Posted: February 15th, 2022













In addition to space, transportation, industry, and defense, autonomous robotic robots are required in various scenarios. Material handling, disaster response, patrolling, and relief efforts are just a few tasks that robotic arms can perform. As a result, end-effectors should freely walk in either a static or dynamic environment. The primary goal of autonomous vehicle navigation is to guide a mobile robot throughout a crowded environment from its starting point to its destination point while maintaining a safe path and producing the shortest path length possible. Several techniques for trajectory tracking pathfinding have been extensively researched in this regard. This article has investigated several route optimization techniques which are well adapted for static and dynamic environments and can also be used for simple mobile robot navigation.


Table of Contents

Introduction. 4

The Benefits of Robotics. 5

Robotic Configuration. 5

Algorithm Firefly (FA) 6

The platform of Mobile Robot 8

Navigation by robots. 8

Navigation using vision. 9

Indoor navigation: 10

The cell decomposition (CD) method. 11

Mobile Robot Navigation Using a Neural Network Technique. 12

Kinematical analysis of a wheeled mobile robot is all being done study. 13

Summary. 14

Conclusion. 14

References. 16




Physical tasks previously deemed to be done by humans are now performed by equipment to reduce the burden of labor on humankind. Nonetheless, there is a requirement that devices perform physical tasks and make important decisions in the same way that humans do. Artificial intelligence understanding is important in making things intelligent and achieving this goal. Path planning is the most frequent problem in robot navigation; robot cars must move from a starting line to a goal node while increasing safety. Different algorithms have been trialed on tiny air vehicles (Yan, Liu, & Xiao), motion robots, doorway robots (Aghababa; Yilmaz, Evangelinos, Lermusiaux, & Patrikalakis), and earthen drones.

Mobile robot navigation is a key issue in the field of artificial intelligence. They have a reputation for being extremely intelligent. Transportation, company, and salvage robots are just a few of their applications. One of the most fundamental aspects of autonomous car robot navigation is the proposed method. Scholars have just been working on the issue to routing for decades, and several systems have been proposed. Scheduling helps determine a pedestrian path from one point to another while minimizing the total cost of the path. Prediction model can be partitioned into stationary and non – stationary environments determined by the nature of the atmosphere. Static path planning is used when roadblocks change their coordinates in terms of time; route strategy is used when obstacles change their coordinates in relation to time. Print and virtual algorithms are subdivided further in this data. In online pathfinding, information about the climate is obtained from a connected local camera hidden on the robot, or the robot then components an environment map using data from two storage sensors. Without the use of sensors, the robot has complete information about its environment with using offline scheduling. Various approaches have been suggested up to this point to ensure satisfactory tracking.


The Benefits of Robotics

  • Medical science and health-care systems are undergoing a revolution.
  • Education and training have a new and broad scope.
  • A valuable asset in the nuclear industry.
  • Team sports make extensive use of it.
  • Play the role of an efficient assistant in the field of R&D sciences.
  • Can handle family firm with ease.

Robotic Configuration

An industrial robot will move, and its workspace is limited by the robot configuration. Cartesian, Cylindrical, Spherical, Selective Conformance Articulated Robot Arm are the six major types of robot configurations (SCARA). Delta and Articulate (Parallel). Some automated systems, like most humans, maybe incapable of scratching the back of their bicep of the same hand. Industrial robots, on the other hand, can be meant to push one‘s tools firstly, accurately, and monotonously to any point within a three-dimensional workspace by paying attention to axes and selecting a layout.

Cartesian, Cylindrical, SCARA, 6-Axis, and Delta are the five types of robotics that can be defined in this more brief and complete manner. Each automaton has its own number of attributes that create it better suited to various uses. Their speed, size, and workstation are their primary distinguishing characteristics.

Cartesian: Is the robot’s tool moving in a straight line along all of the Cartesian coordinates (x, y, z). This configuration can create a box-like work envelope.

