Comparative Analysis of Hybrid A* and RRT* Algorithms and Optimizing the RRT* Algorithm in Autonomous Maze Navigation

The path planning problem of autonomous mobile robots is a very important problem in today's robotics field, and after decades of development, many path planning algorithms have been derived, among which the A* algorithm and the Rapidly Exploring Random Trees (RRT) algorithm are more widely used. These two methods use completely different basic principles, the A* algorithm relies on global search, while the RRT algorithm uses random search, in order to explore the advantages and disadvantages of the two algorithms, this paper mainly analyzes the principles of the A* algorithm and the RRT* algorithm and compares their performances in the maze navigation of robots. After that, the RRT* algorithm is also improved to make up for the shortcomings of the lower efficiency of the RRT* algorithm by utilizing a bidirectional search strategy, and then finally curve fitting is used to obtain a path that is more suitable for the robot's motivation. After comparative analysis, the A* algorithm is more suitable for precise path planning given a global map, and it can calculate the best path quickly. The RRT* algorithm is more suitable for the path planning problem of an unknown map, and due to the principle of stochastic search, it can make the algorithm calculate a reachable path faster.