# Mobile Robot Path Planning Considering Obstacle Gap Features
## 1 Introduction
Therefore, it is necessary to design a local path planning method with strong capabilities to traverse narrow spaces to ensure that the robot can successfully complete the task. Moreover, the path generated by the A* algorithm is composed of adjacent grid nodes, and the distance between each path node is very small. During the path tracking process, due to the large number of path nodes and the short distance between adjacent nodes, it is difficult for the robot to achieve smooth path tracking. (3) Generally, global path planning can obtain the optimal path in static environments, but it cannot handle local changes, and replanning methods make it difficult to achieve real-time performance in large-scale environments. Local path planning can be applied to unknown environments with real-time obstacle avoidance capabilities, but it is easy to fall into a local minimum and cannot guarantee global optimization. Hybrid path planning methods aim to combine these two methods to achieve global optimization and local avoidance of robot navigation in large-scale environments. The weak collaborative ability between global path planning and local path planning is also a problem to be solved. Traditional path planning methods often treat global path planning and local path planning as two independent stages, lacking effective collaborative capabilities. [PERSON] et al. [15] demonstrated a hybrid approach combining global graph search with the Dynamic Window Approach (DWA), while [PERSON] et al. [16] integrated vision-based global planning with potential field methods to enhance obstacle avoidance safety. [PERSON] et al. [17] proposed an adaptive fusion of A* and improved DWA, though their method requires precise knowledge of the robot's dynamic characteristics for velocity space computation. Currently, the research on hybrid path planning algorithms is insufficient to achieve global path planning and simultaneous path tracking and obstacle avoidance [18]. This separated planning method may lead to problems when the robot executes the path, such as inconsistency between the globally planned path and the locally planned path, or the local planning cannot meet the requirements of global planning. Therefore, it is necessary to design a method that can effectively coordinate global path planning and local path planning to improve the overall performance of robot path planning. In this context, recent developments have introduced more adaptive and goal-oriented algorithms for obstacle avoidance. For example, the Intelligent Bug Algorithm (IBA) [19], a novel obstacle avoidance strategy, outperforms traditional Bug algorithms by reaching the target faster, following smoother and shorter paths, and maintaining computational simplicity. Simulation results demonstrate that IBA effectively navigates environments with obstacles, showcasing its potential for real-world applications. Therefore, a path planning method for mobile robots that take into account the characteristics of obstacle gaps is proposed in this paper. Based on the A* algorithm, the proposed method optimizes the orientation map using the gap characteristics of the global map to improve search efficiency first. Next, the optimal guide angle of adjacent obstacles is calculated to guide the robot to pass through the gap at the desired optimal angle. At last, the robot motion model is adopted to generate the global path control points to meet the actual local path planning requirements and improve the synergy of hybrid path planning. The remainder of this paper is organized as follows. Section 2 presents the methodology of the proposed path planning method with clearance characteristics. This includes the global path planning based on the gap map, with subsections detailing the improvement of the heuristic function, the search point selection strategy based on gap optimization, the path optimization based on gap extraction, and the procedure of the proposed algorithm. Additionally, Section 2.2 introduces the real-time robot control method for optimization of the clearance angle.