Dorin Mihail DINULESCU
Polyethnic University of Bucharest

ABSTRACT
This study provides a comprehensive review of mobile robot navigation with a focus on visual data. In the sections that follow, we’ll examine the most common methods used for mobile robot navigation and highlight the noteworthy benefits of approaches that rely on visual information. Our main objective is to offer a new viewpoint on mobile robot navigation by contrasting and evaluating many techniques now in use in order to identify the most successful visual-based navigation strategy. We’ll start by giving a full explanation of the core ideas guiding autonomous robots and path planning in order to accomplish these goals. Then, after thoroughly classifying these tactics, we will go into navigation strategies designed exclusively for autonomous mobile robots. In addition, we will thoroughly evaluate the strengths and shortcomings of these primary methodologies through a thorough comparison study. We shall choose the approaches that will work best in our particular setting after all.
Keywords: Visual Robotics Navigation, Adaptive Robot Guidance, Visual Autonomy, Command Adaptation, Dynamic Navigation.
I.INTRODUCTION
Robots, which are mechanical contraptions, possess the ability to perform tasks typically undertaken by humans, such as traversing to fulfill specific obligations or manipulating various objects. This eliminates the necessity for human interaction. As time has progressed, the definition of the term “robot” has evolved. Particularly, it was initially introduced in science fiction through the works of Josef and Karel Apek. A contemporary robot is characterized as a self-sufficient system capable of perceiving its surroundings, executing actions, making decisions, and communicating. It consists of a mechanical framework that governs its allowable shapes and movements, as well as sensors and actuators for interacting with its environment. Additionally, it incorporates a control system that analyzes sensor data and strategizes the robot’s movements.
Intelligent autonomous robots are indispensable across a multitude of domains, encompassing space exploration, transportation, business operations, and military applications, among others. An autonomous robot must possess the capability to navigate both static and dynamic settings with freedom. The primary objective of mobile robot navigation is to achieve smooth and secure traversal in such environments.
The navigation process of a mobile robot typically comprises five stages: self-localization, path planning, environmental sensing and load assessment, and the execution of predetermined motions. Due to its significance in industrial robot applications, the field of route planning has been extensively explored prior to the widespread availability of mobile robots. It is noteworthy that the challenge of route planning for industrial robots is considerably more complex compared to that encountered by autonomous robots in generally flat terrains. This is primarily due to the increased degrees of freedom possessed by industrial robots.The techniques employed by self-governing robots for path planning are based on those used by industrial robots. It is important to note that autonomous robots are not constrained by the same speed limitations as industrial robots, which often move at high speeds for efficiency and cost-effectiveness. Since many mobile robots move at much slower speeds, route planning can be simplified by disregarding dynamic elements. This simplification has led researchers to explore different navigation planning approaches, as evidenced by an in-depth analysis of navigation strategies suitable for both static and dynamic scenarios in [1]. Real-time navigation scenarios for mobile robots can benefit from the straightforward implementation of these tactics. This study presents a comprehensive review of mobile robot navigation with an emphasis on visual data. In the subsequent sections, we will examine the most commonly used methods for mobile robot navigation and highlight the notable advantages of visual-based approaches




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