Key Technology of Robot

Due to the demands of social development and the increasing applications of robots, there is a growing need for intelligent robots.

Intelligent robots operate in environments that are often unknown and unpredictable.

The research of such robots primarily involves the following key technologies:

Key Technology of Robot

(1) Multi-sensor information fusion

In recent years, multi-sensor information fusion technology has become a popular research topic. This technology combines control theory, signal processing, artificial intelligence, probability, and statistics to provide robots with technical solutions to perform tasks in complex, dynamic, uncertain, and unknown environments.

Robots utilize various types of sensors, which are categorized as internal measurement sensors and external measurement sensors depending on their purposes. Internal measurement sensors are used to detect the internal state of the robot’s components, including:

  • Specific position and angle sensors
  • Arbitrary position and angle sensors
  • Speed and angle sensors
  • Acceleration sensors
  • Tilt angle sensors
  • Azimuth sensors

External measurement sensors used by robots include:

  • Vision sensors for measuring and recognizing objects
  • Tactile sensors for detecting contact, pressure, and sliding
  • Force sensors for sensing force and torque
  • Proximity sensors and distance sensors
  • Angle sensors for measuring tilt, direction, and attitude.

Multi-sensor information fusion involves combining data from multiple sensors to produce more reliable, accurate, or comprehensive information.

The resulting multi-sensor system can provide a more accurate reflection of the characteristics of the detected object, eliminate information uncertainty, and improve information reliability.

Multi-sensor information fusion has the following characteristics: redundancy, complementarity, real-time response, and low cost.

There are several methods for multi-sensor information fusion, including:

  • Bayesian estimation
  • Dempster-Shafer theory
  • Kalman filter
  • Neural Networks
  • Wavelet transform

(2) Navigation and positioning

Autonomous navigation is a fundamental technology in the field of robotics research and a crucial and challenging issue in the robot system.

Navigation consists of three basic tasks:

  • Global positioning based on environmental understanding
    This involves identifying human-made road signs or specific objects in the environment to accurately locate the robot and provide data for path planning.
  • Target recognition and obstacle detection
    Real-time detection and identification of obstacles or specific targets helps improve the stability of the control system.
  • Security protection
    This involves analyzing obstacles and moving objects in the robot’s working environment to avoid damage to the robot.
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(3) Path planning

Path planning technology is a crucial branch of robotics research. It involves finding an optimal path from the initial state to the target state in the robot’s workspace while avoiding obstacles. The path’s optimality is based on one or more optimization criteria, such as minimum work cost, shortest walking route, or shortest walking time.

Path planning methods can be broadly classified into two categories: traditional and intelligent methods. The traditional path planning methods include the following:

  • Free space method
  • Graph search method
  • Grid decoupling method
  • Artificial potential field method

Although the global planning in robot path planning is primarily based on the above methods, there is a need for further improvement in path search efficiency and path optimization.

The artificial potential field method is a well-established and efficient planning method in traditional algorithms. It uses an environmental potential field model for path planning but does not take into account whether the path is optimal.

The intelligent path planning method applies artificial intelligence techniques like genetic algorithms, fuzzy logic, and neural networks to path planning. This approach aims to improve the obstacle avoidance accuracy of robot path planning, speed up the planning process, and meet the practical needs of various applications.

The following are some of the most commonly used algorithms:

  • Fuzzy method
  • Neural Networks
  • Genetic algorithm
  • Q learning
  • Hybrid algorithm

These methods have demonstrated promising research outcomes in scenarios where the obstacle environment is either known or unknown.

(4) Robot vision

The visual system is a crucial component of autonomous robots and typically comprises a camera, image capture card, and computer.

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The primary functions of a robot vision system include:

  • Image acquisition
  • Image processing and analysis
  • Output and display.

The core tasks of a robot vision system are feature extraction, image segmentation, and image recognition.

The accurate and efficient processing of visual information is a critical issue in developing a reliable vision system.

Currently, visual information processing is being gradually refined, which includes:

  • Compression and filtering of visual information
  • Detection of environmental and obstacle information
  • Identification of specific environmental signs
  • Perception and processing of 3D information.

Environmental and obstacle detection is one of the most important and challenging processes in visual information processing.

Edge extraction is a common technique used in visual information processing.

However, for mobile robots that need to process images in real-time, general image edge extraction methods, such as gradient method using local data and second-order differential method, can be insufficient.

Therefore, an image edge extraction method based on computational intelligence is proposed, such as neural network-based methods and fuzzy inference rule-based methods. Recently, Professor Bezdek J.C has extensively discussed the significance of using fuzzy logic inference for image edge extraction.

This method is specifically designed for visual navigation, which involves integrating road knowledge required for the robot to move outdoors, such as highway white line and road edge information, into the fuzzy rule base to enhance road recognition efficiency and robustness.

Furthermore, a combination of genetic algorithms and fuzzy logic has also been proposed.

(5) Intelligent control

With the advancement of robotics, traditional control theory has revealed its limitations in accurately modeling physical objects and handling ill-conditioned processes with inadequate information.

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In recent years, several scholars have proposed various intelligent control systems for robots. These include:

  • Fuzzy control
  • Neural network control
  • Fusion of intelligent control technologies

Integrating intelligent control technology involves the following:

  • Fusion of fuzzy control and variable structure control
  • Fusion of neural networks and variable structure control
  • Fusion of fuzzy control and neural network control

Intelligent fusion technology also encompasses fuzzy control methods based on genetic algorithms.

(6) Man-machine interface technology

The aim of man-machine interface technology is to facilitate communication between humans and computers.

To achieve this objective, it is necessary for the robot controller to have a friendly, flexible, and convenient human-machine interface. Additionally, the computer should be able to comprehend language, understand text, speak, and even translate between different languages. These functions are implemented based on knowledge representation methods.

Therefore, the study of man-machine interface technology holds great application value and basic theoretical significance.

Currently, significant progress has been made in man-machine interface technology. Technologies such as speech recognition and synthesis, text recognition, image recognition and processing, and machine translation have been successfully applied in practice.

Furthermore, human-machine interface devices and interactive technologies, monitoring technologies, remote operation technologies, and communication technologies are important components of man-machine interface technology.

Remote operation technology is an important area of research in this field.

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