# Mobile Robot Path Planning Considering Obstacle Gap Features
## 1 Introduction  
Path planning of mobile robots is one of the most important problems in robot navigation [1]. Typically, the mobile robot should be able to plan paths to complete a variety of tasks efficiently, including avoiding obstacles in complex environments, traversing tight spaces, and so on. In traditional path planning methods, the obstacles were treated as insurmountable, and the shortest path to avoid the obstacles was the only target. For instance, in agricultural automation, robots are required to operate autonomously in fields, performing tasks like seed planting or crop monitoring, where reliable navigation through uneven terrains and obstacle-rich environments is essential [2]. However, there may be enough gaps between the obstacles in some cases, through which the mobile robot could traverse the obstacles more quickly. Therefore, a more flexible and efficient path planning solution could be obtained by considering the clearance between obstacles [3]. The traditional global path planning methods, such as the A* algorithm [4], Dijkstra algorithm [5], RRT* algorithm, and intelligent optimization algorithm, are usually based on graph search algorithms. The main problems with these algorithms, which need to traverse the entire map and calculate the cost of each possible path, fall into the following three categories:(1) When the area of the map is too large or there are lots of obstacles in it, the time consumption of the global path planning algorithm will increase significantly [6], resulting in the low efficiency of the robot's path planning. For example, when the A* algorithm is adopted for searching the optimal path in a large-scale scene, the time consumption will increase significantly due to the increase in the calculation amount of the evaluation function [7]. To tackle the issue of excessive computation in the A* algorithm, [PERSON] et al. [8] improved the specific calculation method of the evaluation function, thereby reducing the redundant points and inflection points in the generated path by adjusting the weight ratio of the evaluation function. To further improve the search efficiency, [PERSON] et al. [9] proposed a bidirectional adaptive A* algorithm that employs a direction search strategy to enhance expansion efficiency, along with adaptive step size and weight strategies to accelerate the search speed. [PERSON] et al. [10] addressed the limitations of the sub-node search strategy in the A* algorithm by proposing the Jump Point Search (JPS) algorithm, which significantly improves path search efficiency through jump point selection. However, the highly efficient JPS algorithm does not apply to unstructured scenarios. Additionally, traditional global path planning methods often generate redundant paths containing unnecessary turning points. These redundant points not only increase the robot's travel distance but may also hinder its passage through narrow spaces. Therefore, it is necessary to design a global path planning method that reduces redundant turning points to improve path planning effectiveness. Consequently, an efficient global path planning method needs to be proposed to accelerate path search and reduce computational complexity. (2) In complex environments, robots may need to traverse narrow passages or avoid narrow obstacles [11]. [PERSON] et al. [12] proposed a real-time A* search algorithm to handle environments with small obstacles, while [PERSON] et al. [13] developed a dynamic pruning A* algorithm specifically for replanning in navigation grids. [PERSON] et al. [14] further introduced a bi-directional A* algorithm based on weighted cost graphs for path planning and replanning. However, A* algorithms and replanning methods are not suitable for complex environments, and their path search time will multiply with the increase in map size. Relevant methods often cannot effectively handle this situation, leading to the robot's failure to successfully pass through narrow spaces.