Journal of Material Sciences and Engineering Technology

Simulation of Obstacle Avoidance Robots

Mukhtar Ibrahim Bello¹ and Muhammad Ahmad Baballe^2*

¹Department of Computer Science, School of Technology, Kano State Polytechnic, Kano, Nigeria 
²Department of Computer Engineering Technology, School of Technology, Kano State Polytechnic, Kano, Nigeria

*Corresponding author
Muhammad Ahmad Baballe, Department of computer engineering technology, School of Technology, Kano state polytechnic, Kano, Nigeria.

ABSTRACT
The first function of the system is to detect the presence of obstacles. When the user activates the system using the power ON/OFF switch, the Arduino  microcontroller will read the data. When the ultrasonic sensor detects the presence of an obstacle in the process of moving forward, the robot will move backward. If the robot does not sense any obstacle, that is, if the distance between an obstacle and it is wide, it will then move forward again until it senses an obstacle before it stops. C programming is used for Arduino board applications to develop the program for the whole system's operation. There are three light-emitting diodes; the first one shows the amount of charge in the batteries, while the remaining two show that if the robot is moving forward, one of the two LEDs will be on, and if it is moving backward, one of the LEDs will also be on. There is also a power source unit that is used to charge the batteries used in the system.

Keywords: Simulation, Arduino Uno, Ultrasonic Sensor, Robots 

Introduction
Robotic navigation research is beginning to gain momentum on its own. Robotics experts started to create various free routes finding algorithms. The navigation system is regarded as being of paramount importance since the robot must be able to be securely controlled from the starting point to the target (destination). To avoid impediments, or to put it another way, the robot must be able to avoid them. The first of two elements that serve as a guide is this. The robot must also continually make sure that it reaches its goal (target). Making a decision from the different travel possibilities is the challenging part. When making decisions, like in the example, a driver is frequently nevertheless troubled by uncertainty in reality above. It will be interesting to see how this is implemented in a mobile robot (autonomous robot). Simple problems like these can turn into more complex ones if they are applied to autonomous mobile robots that must avoid obstacles. It would be difficult for a mobile robot to identify obstacles and decide how to avoid them, not to mention that the main target (goal) can disappear from the camera's field of view. All of these calls for a very difficult computational process. The light intensity is a factor that must be taken into account because the sensor that will be used is a camera sensor. The aforementioned factors will make it difficult for the robot to get where it is going. Obstacle avoidance has been the subject of numerous studies, starting with the presentation of fuzzy algorithms for reactive navigation of mobile robots in challenging environments [1-61]. This study shows that fuzzy logic is reasonably efficient and reacts rapidly to problems. This study only considers static workplace obstructions; moving obstructions caused by moving objects are not included. This study only considered unexpectedly appearing static obstacles, although model-based predictive controller (MBPC) using neural networks and ultrasonic sensors is also utilized to guide mobile robots around unexpectedly appearing static obstacles in their environment [62-77]. The Dynamic Artificial Neural Network (DANN) approach is used for motion planning for mobile robot paths through [78-80]. This research shows that on a flat surface, a mobile robot can be directed around both stationary and moving obstacles. In order to further enhance the robot's ability to overcome obstacle avoidance, generalized dynamic fuzzy neural networks (GDFNN), a blend of neural network and fuzzy approaches, were employed to construct real-time control autonomous mobile robots [81]. The experimental findings show that GDFNN outperforms conventional fuzzy logic control in terms of performance. The obstacle avoidance problem for mobile autonomous robots is also addressed by certain researchers using Reinforcement Learning with Neural Networks (RLNN) [82]. The simulation's results show that the robot can improve its capacity for learning and can complete the tasks assigned to it in a challenging setting [89-92]. By combining camera sensors with lasers, researchers are starting to invent new ways to identify obstructions instantly. These sensors are capable of precisely identifying both two- and three-dimensional objects [83]. Even in more recent studies, stereovision systems were developed using a combination of omnidirectional cameras and perspective cameras [84]. To estimate the locations of obstacles in three dimensions, this technique combines a wide field of view from a perspective camera with a 360°C field of view from an omnidirectional camera. Several vision system implementations based on color sensors camera sensor Pixy 2 CMUcam5 and thermal cameras have been investigated in past studies [85-87]. Excellent outcomes are obtained from the aforementioned trials, particularly with regard to real-time obstacle detection. The earlier experiments, however, did not use movable barriers. The goal of this project is to develop a method for avoiding moving obstacles. Based on prior research, this project will develop an autonomous mobile robot that can navigate by itself to avoid moving obstructions caused by environmental changes in the robot's working environment. To recognize the environment, two webcams are used as stereo vision sensors. Because it is possible to detect pedestrians' upper bodies, they are used as impediments. Since the robot is operating in its genuine environment, this item was picked. The intelligence technique as a control system must be able to handle the issue of moving obstructions in the work area in order to send the robot to the objective (destination). The control system that is used to avoid obstacles is neurofuzzy. With the hope that it would be able to negotiate obstacles with ease and flexibility, a three-wheeled omnidirectional robot was deployed for this experiment. A robot behavior that can recognize the target object, recognize moving obstacles, and make flexible judgments to avoid them must be designed in order for the mobile robot to reach its predetermined target (goal). These actions will be used by the robot to navigate. With the help of stereo vision and the Neuro-Fuzzy algorithm, the robot is directed from its beginning point to its final location. In order to improve the robot's ability to adapt to changing environments, omnidirectional robotics and the Neuro-Fuzzy algorithm are employed to assist the robot in identifying obstacles and making decisions that the robot will avoid. The focus of this research is on robot navigation systems, which comprise locating the target (destination), which is assumed to always be in the robot's line of sight, spotting obstacles and avoiding them, and creating flexible and fluid movements. The impediment items employed are pedestrians, who are located via upper body detection. The robot's workstation consists of a corridor and an interior chamber that are each 4 meters long and 4 meters wide. This study is not focused on traveling the shortest distance because the robot does not walk along a path. To help omnidirectional mobile robots avoid obstacles, this research aims to develop a stereo vision-based navigation system. The proposed method employs the Neuro-Fuzzy algorithm to create a barrier-free path in real-time and to control the robot's movement in a flexible and fluid manner. To guide the mobile robot to a predetermined place, it is crucial to design a robot behavior that can recognize the target object, detect moving barriers, and make adaptive judgements to avoid them. This study aims to investigate the navigational behaviors of the robot. As shown, by using a stereo camera to detect a target and obstacles as input to ANFIS, this study advances the state-of-the-art in obstacle avoidance based on the visual sensor for robot navigation systems. The research technique for this work is divided into two primary sections. The first step is to create a technique for managing the angular and linear velocities of mobile autonomous robots [88]. 

