Research Process of Path Planning based on RRT Algorithm and Its Improvements

Abstract

At present, path-planning algorithms in the field of robotics is a problem of research value. Especially in the field of robotics, it has great significance. A good algorithm can improve the efficiency of the robot and ensure the safety of the robot in complex environments. In addition, the path planning algorithm can improve the autonomy and intelligence of the robot, help the robot to realize autonomous navigation and decision-making, and through different algorithms, the robot can also play different roles in different fields. In the past few years, many relatively perfect algorithms have been formed and used. Such as A* algorithm, Q-learning, ant colony algorithm, RRT algorithm, etc. After consulting the relevant literature, the author mainly researches the applications of RRT algorithm and its improved algorithms (RRT*, RRT*-smart, Informed RRT*). There are also some examples of combined algorithms in this article. Therefore, the author will focus on RRT algorithm and its improvements and analyze the common target of these algorithms through examples and comparisons, which is to find a shorter and more accurate path. In addition, the algorithms also need to run faster. In the end, the advantages, and disadvantages of these algorithms in different environments and fields are summarized. In the future research, scientists can further optimize the performance and applicability of these improved algorithms for specific fields or application scenarios. Resulting in better improvements to solve path planning problems, improve efficiency and reduce costs.