RRT Algorithm Implementation and Simulation for Robot Path Planning to Obtain the Shortest Path

In this project, we employed the MATLAB programming language to implement the Rapidly-exploring Random Tree (RRT) algorithm. The RRT algorithm is a widely-used path planning method capable of exploring optimal paths in complex environments. We applied this algorithm to robot path planning and, through simulation experiments, successfully derived the robot's shortest path. Key implementation steps involved: - Random node generation within the configuration space - Nearest neighbor search using Euclidean distance calculations - Collision detection checks to ensure feasible paths - Tree expansion towards random samples while avoiding obstacles We conducted multiple experiments to validate the accuracy of the results and made algorithmic adjustments to improve performance, such as incorporating path smoothing techniques and optimizing sampling strategies. In the future, we plan to further enhance the algorithm through mechanisms like RRT* for asymptotic optimality and apply it to broader domains including autonomous vehicles and robotic manipulation tasks.