Artificial Intelligence is the driving force behind modern robots’ ability to operate autonomously, adapt to dynamic environments, and perform complex tasks that go beyond pre-programmed movements. AI enables robots to perceive, reason, learn, and make decisions, transforming them from mere machines into intelligent agents. This section explores how AI is integrated into robotic control and navigation.
Perception (Computer Vision for Robots) #
Robots rely heavily on computer vision to understand their surroundings. AI-powered perception systems allow robots to:
- Object Recognition and Detection: Identifying and locating objects in the environment (e.g., for grasping, manipulation, or avoidance). Deep learning models (CNNs) are extensively used here.
- Scene Understanding: Interpreting the overall context of a scene, including identifying different surfaces, obstacles, and navigable areas.
- 3D Reconstruction: Building a 3D model of the environment from sensor data (e.g., from stereo cameras, LiDAR, or RGB-D cameras). This is crucial for accurate navigation and interaction.
- Semantic Segmentation: Pixel-level classification of objects and regions, allowing robots to understand the meaning of different parts of their environment (e.g., distinguishing between a drivable road and a sidewalk).
Localization and Mapping (SLAM) #
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