A Study on Rapidly Exploring Random Tree Algorithms for Robot Path Planning

Robot path planning is a critical feature of autonomous systems. Rapidly-exploring Random Trees (RRT) is a path planning technique that randomly samples the robot configuration space to find a path between the start and end point. This thesis studies and compares the performance of four important RRT algorithms, namely, the original RRT, the optimal RRT (also termed RRT*), RRT*-Smart, and Informed RRT* for six different environments. The performance measures include the final path length (which is also the shortest path length found by each algorithm), time to find the first path, run time (of 1000 iterations) for each algorithm, total number of sampling nodes, and success rate (out of 100 runs). It is found that both RRT*-Smart and Informed RRT* algorithm result in shorter path lengths than the original RRT and RRT*. Typically, RRT*-Smart can find a suboptimal path in less number of iterations while the Informed RRT* is able to find the shortest path with increased number of iterations. On the other hand, the original RRT and RRT* are better suited for real-time applications as the Informed RRT* and RRT*-Smart have longer run time due to the additional steps in their processes