In a controlled indoor environment, line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots. A line tracking robot is a self-mobile machine that can recognize and track a painted line on the floor. In general, the path is set and can be visible, such as a black line on a white surface with high contrasting colors. The robot’s path is marked by a distinct line or track, which the robot follows to move. Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation. Localization, automated map generation, autonomous navigation and path tracking is all becoming more frequent in vision applications. A visual navigation line tracking robot should detect the line with a camera using an image processing technique. The paper focuses on combining computer vision techniques with a proportional-integral-derivative (PID) controller for automatic steering and speed control. A prototype line tracking robot is used to evaluate the proposed control strategy.

This paper considers the problem of stabilizing a first-order plants with known time delay using a fractional-order proportional–integral controller $\text{P}{\text{I}}^{\lambda }$ . Using a generalization of the Hermite–Biehler theorem applicable to quasi-polynomials, a complete analytical characterization of all stabilizing gain values ($k_p,k_i$ ) is provided. The widespread industrial use of fractional PI controllers justifies a timely interest in $\text{P}{\text{I}}^{\lambda }$ tuning techniques.

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic. This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods (including medicines) that is needed to prevent infection and treatment for infected patients. The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic. The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways, particularly in the control of autonomous medic robots. Health officials aim to curb the spread of COVID-19 among medical, nursing staff and patients by using intelligent robots. We propose an advanced controller for a service robot to be used in hospitals. This type of robot is deployed to deliver food and dispense medications to individual patients. An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction. These criteria were met by using two controllers simultaneously: a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative (PID) controller for automatic steering and speed control.

With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed control. Two controllers were used simultaneously to achieve this. Convolutional neural networks (CNNs) are used for target tracking and trajectory prediction, and a proportional-integral-derivative controller is designed for automatic steering and speed control. This study makes use of a Raspberry PI, which is responsible for controlling the robot car and performing inference using CNN, based on its current image input.

The problem of stabilizing a second‐order delay system using classical proportional‐integral‐derivative (PID) controller is considered. An extension of the Hermite‐Biehler theorem, which is applicable to quasipolynomials, is used to seek the set of complete stabilizing PID parameters. The range of admissible proportional gains is determined in closed form. For each proportional gain, the stabilizing set in the space of the integral and derivative gains is shown to be either a trapezoid or a triangle.