How to Create a Robot That Avoids Obstacles Using Ultrasonic Sensors
Understanding the ultrasonic sensor's operation is crucial before beginning construction of the robot because it will be used to detect obstacles. The essential premise underpinning how an ultrasonic sensor operates is keeping track of how long it takes to broadcast ultrasonic beams and how long it takes to receive them after they have impacted a surface. The distance is then determined using the formula. Therefore, the trig pin of the HC-SR04 is raised for at least 10 us. Eight pulses at a frequency of 40 kHz are employed to transmit a sound beam. The signal is received by the HC-SR04 after it bounces off the surface and lands on the receiver echo pin. When the message was sent, the Echo pin had already risen significantly [94,95].

Materials and Method
The materials used in this research are shown in Table I below.

Method 
This section of research handles the operation of the whole system. The first function of the system is to detect the presence of obstacles. When the user activates the system using the power ON/OFF switch, the Arduino microcontroller will read the data. When the ultrasonic sensor detects the presence of an obstacle in the process of moving forward, the robot will move backward. If the robot does not sense any obstacle, that is, if the distance between an obstacle and it is wide, it will then move forward again until it senses an obstacle before it stops. C programming is used for Arduino board applications to develop the program for the whole system's operation. There are three light-emitting diodes; the first one shows the amount of charge in the batteries, while the remaining two show that if the robot is moving forward, one of the two LEDs will be on, and if it is moving backward, one of the LEDs will also be on. There is also a power source unit that is used to charge the batteries used in the system. 

Arduino Uno with Driver Motor
This is the pin configuration of how the Arduino Uno is connected with the motor driver, which is used to turn on the motor.

Pin Configuration of the Arduino with Servo Motor
The below diagram shows how the Arduino Uno is connected to the 5-volt servo motor. The ultrasonic sensor is mounted on the servo motor. This motor helps rotate the sensor to detect obstacles.

Pin Configuration of the Arduino to Ultra Sonic Sensor
The image below shows how the pin of the Arduino Uno is connected to the ultrasonic sensor. This ultrasonic sensor is used to detect an obstacle that is in front of the robot.

Result
The simulation's results are produced, and they also demonstrate how the robot's entire circuit is implemented. The Arduino IDE integrated development environment was used to write the software, which was then translated from the C language into machine code (a hex file) for debugging. Then it is tested and simulated on the PROTEUS ISIS professional. The program was determined to be functional, albeit with a few small flaws that were fixed before the project was finished. Based on a dimension, plastic rubber was used to create the casing, and gum rubber was used to attach the pieces.

Conclusion
This research has been successfully completed and tested. The obstacle avoidance robot was tested to sense the presence of an obstacle. Whenever this robot senses the presence of an obstacle, it will move backward until it is far away from the object it senses, then it will move forward again towards this object. This device is useful, and it finds application in diverse areas of life.

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