The EBS-A* algorithm: An improved A* algorithm for path planning

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%.

A-star Optimization with Heap-sort Algorithm on NPC Character

“…-Node [0, 3] -Node [0, 2] F(x) = 80 -Node (1,2) F(x) = 80 -Node (1,3) F(x) = 66 (Node selected) 3. Calculate F(x) from nodes around (1,3) towards the position of the player -Node (1,3) -Node (2,2) F(x) = 86 -Node (2,3) F(x) = 72 (Node selected) 4.…”

“…-Node [0, 3] -Node [0, 2] F(x) = 80 -Node (1,2) F(x) = 80 -Node (1,3) F(x) = 66 (Node selected) 3. Calculate F(x) from nodes around (1,3) towards the position of the player -Node (1,3) -Node (2,2) F(x) = 86 -Node (2,3) F(x) = 72 (Node selected) 4. Calculate F(x) from nodes around (2,3) towards the position of the player -Node (2,3) -Node (2,3) F(x) = 92 -Node (3,3) F(x) = 78 (Node selected) 5.…”

“…Calculate F(x) from nodes around (2,3) towards the position of the player -Node (2,3) -Node (2,3) F(x) = 92 -Node (3,3) F(x) = 78 (Node selected) 5. Calculate F(x) from nodes around (3,3) towards the position of the player -Node (3,3) -Node (2,4) F(x) = 98 -Node (3,4) F(x) = 88 -Node (4,4) F(x) = 78 (Node selected) 6. Calculate F(x) from nodes around (4,4) towards the position of the player -Node (4,4) -Node (3,5) F(x) = 94 -Node (4,5) F(x) = 84 -Node (5,5) F(x) = 78 (Node selected) 7.…”

“…Pathfinding has the goal of finding the shortest path to its destination. There are various algorithms that can be implemented to find a path; one of them is the A-Star algorithm (1,2) . This algorithm is one of the most famous pathfinding algorithms due to its flexibility and ability to be used on various surfaces to find the shortest path by selecting the path with the lowest value (3) .…”

Path Planning for Multiple Unmanned Vehicles (MUVs) Formation Shape Generation Based on Dual RRT Optimization

“…Among these methods, optimization-based methods include path planning algorithms. There are many unmanned vehicle path planning algorithms, such as the multi-objective genetic algorithm proposed by Luis et al [12], the EBS-A* algorithm proposed by Wang et al [13], the ant colony algorithm improved by M Miao C et al [14], and so on. Among these algorithms, the rapidly exploring random tree (RRT) has been widely used in the field of UGV path planning because of the advantages of rapid path search and significant collision avoidance, but there are also two problems brought about by the algorithm itself that need improvement.…”