Create README.md

Author: EricChen0104

README.md

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+# D* Path Planning Algorithm
+
+### D* Lite Pathfinding Visualization
+This project implements a visualization of the D* Lite pathfinding algorithm on a 2D grid map using Python. D* Lite is designed for efficient path planning in dynamic environments where obstacles may appear or disappear over time. Unlike traditional A*, which recomputes paths from scratch, D* Lite incrementally updates its existing path, making it ideal for real-time robotic navigation.
+
+The project features:
+- Interactive 2D grid map with dynamic obstacle updates
+- Visual explanation of node expansion and path repair
+- Clean Python implementation with clear data structures
+
+This tool is useful for students and researchers learning about adaptive pathfinding, autonomous agents, and search-based planning.
+
+## DEMO
+![螢幕錄影 Jun 17 2025 (1)](https://github.com/user-attachments/assets/a5c7630c-4e00-4258-b2c9-05d140d0ed41)
+
+### Installation
+clone the repository
+```bash
+ 
+git clone https://github.com/EricChen0104/D_star_lite_Algorithm_PYTHON.git
+cd D_star_lite_Algorithm_PYTHON
+ 
+```
+install the requirements
+```bash
+ 
+pip install -r "requirements.txt"
+ 
+```
+
+## D* Lite Algorithm Introduction
+
+D* Lite is an incremental heuristic search algorithm designed for path planning in dynamic environments. It can be seen as an optimized extension of A*, capable of efficiently updating paths when the map changes (e.g., newly discovered obstacles), rather than recomputing from scratch like standard A*.
+
+### Basic Concepts
+Each node maintains two key values:
+
+- `g(n)`: the current known cost from the start node to node `n`
+- `rhs(n)`: one-step lookahead value, representing the best cost to reach `n` through any predecessor
+
+The node priority is determined by a key function (similar to A*):
+
+`key(n) = [min(g(n), rhs(n)) + h(start, n), min(g(n), rhs(n))]`
+
+The algorithm always expands the node with the **smallest key**, and incrementally repairs the path when changes occur in the map.
+
+### Heuristic Function h(n)
+As with A*, a popular heuristic is the **Manhattan Distance**:
+
+`h(n) = |Xstart - Xn| + |Ystart - Yn|`
+
+This allows D* Lite to maintain efficiency while guaranteeing optimality, even as obstacles change.
+
+## References
+- Stentz, A. (1994). The D* algorithm for real-time planning of optimal traverses (p. 34). Carnegie Mellon University, the Robotics Institute.
+- https://www.cs.cmu.edu/~motionplanning/lecture/AppH-astar-dstar_howie.pdf 
+