Revised: 09 July 2021
and the designed PID controller. Due to the complexity of selection the parameters of PID controller, the Particle Swarm
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- KEYWORDS: Arduino Mobile Robot, Line follower, Infrared Ray (IR) sensors, Particle Swarm Optimization(PSO) algorithm, PID controller. I.
| and the designed PID controller. Due to the complexity of selection the parameters of PID controller, the Particle Swarm
Optimization (PSO) algorithm are used to select and tune the parameters of designed PID controller. Five Infrared Ray (IR) sensors are used to collect the information about the location of mobile robot with respect to the desired path (black line). Depending on the collected information, the steering angle of the mobile robot will be controlled to maintain the robot on the desired path by controlling the speed of actuators (two DC motors). The obtained simulation results show that, the motion of mobile robot is still stable even the complex maneuver is performed. The hardware design of the robot system is perform by using the Arduino Mobile Robot (AMR). The Simulink Support Package for Arduino and control system toolbox are used to program the AMR. The practical results show that the performances of real mobile robot are exactly the same of the performances of simulated mobile robot. KEYWORDS: Arduino Mobile Robot, Line follower, Infrared Ray (IR) sensors, Particle Swarm Optimization(PSO) algorithm, PID controller. I. I NTRODUCTION Nowadays, the researchers have made significant effort in the field of line follower robot because it is used in many applications such as: medical assistance, transportation, educational, marketing, and industrial applications. The line follower mobile robot is one of the types of the autonomous robot that can perform a specific desired mission without human intervention. The operations of the line follower robot can be conclude as follow: the robot used the IR sensors, which are mounted at front ends of the robot, to capture the line position that is drawn on the floor. Depending on the IR sensors reading, the control system will send the command signals to the actuators (DC motors) to govern the steering angle of the mobile robot to track the line smoothly and accurately in shortest time. So that, the sensing process of the line requires high robustness and high resolution. In reference [1], the authors designed a line follower robot for public transport to solve the problems of number of passengers in Turkey. The web server is used to control the motion of line follower mobile robot to track the desired trajectory, where the PID controller is used to perform this mission [2]. The IR sensor to collect the data and it to the microcontroller, then the microcontroller generate the suitable signals to actuate the motors to and maintain the robot follow the desired path [3-5]. The main issue in the design of the line follower mobile robot is the design the suitable controller to govern the steering angle of the robot to keep it on the desire path. The PID control scheme is used successfully in many industrial applications in last five decades [6]. The PID controller is most widely used in the industrial application over the other controller types due to the following reasons: its structure is simple, robustness in wide range of operation, and just three parameters should be adjusted to design its structure [7]. The PID controller system has three parameters that should be tuned to enhance the performance of the controlled plant. The task of adjusting the parameters of PID controller is 12 | Alwan, Green, Noori & Aldair quite difficult and there are many method to perform this task. The Ziegler–Nichols tuning method is a heuristic method of tuning a PID controller. It was developed by John G. Ziegler and Nathaniel B. Nichols [8]. Unfortunately, this classical method does not return an optimal parameters for the PID controller, so that by using this method to design the controller, the performance of controlled system is still insufficient. Some other classical methods are used to tune the parameters of PID controller such as Cohen-Coon method, rule-based method and model-based method. Each of those methods has its advantages and drawbacks [9]. The drawbacks of those methods are they utilize only for first order models including large process delays and they require experienced persons in industrial applications. In the recent decade, the researchers focused on developing and proposing the optimization methods to obtain the optimal parameters for PID controller to enhance the performances of the controlled system. The emergence of intelligence and optimization algorithms such as genetic algorithm (GA), Particle Swarm Optimization (PSO) method, Ant Colony Optimization (ACO) method, provides new techniques to tuning the PID parameters successfully [10]. In this paper, the line follower algorithm is proposed and the PID controller is designed for the mobile robot. The PSO algorithm has been applied to find the optimal parameters of the designed PID controller. Undoubtedly, the cost of line follower mobile robots are relatively expensive. Hence, a precise controller should be properly designed and it performance should be thoroughly studied before buying a physical robot. Therefore, many simulation programs are designed to help the researchers to evaluate the performance of designed controlled system. For such reasons, the usage of a simulator program becomes advantageous as it can save time and cost effectively. Therefore, in this study, the Robot Simulator and Simulink package are used to simulate the tracking of the controlled mobile robot for the desired path that is drawn on the floor. The obtained simulation results proved that the performance of the designed PID controller is very accurate. Then, the Arduino Mobile Robot (AMR) is implemented and programed to follow the desired trajectory. Five Infrared Ray (IR) sensors are used to collect the information about the location of mobile robot with respect to the desired path (black line). Depending on the collected information, the steering angle of the mobile robot will be controlled to maintain the robot on the desired path by controlling the speed of actuators (two DC motors). The ardouin and control system toolboxes (PID controller Blocks), which are constructed in Matlab, are used to program the AMR. The practical results show that the performances of real mobile robot are exactly the same of tải về 1.13 Mb. Chia sẻ với bạn bè của bạn:
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