I. Kinematics and Trajectory Planning
Kinematics Basics
- Forward kinematics: Input joint angles → via DH parameter method → output end effector pose
- Inverse kinematics: Input desired end pose → analytical or numerical iteration → output joint angle combinations
Trajectory Planning Methods
Joint Space Planning
- Cubic/quintic polynomial interpolation
- Trapezoidal velocity curve (accelerate-uniform-decelerate)
- S-curve velocity curve (smoother jerk control)
Cartesian Space Planning
- Linear interpolation: End moves along straight line
- Circular interpolation: End moves along arc path
- Orientation interpolation: Tool coordinate system rotates smoothly
Advanced Planning Technologies
| Type | Method |
|---|
| Time-optimal | Minimum time trajectory under dynamic constraints |
| Energy-optimal | Minimize motor energy consumption |
| Obstacle avoidance | RRT, PRM, artificial potential field |
Software Implementation Solutions
- Open-source frameworks: ROS MoveIt, OMPL, KDL
- Commercial software: RoboDK offline programming, DELMIA digital twin
II. Robot Vision and Positioning
Core Processing Algorithms
- Object detection: YOLOv5, Faster R-CNN
- Pose estimation: PnP algorithm or deep learning for 6D object pose prediction
- Semantic segmentation: Distinguish different object categories from background
- 3D reconstruction: Build environment models based on point cloud data
Typical Application Scenarios
| Scenario | Performance |
|---|
| Intelligent sorting | 60 pieces/min, positioning accuracy ±0.1mm |
| Vision-guided assembly | Real-time position deviation compensation |
| Dynamic obstacle avoidance | Response time <50ms |
Market Status
- 2024 global industrial robot vision system market: $4.2 billion
- 75% of newly installed robotic arms equipped with integrated vision systems
III. Artificial Intelligence and Machine Learning
Reinforcement Learning Applications
- Training process: Simulation environment → reward function design → PPO/SAC training → domain randomization → real robot fine-tuning
- Typical cases: Block flipping, threading through holes and other fine manipulation tasks
Imitation Learning
- Motion capture records human expert operations
- Behavior cloning or inverse reinforcement learning to extract strategies
- Application: Medical surgical robots learning surgeon techniques
Industry Trends
- AI penetration rate in industrial robotics expected to reach 65% in 2025
- Large language models enable natural language programming interfaces
- Vision-Language-Action (VLA) multi-modal models
IV. Force Control and Compliance Strategies
Force Control Implementation Methods
- Direct force control: Six-axis force/torque sensor for direct measurement
- Indirect force control: Estimate joint torque through motor current
- Hybrid control: Combines position loop and force loop
Typical Force Control Strategies
Impedance Control
- Principle: Establish virtual spring-damping system
F = Kx + Bv
- Applications: Peg-in-hole assembly, electronic component insertion, surface tracking
Force Tracking Control
- Real-time force sensor data acquisition (1000Hz)
- Compare with target force values and adjust end position via PID
- Applications: Precision polishing (20±1N), polishing, medical robots
Collaborative Robot Special Features
| Feature | Description |
|---|
| Manual guidance | Enter guidance mode when external force >2N, zero-force control |
| Collision detection | Response time <10ms, trigger protection at 15% deviation |
V. High-level Decision and Integration
System Architecture
- Task planning: Automatically generate optimal operation sequences
- Multi-machine coordination: Operation coordination and synchronization, real-time collision avoidance
- Integration interfaces: OPC UA, PROFINET for communication with PLC/MES
Industry 4.0 Integration Architecture
| Layer | Function |
|---|
| Cloud | Global scheduling, big data analysis, AI training |
| Edge | Local real-time decision making |
| Device | Execute control commands |
Communication Protocols
- ROS2 DDS for real-time control
- MQTT for cloud communication
- 5G network requires <100ms latency
Summary
The robotic arm software algorithm system forms a complete technology stack from low-level motion control to high-level decision integration. Currently, these advanced functions have demonstrated applications in high-end manufacturing sectors such as semiconductors and automotive. With the development of 5G and TSN network technologies, wider applications are expected within the next 3-5 years.