I. Obsolete Technologies
Drive Systems
- Early robots used hydraulic drive (e.g., Unimate in 1960s)
- Problems: Requires hydraulic station, maintains oil circuit seals, risk of oil leakage
- Control accuracy: ±1mm level
- Modern motor drive: ±0.01mm accuracy, simple maintenance, low energy consumption
- Over 90% of modern industrial robots use motor drive
Control System Evolution
| Era | Technology | Characteristics |
|---|
| First generation | Hard-wired, magnetic drum storage | Modifications require physical circuit changes |
| 1970s | Microprocessor digital control | Can achieve complex motion planning, supports online modifications |
| Modern | Digital control + PLC | Multi-core processor, real-time operating system |
Control Architecture
- Early: Open-loop control (relies on mechanical stops for positioning)
- Modern: Closed-loop control (encoder, force sensor feedback)
- Effect: Weld deviation reduced from 2mm to 0.1mm
II. Mainstream Technologies
Power Systems
- Electric servo drive: Permanent magnet synchronous motor + high-precision reducer
- Repeat positioning accuracy: 0.02-0.05mm
- Advantages: No oil contamination, low noise, high energy efficiency
Digital Control and PLC
- Layered design: Upper-level planning system → Mid-level motion controller → Lower-level servo drive
- Communication: EtherCAT/Profinet industrial Ethernet
Perception Systems
| Level | Sensors |
|---|
| Basic | Limit switches, photoelectric encoders |
| Environmental | ToF cameras, solid-state LiDAR, 3D structured light |
| Force sensing | Six-dimensional force/torque sensors |
Safety Collaboration Technology
- Full-joint torque monitoring (≥1kHz)
- ISO/TS 15066 safety standard
- Electronic speed limiting (≤1m/s)
- Explosive growth in collaborative robot market
III. Cutting-edge Technologies
AI-Driven Robotics
- Machine learning: Deep reinforcement learning (DQN, PPO) for autonomous optimization decision-making
- Computer vision: Transformer architecture (ViT), CLIP multimodal models
- Autonomous decision-making: Probabilistic graphical models + deep learning
Adaptive Control and Multimodal Perception
- Sensor fusion: Vision + tactile + auditory + IMU
- Adaptive control: Real-time MPC adjustment, impedance control, force-position hybrid control
Autonomous Navigation Technology
- SLAM evolution: Visual-inertial SLAM, LiDAR SLAM, semantic SLAM
- Navigation optimization: RRT*, deep learning path planning, dynamic obstacle avoidance
- Swarm coordination: Distributed control, auction-based task allocation
Bionic Structures and Soft Robotics
- Boston Dynamics Atlas: hydraulic drive
- Pneumatic artificial muscles (PAM), shape memory alloys
- Soft endoscopes, minimally invasive surgical instruments
Humanoid Robot Technology
- Whole-body dynamics control, ZMP balance algorithm
- Tesla Optimus, Agility Digit, Figure 01
Collaboration and Swarm Intelligence
- Distributed consensus algorithms
- Blockchain task allocation
- Swarm reinforcement learning
- Applications: UAV swarm coordination, ground robot swarm mapping
Summary
Cutting-edge technologies represent the future development direction of robotics, driving robots to become more intelligent, safer, and more aligned with human needs. Application boundaries will continue to expand from industrial production to home services, from healthcare to space exploration.