# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 1 Introduction  
Mobile robots have gained widespread application in fields such as industrial production, transportation, and the service industry, becoming a focal point in current technological research [1; 2]. Path planning refers to the process by which robots navigate autonomously based on environmental perception through their sensors [3; 4], and its algorithm design and optimization cannot be separated from the effective deployment of sensors [5]. The autonomous navigation of mobile robots is an intelligent system encompassing technologies such as environmental perception, path planning, and navigation positioning [6; 7]. Path planning is a core research issue within this system, with its goal being to find a path from a starting point to a destination while ensuring that the robot avoids collisions with obstacles in the environment. Moreover, the path should meet one or more criteria, such as shortest distance, least time, or minimum energy consumption [8], ensuring the efficiency and reliability of pathways is also critical to achieving a highly automated urban transportation network [9]. Over the past few decades, numerous path planning algorithms have been proposed and widely applied in various fields, including, but not limited to, autonomous driving, drone navigation, and industrial robots. The study of path planning problems was initially carried out based on a grid-based approach, which first divides the environmental map into a series of grid cells. For example, graph search algorithms like A* [10; 11] and D* [12], while theoretically robust and offering optimal solutions at a consistent resolution, are hampered by a significant drawback due to the challenges in selecting an appropriate a priori resolution. When the resolution is set too low, the quality of the resultant paths tends to be suboptimal. Conversely, setting a high-resolution leads to an exponential increase in computational costs, especially in the context of high-dimensional space problems, necessitating prolonged processing times. Another issue istheir lack of real-time efficiency, as these algorithms require re-graphing and replanning, resulting in reduced efficiency. Optimization-based methods, such as artificial potential field methods [13], are widely used due to their ease of implementation but may fall into local minima. With the advancement of technology, computing power has been greatly improved and more complex algorithms have been developed, so geometry-based methods have begun to be studied and applied. For example, the Voronoi diagram [14]. The Voronoi diagram divides the space into a series of polygonal regions; each region contains a generating point, and the distance from any point in the region to the generating point is less than the distance to other generating points. The Voronoi diagram guides the mobile robot to avoid obstacles by generating paths within a safe distance from the edges of obstacles, but in highly complex environments, the Voronoi diagram may generate too many Voronoi diagrams, and the generated paths tend to bypass the midpoint of the obstacle, making it difficult to find an optimal solution. This is especially true in environments with many small obstacles or obstacles with complex shapes. Subsequently, sampling-based path planning methods have received a lot of attention. Sample-based planning methods, due to their ability to provide feasible paths for robots in the short term, have garnered considerable interest. For instance, the Rapidly Exploring Random Tree (RRT) [15] method consistently samples independent states in the search space and incrementally extends towards the goal state, offering the robot a path around obstacles. Another approach, RRT-Connect, proposed by [PERSON] et al. [16], improves the speed of finding feasible paths by expanding two search trees simultaneously from the starting point and the endpoint. However, although RRT exhibits good computational efficiency, the path it produces is often not optimal, causing the robot to take more time to reach the target location.