The print nozzles on many 3D printers are mounted in a Cartesian configuration.

Cylindrical: Its tool can rotate around for a central axis. The tool could also move towards or away as from central axis, as well as upwards on it. This configuration generates a cylinder-shaped work-volume.

This configuration is commonly used for parts and components, manufacturing process and die-cast computer handling, and soldering.

Spherical:  This configuration generates a tool motion that sweeps out a sphere-shaped workspace. It rotates its tool around a central axis, and the tool can also rotate around a second axis that is at a 90-degree angle here to central axis. The device can also move and forth within an axis.

They are frequently employed in die casting, blow mounding, welding, and industrial equipment.

Articulated Robot Arm with Selective Compliance (SCARA): Pivot points are used to allow its software to move in a combo of Cartesian and cylindrical motions. This enables the software to move quicker and more efficiently in certain movements, such as shifting in an arc.

SCARA robots can be used in assembly, palletizing, and bio-medical software.

Algorithm Firefly (FA)

Yang first proposed the FA in 2008. It is prompted by the flashing behavior of fireflies, The meta-heuristics algorithm is another name for it. Its premise is based on random towns and expressed, similar to how flies learn by guesswork. The firefly is a winged animal belonging to the Lampyridae family that is known as a flash bug due to the ability to produce light. It produces light by oxidizing Lucifer in the form of proteins that produce compound. Biology is the process by which light is produced, and insects use it to sparkle without pissing away heat energy. Fireflies used the light to attract a mate, teach a lesson, and occasionally to frighten off predators. Figure 14 shows the pseudo-code and Gantt chart for using the FA to navigate a robot. The FA has recently been used optimal algorithm, and thenit is making inroads across all engineering, including mobile robots.


Articulated: When people think of industrial robots, they typically think of this type of motor. It must have at least a shoulder, an elbow joint, and a wrist joint. Much of these configurations can include both large and small axes.

Assembly, arc welding, industrial equipment, machine attempting, and packaged foods are common applications for articulated robots. An Articulated configuration is demonstrated by the VEX V5 Work cell.


Delta (Parallel): The robot’s tool can be moved the greatest of all robot configuration types. It employs parallel interconnectedness to allow the tool to sweep out all its workplaces quickly.

A Delta can pick and place key items and nimbly in a grouping task, etc and perform a variety of other tasks.

Robotics is the study of layout, creation, and use of devices (robots) to carry out tasks previously performed by humans. Robots are utilized in a variety of companies, including car manufacture, to perform routine repetitive tasks, as well as in industries where tasks must be completed in hostile conditions. Artificial intelligence is used in many aspects of robotics, and robots may be equipped with human-like senses such as vision, touch, and temperature sensing. Some can even make simple decisions, and current robotics research is geared toward constructing self-contained robots that would allow portability and option in unfamiliar areas. Today’s robotic arms need not resemble humans; a robot that resembles a human is referred to as an android.


Mobile Robot Platform

The platform of a robot system is similar to that of a tank. As a result, they can move on multiple multiple but have a current circle, which is useful for outdoor WSN applications. The robotic arm prototype, which was made up of a single unit that endorsed the paths, battery pack, gearbox, and two motors, provided mobility as well as power to drive a mobile robot. Two different sources of energy were used to power a mobile robot. The engines where system is comprised of three AAA batteries, while the MICA2 platform was driven by a single source. Because DC motors show a larger current than the MICA2 battery, they cannot be powered by it; a different power supply is being used to power the DC motors. To increase the reliability of the mobile robot, we devised its base as a fixed piece of material. We chose lightweight material for the body of mobile robot to reduce its weight.

Navigation by robots

Localization usually requires evaluating the robot’s new status and the position of a goal location, in the same frame or cooperate. A metric map or any other mathematical that explains areas within robot system of thought can be used to build a map.

