Design and Development of Intelligent Navigation Control Systems for Autonomous Robots Using Neural

International Journal of Excellence Innovation and Development ||Volume 1, Issue 1, Nov. 2018||Page No. 050-053||

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 www.ijeid.com

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

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Design and development of intelligent navigation control systems for autonomous robots the mapping of the maze as also the generation of the path if it exists [9]. A neuro-fuzzy controller for sensorbased 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.

Viji R

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 .

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. www.ijeid.com

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 compute the network outputs For each output unit = (1− ) ( − )…

For each hidden unit ℎ ℎ= ℎ (1− ℎ) Σ ℎ, …

For each network weight , Do , = , +Δ , , where Δ , = , ...

End Do

and

(1)

(2) (3)

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

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International Journal of Excellence Innovation and Development ||Volume 1, Issue 1, Nov. 2018||Page No. 050-053|| 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

Fig. 6: Robot navigates through maze environment 1.

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. 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. 3: Controller verified through simulink.

Fig. 4: Functional layer controller subsystem.

Fig. 5: Bias, weights and activation function. www.ijeid.com

Fig. 8: Performance plot of the trained network.

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Design and development of intelligent navigation control systems for autonomous robots

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.

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Viji R

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