Essay on Autonomous Navigation: Design & Research of Multi-Machine Autonomy

Introduction

Autonomous navigation refers to the ability of robots to utilize their sensors, which are internal and external, to detect the environment to move around and perform actions achieving autonomous movement. Robot intelligence level is determined by the degree to which a robot can implement autonomous change (Liu et al., 2018). Autonomous movement is useful in making robots that are useful in performing human or commercial tasks. This paper shows the design and research of a multi-machine autonomous navigation mobile root system based on group intelligence kinematic chain technology.

Statement of Problem

Several technologies have enabled the development of mobile robots. They are now evolving to perform tasks like humans. Therefore, there is a need for research on multi-machine autonomous navigation mobile robot system based on group intelligence kinematic chain technology. It is crucial as technology continues to evolve and mobile robots are huge aspects of the future.

Research Questions

• What is the system design structure of mobile robots using autonomous navigation?
• What are the main principles of an autonomous navigation system?
• What are the types of sensors used in mobile robots to achieve autonomous navigation with group intelligence kinematic chain technology?

Research Objectives

• The research aims to show the system design structure of mobile robots using autonomous navigation.
• The study aims to reveal the main principles of an autonomous navigation system.
• The research projects to show the types of sensors that are important to achieve autonomous navigation using group intelligence kinematic chain technology and precise positioning.

Literature Review

The system design structure of a mobile robot using autonomous navigation with group intelligence kinematic chain technology uses, SLAM algorithm, global path planning algorithm, and plan planning algorithm. The main principles that an autonomous navigation system uses include environmental map building, path planning, and speed-control switch design Sensors in mobile robots are used to achieve autonomous navigation using group intelligence kinematic chain technology and precise positioning, including include vision, internal state, distance measurement, localization and orientation sensors.

System Design Structure

The simple system design of a mobile robot using autonomous navigation with group intelligence kinematic chain technology includes three parts. When building a 2D grid map of the robot environment, the SLAM (Simultaneous localization and mapping) algorithm is utilized (Ren et al., 2018). The two other algorithms include the global path planning algorithm and plan planning algorithm. They are all useful for mobile robots to navigate the set environment to avoid obstacles and to find the smoothest and shortest path to target position. The barriers can either be dynamic or static. When maintaining the stability of the robots, a speed switching controller is useful to perform the task (Wang et al., 2018). An overview of the system structure can be as follows:

Principles of An Autonomous Navigation System

The main principle that an autonomous navigation system uses include environmental map building, path planning, and speed control switch design (Song et al., 2019). The environment scene must be set out first for the smooth operation of autonomous navigation using group intelligence kinematic chain technology, and the environment map building algorithm is utilized. The environmental map building algorithm works such that it gets the posterior joint probability density function first using readings from the robot's odometer and robot sensor measurements (Shi, 2019). The mobile robot trajectory determines the accuracy of the mobile root trajectory. To update the real-time position of the mobile robot position, the readings of the odometer sensor are crucial (Singhal et al., 2017). The mobile robot also continuously builds the environment map using the kinetic depth camera using self-localization.

The path planning algorithm includes local and global path planning. The mobile robot uses it to get to its target position smoothly while avoiding the dynamic and static obstacle (Wei, 2019). Global path planning, however, does not navigate dynamic obstacles but is useful when navigating static barriers (Xin et al., 2020). The local plan can navigate both the dynamic and static obstacles effectively; therefore, it is more advantageous than global planning (Zhang et al., 2018). Mobile robots use both local and global planning to navigate the barriers and avoid collisions to reach the target position smoothly (Yablonina & Menges, 2018). Mobile robot speed varies to enable it to navigate slopes of different angles and inclination (Yu et al., 2017). The speed switching controller comes in handy as it uses slope value to alter the speed of the mobile robot to achieve a robust mobile robot autonomous navigation system.

Types Of Sensors

Sensors in mobile robots are used to achieve autonomous navigation using group intelligence kinematic chain technology and precise positioning. They include vision, internal state, distance measurement, localization, and orientation sensors (Zhao et al., 2019). In animals, however, it is different as they use touch, sight, magnetic fields, smells, or echolocation. The majority of animals primarily use vision as the primary sensor because it contains more information compared to other forms of senses. The mobile robots use sensors for locomotion, path planning, mapping, and localization. Vision is one of the most accurate, cheap, and straightforward. Video cameras are utilized to approximate the energy in the mobile robot environment, and it is a form of a passive sensor. Unlike ultrasound or infrared cameras, they do not alter the content of the environment that the mobile robot (Zhang et al., 2018). Vision sensors have an enormous application value and can be specialized to perform other tasks apart from navigation.

Significance of the Study

Mobile robots that use autonomous navigation determine their environment, their location, and the paths in the background. Vision sensors are the most utilized to achieve most of these functions together with other sensors are they have a high application value. The study gives the design structure of a robot utilizing autonomous navigation using group intelligence kinematic chain technology, the main principles, and its sensors.

Research Methodology

The research methodology in the study is meta-analysis. It is a branch of qualitative research, and it is the compilation and analysis of peer-reviewed journals to get information on the research topics. Peer-reviewed journals are stable, and they constitute reviews from different scholars, and knowledge is in-depth and hardly changes. They are found in online and physical libraries. Meta-analysis is a cost-effective method as only one researcher can do all the work.

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