Overview of AI Control Systems in Robotics
Artificial Intelligence (AI) enables robots to perceive their environment, process data, and execute tasks autonomously. AI control systems consist of multiple components that work together to optimize robotic performance and decision-making.
Key Components of AI-Driven Robot Control:
- Perception: AI uses vision, LiDAR, and sensor fusion for object detection and environment mapping.
- Decision-Making: Machine learning algorithms process data to determine the best course of action.
- Motion Planning: AI calculates optimized movement paths using kinematic and dynamic models.
- Actuation: AI-driven controllers send precise signals to motors and actuators for accurate execution.
AI Control System Layers:
| Layer | Function | Examples |
|---|---|---|
| Perception | Processes sensor inputs and reconstructs environmental models. | Computer vision, depth sensing, LiDAR |
| Processing | AI interprets data, predicts outcomes, and generates responses. | Neural networks, reinforcement learning |
| Control | Regulates robotic movement and interaction with the environment. | PID controllers, model predictive control (MPC) |