Any mobile device’s ability to control its surroundings is critical. The robot still must avert possible risks like accidents and unsafe conditions (temperature, radiation, climate exposure, and so on), though if one has a feature that needs it to visit particular areas in the machine environment, it will do so.

This essay will provide an idea of flying skills and help to determine the cornerstones of a robot navigation system, as well as the different types of flight controls and their associated building systems.

Navigation refers to a robot’s power to assess its own location using its conceptual framework and then plan a path to a specific destination. A robot or other action device must be able to recognize its surroundings, which requires a map of the area, or the translate that representation.


Navigation using vision

Vision-based navigation, also known as photonic navigation, extracts the visual essential features for localization in the surrounding environment using image processing techniques and sensor arrays such as precision range finders and photometric cameras with CCD arrays. However, there are a variety of techniques to use visual acuity information for transportation and localization, the major components of which are as follows:

  • environmental representations
  • models of sensing
  • Algorithms for localization

We divide perception travel or its techniques into two categories to get an overview: interior navigation and accessible navigation.

Navigation within the building:

Simply guiding a robot to a specific location is the simplest way to get it to go there. This advice could be done in a variety of ways, such as dumping an inductive circuit or crystals with in-floor, painting watercolor lines here on the floor, or putting beacons, markers, bar codes, and other markers throughout the environment. These Self Driving Vehicles (AGVs) are used in industrial settings for travel tasks. Robots can use IMU-based localization devices to traverse indoors.

Indoor navigation systems come in a wide range of configurations. The fundamental reference for indoors and out navigation systems is Guilherme N. DeSouza and Avinash C. Kak’s “Vision for robot manipulators: a survey.”


The cell decomposition (CD) method

To achieve the goal, this study divided the region into quasi grids (cells) and traverses from one cell to the next using connectivity graphs. During navigating, pure cells (cells without obstacles) are considered to achieve path planning from the starting stance to the target point. Deeply corrupt cells (cells with obstacles) in the path are split into 2 new cells produce a pure cell, which then is added to a successor while determining the best route from the previous condition to the target location. The actual and forecast positions are represented by the start and end cells in the CD approach. Adaptive, approximate, and exact CD strategies are the three types of CD approaches.

The exact cells also have no specific shape, but the chart of a environment, as well as the shape and location of the obstacle within it, can be used to define them. The regular grid is used in a variety of ways in just this method. The surrounding free space is first dissolved in to the small elements (trapezoidal and triangular), with every factor assigned a number. In a communication systems graph, every element in the climate serves as just a node. The implementations then are required to join inside the solution space, and a trail in this diagram corresponds to a disparity in free space, which is marked by plaid cell proteges. A path connects to a system in space, which is represented by a series of polka dot cells in this diagram.


Mobile Robot Navigation Using a Neural Network Technique

Neural networks are one of the most important techniques for path planning. Many study teams in fields like signal and algorithms, information processing, mobile robot motion planning, and company use this neurons technique, which is influenced by the sentient amygdale. A literature on human brains and their devices in mobile robotics was presented by Zou et al. To create a robust strategy to ensure for a robot system, the authors of Xiao used a cross feed forward learning algorithm in conjunction with the Q-reinforcement mode of instruction. Rai and Rai developed a speed control system for the Adriano Uno microcontroller that uses the Convolution neural net joystick or the Proportional Integral Derivative (PID) controller. Patino and Carelli created an automated vehicle controller for a common bus using neural network architecture. Yang and Meng56 created an attack trail in a no stationary environment using a genetic algorithm learning algorithm. Nichols et al. presented a genetic algorithms computer vision wall-following mobile robot. 57 An intriguing problem in the context of mobile robotics is online path scheduling with both unknown challenges in the surroundings.


Kinematical analysis of a wheeled mobile robot is all being did study.

