance [6].Ensure a
collision free navigation plan for each of the robots by
defining the co-ordinates of workspace believes in
learning and movements of the robot. The robot finds
nearly most optimal path at each instant of time of robot
travel [7].
An improved Hopfield – type neural network for robot
path planning in which target activity is treated as an
energy source injected into the neural system and is
propagated through the local connectivity of neurons in
the state space by neural dynamics using a harmonic
function. The target and obstacles remain at the peak.
The real-time collision-free robot motion is planned
through the dynamic neural network activity without any
prior knowledge of the dynamic environment, without
explicitly searching over the global free workspace or
searching collision paths, and without any learning
procedures [8].
Route through a maze use recursive computations in
which unwanted or non-ways are slaughtered recursively.
Neural networks with their parallel and distributed nature
of processing seem to provide a natural solution to this
problem using a two-level hierarchical neural network for

2. Design and development of intelligent navigation control systems for autonomous robots Viji R
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the mapping of the maze as also the generation of the
path if it exists [9]. A neuro-fuzzy controller for sensor-
based versatile robot route in indoor conditions. The
control system consists of a hierarchy of robot behaviours
[10].
METHODOLOGY
This paper presents a new methodology based on neural
dynamics for real-time collision-free robot path
generation in an arbitrarily varying environment. The
proposed methodology incorporates activation functions
in a neural system to carry out real-time robot path
planning.
Architecture of Robot
The robot utilized in this exploration is a portable robot
which is outfitted with two actuator wheels considered
as a framework subject to non holonomic requirements.
A very low cost mobile robot for heavy load that
consists of distance sensors, and Arduino, 5A driver DC
motors, and DC motors with wheels.
Our strategy utilizing 3 ultrasonic separation sensors is
sufficient for distinguishing impediment, so we execute
that technique for this examination. Ultrasonic sensors
work at a recurrence of 40 kHz and have a deviation
point greatest of about 30∘, so as a rule robots require in
excess of one sensor to have the capacity to quantify the
separation of a deterrent in its region.
Fig. 1: Three distance sensor mounted on robot chassis.
Design of an Artificial Neural Network for Learning
This work is concerned with collection of sensory data
and robot navigation. Back propagation is an algorithm
in neural network that can be used to train a robot with
neural network. Training a neural network is the process
of finding a set of weights and bias values so that, for a
given set of inputs, the outputs produced by the neural
network are very close to some known target values.
Gradients are values that reflect the difference between a
neural network’s computed output values and the desired
target values. As it turns out, gradients use the calculus
derivative of the associated activation function. The
gradients of the output nodes must be computed before
the gradients of the hidden layer nodes, or in other
words, in the opposite direction of the feed forward
mechanism.
Supervised learning which incorporates an external
teacher, so that each output unit is told what its desired
response to input signals is used in our system. Back
propagation is basically a gradient descent process, with
each change in the weights of the network bringing the
network closer to a minimum error represented in a
multidimensional weight space. Gradient descent does
have its problems; however, in back propagation, these
problems manifest themselves as the time taken to reach
a minimum and the occurrence of local minima. Let be
an input; is weight and the output are .
Fig. 2: Neural network with back propagation control.
The algorithm for back propagation is shown below.
Algorithm 1 (back propagation).
Initialize each to some small random value
While not reach termination condition
Do
For each training example ⟨ ( 1, . . ., ), ⟩
Do
Input the instance ( 1, . . ., ) to the network and
compute the network outputs
For each output unit
=
(1− ) ( − )… (1)
For each hidden unit ℎ
ℎ= ℎ
(1− ℎ) Σ ℎ, … (2)
For each network weight , Do
, = , +Δ , , where Δ , = , ... (3)
End Do
Algorithm for Robot
We need to ensure that our framework can be connected
in genuine word, for example, generation robot
framework. We have developed algorithms and
programs consisting of 2 main modules, namely, neural
network(), and the moving method().
Declare variables
Declare functions
Set all motors off
Initialize the sensors
Far is greater than 20cm
Near is below 8cm
Do
Call neural network()
Call moving method()
If front distance is far then
Call forward

