Detailed analysis of the classification and control system of industrial robots

First, the classification of industrial robots

1. According to the coordinate system, industrial robots can be categorized into several types:

(1) Cartesian Coordinate Robots

These robots use three linear movements along perpendicular axes (PPP), creating a rectangular workspace. They are known for their intuitive operation, easy programming, and high positioning accuracy. However, they tend to occupy more space and have limited flexibility, making it difficult to integrate with other robots.

(2) Cylindrical Coordinate Robots

This type combines one rotational movement with two linear motions, forming a cylindrical workspace. Compared to Cartesian robots, they offer better space efficiency but still face challenges in coordination with other machines. Their positional accuracy is slightly lower than that of Cartesian robots.

(3) Spherical (Polar) Coordinate Robots

Also known as polar-coordinate robots, these use two rotations and one linear motion (RRP). The workspace is spherical, allowing for greater reach and flexibility. They are suitable for tasks requiring vertical and horizontal movement, such as picking up objects from the ground. However, their accuracy decreases with arm length.

(4) Multi-Joint (Articulated) Robots

Similar to human arms, these robots have multiple rotating joints (RRR). They are compact, flexible, and ideal for working alongside other robots. Despite their advantages, they often suffer from lower position accuracy and complex control due to coupling between joints.

(5) Planar Joint Robots

Also called SCARA robots, these feature a prismatic joint and two rotational joints (PRR). They are highly flexible horizontally and rigid vertically, making them perfect for assembly tasks, especially in electronics manufacturing where small parts need precise placement.

2. Based on the driving method, industrial robots can also be classified into different types:

(1) Pneumatic Robots

These use compressed air to power the robot’s movements. They are fast, simple, and cost-effective, but their performance is affected by air compressibility, leading to less stable speed and limited force output—usually only a few tens of Newtons.

(2) Hydraulic Robots

Hydraulic systems provide higher force compared to pneumatic ones, with grip forces reaching thousands of Newtons. They are stable and responsive, but require strict sealing and are not ideal for extreme temperatures.

(3) Electric Robots

The most widely used type, electric robots offer flexibility, precision, and various motor options. They can be driven by stepper motors, DC or AC servo systems, and often use harmonic reducers for compact and efficient motion control.

Second, the Industrial Robot Control System

1. Industrial Robot Control Technology

While similar to traditional mechanical control systems, industrial robot control has unique features. It involves controlling multiple joints simultaneously, performing complex coordinate transformations, and managing nonlinear, multivariable models. Advanced techniques like feedforward, decoupling, and adaptive control are commonly used.

2. Basic Requirements for the Control System

A good control system must handle position, speed, and acceleration, support trajectory planning for continuous motion, allow user-friendly interaction, and include environmental sensing capabilities. These features help robots adapt to changing conditions and work efficiently in automated environments.

3. Classification of Control Systems

Control systems can be divided based on motion control methods, trajectory types, and speed/force control. Common types include program control, adaptive control, and AI-based systems. Each has its own strengths and is suited for different applications.

(1) Program Control Systems

These follow pre-programmed instructions to guide each joint, enabling precise spatial movement. They are widely used in repetitive tasks where accuracy is essential.

(2) Adaptive Control Systems

These adjust control parameters in real-time based on feedback from the robot's state and errors. This allows the system to improve performance over time and adapt to changing conditions.

(3) Artificial Intelligence Systems

AI-driven systems make decisions in real-time using sensor data, rather than relying on pre-programmed steps. They can learn from experience and optimize control strategies dynamically, making them ideal for complex and unpredictable environments.

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