Abstract
Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the traditional A* algorithm has some limitations, such as slow planning speed, close to obstacles. In this paper, we propose an improved A*-based algorithm, called the EBS-A* algorithm, that introduces expansion distance, bidirectional search, and smoothing into path planning. The expansion distance means keeping an extra space from obstacles to improve path reliability by avoiding collisions. Bidirectional search is a strategy searching path from the start node and the goal node simultaneously. Smoothing improves path robustness by reducing the number of right-angle turns. In addition, simulation tests for the EBS-A* algorithm are performed, and the effectiveness of the proposed algorithm is verified by transferring it to a robot operating system (ROS). The experimental results show that compared with the traditional A* algorithm, the proposed algorithm improves the path planning efficiency by 278% and reduces the number of critical nodes by 91.89% and the number of right-angle turns by 100%.
Highlights
Mobile robots are a comprehensive system that integrates environmental perception, dynamic decision-making, behavior control, task planning, and execution
The expansion distance means keeping an extra space from obstacles to improve path reliability by avoiding collisions, for the expanded nodes are no longer traversed, the speed of path planning is improved
The A algorithm is a path planning algorithm based on graph search, which is developed from breadth-first search (BFS)
Summary
Mobile robots are a comprehensive system that integrates environmental perception, dynamic decision-making, behavior control, task planning, and execution. Scholars have performed much research on the A algorithm, there are still some defects, such as small distances between the path and obstacles and slow speed due to right-angle turns These factors lead to decreasing robustness of the planned path. To improve the speed of path planning, bidirectional search strategy is introduced into the proposed algorithm. The main contributions of the research in this paper are as follows: we propose three methods for the improvements of the conventional A algorithm, including expansion distance, bidirectional search, and smoothing. We introduced these methods into A algorithm form a new algorithm named EBS-A.
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