Rapidly-Exploring Random Tree (RRT) Path Planning Example in MATLAB

This is a MATLAB-based implementation example of the Rapidly-Exploring Random Tree (RRT) algorithm. RRT is a probabilistic path planning algorithm that mimics tree growth through random sampling to explore valid paths in configuration space. The implementation follows these key development phases: 1. Problem Definition: The code establishes the environment configuration, defines start and goal positions, and incorporates robot kinematic constraints through parameter initialization functions. 2. RRT Algorithm Implementation: The core algorithm involves several MATLAB functions including: - Random sampling using rand() function for state space exploration - Nearest neighbor selection through Euclidean distance calculations - Path extension with step size control and collision checking routines - Tree data structure management using node-parent relationships 3. Path Optimization: Post-processing techniques are applied to smooth the generated path, potentially using interpolation methods or shortcut algorithms to improve path quality and reduce unnecessary waypoints. 4. Result Visualization: MATLAB's plotting capabilities are utilized to display the exploration tree, final path, and environment obstacles, providing intuitive visual feedback about the algorithm's performance and solution quality. This example serves as an educational tool for understanding RRT's fundamental principles and implementation details. Users can modify parameters such as step size, sampling bounds, and obstacle configurations to test the algorithm's adaptability across different scenarios and evaluate its computational efficiency in various environments. The modular code structure allows for easy extension to more complex variants like RRT* or bidirectional RRT.