Behind the Scenes: Introduction to Artificial Intelligence with Brian Yu - Chapter 7 - Moving

AI navigation in physical environments relies on path planning algorithms that balance efficiency with real-world constraints. Depth-First Search and Breadth-First Search provide foundational methods for traversing grids, while Dijkstra’s algorithm and A* search optimize for cost and speed by incorporating heuristics. Moving beyond abstract grids, physical agents must navigate continuous spaces, requiring motion planning that accounts for vehicle kinematics, turning radii, and irregular obstacle shapes. To manage uncertainty and ensure safety, autonomous systems integrate sensor data from cameras and LIDAR. Region-based convolutional neural networks enable precise object detection, while probabilistic models help robots and self-driving cars reconcile sensor inputs to maintain accurate localization. These technologies collectively transform AI from virtual decision-making engines into agents capable of navigating complex, dynamic, and unpredictable real-world environments.