daniel-s-ingram/ai_for_robotics

Implements core probabilistic localization and planning algorithms including histogram filters, Kalman filters, particle filters, A* and D* search, and SLAM, each with animated visualizations showing convergence behavior and real-world performance. The project uses Python with matplotlib animations to demonstrate how uncertainty propagates through dead reckoning alone versus with landmark sensing, and visualizes control strategies from basic proportional controllers to full PID feedback loops. Each lesson module builds progressively from 1D to 2D implementations, enabling side-by-side comparison of different filtering and pathfinding approaches for autonomous navigation.

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