3. International Journal of Excellence Innovation and Development
||Volume 1, Issue 1, Nov. 2018||Page No. 050-053||
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End if
Function moving method
If front distance is near, then
Call backward
End if
If front distance is far and front right is far and front
left is near then
Call turn right
End if
If front distance is far and front left is far and front
right is near then
Call turn left
End if
If front distance is near and front right is near and
front left is near then
Call backward
End if
End function
EXPERIMENT AND OBSERVATION
Our proposed method for the robot has been successfully
implemented and it has shown a good performance in
various environments. Three ultrasonic sensors
prevailing to distinguish and measure the separation of
hindrance consistently. The system is verified through
Simulink and shows the positive result. Figure 3 Shows
the Simulink model of the system. Figure 4 and 5 shows
the subsystem of neural network of function layer and
functional fitting layer which gives, the weights, bias,
and activation function. The robot is successfully
navigating through the maze environments 1 and 2.
Figure 6 and 7 various environments of maze through
which robot navigates.
Fig. 3: Controller verified through simulink.
Fig. 4: Functional layer controller subsystem.
Fig. 5: Bias, weights and activation function.
Fig. 6: Robot navigates through maze environment 1.
Fig. 7: Robot navigates through maze environment 2.
RESULTS AND DISCUSSION
This paper presents a robot with obstacles avoidance
capabilities for general purpose robot in maze
environments. Algorithms of Neural Network with back
propagation Control for obstacle avoidance were
implemented in the robot. Experimental results with
various positions of obstacle shows the ability of robot to
avoid it and have shown a good performance. The sensor
framework is exceptionally shabby in light of the fact
that it just uses 3 remove sensors. Figure 8, implies the
training, validation, testing of data are perfect, which
encloses inside the circle. All though many plots are
there i.e., histogram, performance, etc., Regression plot
is considered as important, when the trained data is
linear, the robot is perfectly trained Figure 9, shows
Regression Plot of the Trained Network.
Fig. 8: Performance plot of the trained network.

4. Design and development of intelligent navigation control systems for autonomous robots Viji R
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Fig. 9: Regression plot of the trained network.
CONCLUSION
An intelligent control of an autonomous robot which is
trained with Neural Network with back propagation
Control is developed. This robot successfully navigates
in a maze structured environment has been presented.
The Desired output of the trained network is obtained
and successfully implemented to the prototype. An
arrangement of testing conditions that was made to test
the model in various circumstances. For future work, we
will improve this system for swarm robotics system.
REFERENCES
[1] M. S. H. Sunny, E. Hossain, T. N. Mimma and S.
Hossain, "An autonomous robot: Using ANN to
navigate in a static path," 2017 4th International
Conference on Advances in Electrical Engineering
(ICAEE), Dhaka, 2017, pp. 291-296.
[2] Widodo Budiharto, “Intelligent Surveillance
Robot with Obstacle Avoidance Capabilities
Using Neural Network,” Computational
Intelligence and Neuroscience, vol. 2015, Article
ID 745823, 5 pages, 2015.
[3] Hachour, Ouarda, “A neural network-based
navigation for intelligent autonomous mobile
robots”, International Journal of Mathematical
Models and Methods in Applied Sciences. 4. 177-
186, 2010.
[4] A. Medina-Santiago, J. L. Camas-Anzueto, J. A.
Vazquez-Feijoo, H. R. Hernández-de León,
R.Mota-Grajales, “Neural Control System in
Obstacle Avoidance in Mobile Robots Using
Ultrasonic Sensors”, Journal of Applied Research
and Technology, Mexico, Volume 12, Issue 1,
pp.104-110, 2014.
[5] X. Yang and M. Meng, "Neural network
application in robot motion planning," 1999 IEEE
Pacific Rim Conference on Communications,
Computers and Signal Processing (PACRIM
1999). Conference Proceedings (Cat.
No.99CH36368), Victoria, BC, 1999, pp. 611-614.
[6] I. Engedy and G. Horvath, "Artificial neural
network based mobile robot navigation," 2009
IEEE International Symposium on Intelligent
Signal Processing, Budapest,2009, pp.241-246.
[7] R. Kala, A. Shukla, R. Tiwari, S. Rungta and R. R.
Janghel, "Mobile Robot Navigation Control in
Moving Obstacle Environment Using Genetic
Algorithm, Artificial Neural Networks and A*
Algorithm," 2009 WRI World Congress on
Computer Science and Information Engineering,
Los Angeles, CA, 2009, pp. 705-713.
[8] Yongmin Zhong, Bijan Shirinzadeh and Yanling
Tian, "A new neural network for robot path
planning", 2008 IEEE/ASME International
Conference on Advanced Intelligent
Mechatronics, Xian, 2008, pp. 1361-1366.
[9] S. Srinivasan, D.P. Mital, S. Haque, “A novel
solution for maze traversal problems
usingartificial neural networks”, conference on
computers and electrical engineering, volume 30,
issue3, 2004, pp.563-572.
[10] P. Rusu, E. M. Petriu, T. E. Whalen, A. Cornell
and H. J. W. Spoelder, "Behavior-based neuro-
fuzzy controller for mobile robot navigation,"
in IEEE Transactions on Instrumentation and
Measurement, vol. 52, no. 4, pp. 1335-1340, Aug.
2003.