In recent decades, the motion control problem of an autonomous wheeled mobile robot has gotten a lot of attention. In recent years, there has been a surge of interest in designing and developing an autonomy four wheels mobile robot utilising various models. Hui’s novelists looked into the kinematic constrictions of a car-like robot system and used neuro-fuzzy methodologies to navigate around moving barriers. To solve the model predictive problem of nonholonomic wheeled mobile robots, Abadi and Khooban used an effective Mandeni-type fuzzy control system based on Strange Weight Vector Optimizer (RNW-PSO). Chakra Booty tackled the issue of mobile robots vibrating on rough terrain. For nonholonomic pmsm drive multirobot navigation, Wang and Yang developed a brain console. The robot has four sharp infrared sensors that interpret the additional amount hurdle position and feed location microcontroller, which controls the motion of the robot’s 2 different motors. Wheeled mobile robots have been used in a number of engineering, haulage, and social application areas. Martinez et al. designed the kinematics and dynamics dynamic model power of the automated unicycle mobile robot using type-2 fuzzy rationale and simulated annealing. Researcher prepared a nonholonomic robotic system with a multilayer perception web motion and awareness govern in Al-Arajii. Liang et al. mobile robot has presented the dynamic system design of the double disparity drive.

A biologically inspired neural network 

based wall-following mobile robot has been presented by Nichols 

et al. [57]. Online path planning between unknown obstacles in 


robotics. Motlagh et al. [58] have presented the target seeking,

and obstacle avoidance behaviours using neural networks and 

reinforcement learning. Mobile robot navigation using hybrid 


Parhi [60] have designed multilayer feed-forward neural network,

which controls the steering angle of the robot autonomously in 

the static and dynamic environments. The different obstacle 

distances are the inputs of the four-layered neural network, and

the steering angle is the output. Real-time collision-free path 

         

dynamic and unstructured environment

A biologically inspired neural network 

based wall-following mobile robot has been presented by Nichols 

et al. [57]. Online path planning between unknown obstacles in 


robotics. Motlagh et al. [58] have presented the target seeking,

and obstacle avoidance behaviours using neural networks and 

reinforcement learning. Mobile robot navigation using hybrid 


Parhi [60] have designed multilayer feed-forward neural network,

which controls the steering angle of the robot autonomously in 

the static and dynamic environments. The different obstacle 

distances are the inputs of the four-layered neural network, and

the steering angle is the output. Real-time collision-free path 

         

dynamic and unstructured environment


The researchers used a variety of algorithms, such as Deterministic, Nondeterministic, and Evolutionary algorithms, among others, for robot manipulators and obstacle detection in a variety of environments.

According to a review of the literature, many scientists only used these computational methods in complex environments for path planning and obstacle avoidance. Few scholars, on the other hand, have identified circumstances in which routing protocol is appropriate.

Many researchers, as per the literature review, have only shown computer simulation results without proving external robot capabilities.

A crucial research topic is nature-inspired algorithm-based mobile robot navigation and collision detection. Combining computational and nondeterministic algorithms is also a solid research option.


This study on mobile robot navigation divides the strategies into two categories: canonical and resistant approaches. The following are the study’s key points…

  • Reactive approaches outperform traditional approaches because they are better at dealing with uncertainty in the environment.
  • For actual navigation issues, communication is preferred.
  • In comparison to a single path, a daunting world publishes a small lot of papers.
  • There are far fewer papers on autonomous navigation for a moving goal issue in a challenging world than a moving extra hurdle problem.
  • To date, the majority of papers have only demonstrated a simulation analysis; papers on real-time applications are far scarcer.
  • There really are fewer papers on the navigation of various wifi mobile robots than on the steering of a single device robot system.
  • Hybrid algorithms have far fewer newspapers than game play algorithms.
  • New advanced algorithms such as SFLA, CS, IWO, BA, HS, DE, BFO, ABC, and FA have a lot of promise for navigation in an unfamiliar difficult system with maximum uncertainty, and these were used to develop hybrid approaches.
  • The efficiency of traditional approaches can be enhanced by combining them with the right to pursue.



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