Is A* better than previous path finding algorithms?

Path finding – or planning a route to a destination that bypasses obstacles – is a big deal in AI lately. In this respect, the A* search algorithm has attracted a lot of attention for its ability to solve path complexities. However, according to Stack overflow, there are situations where A* may not be the best algorithm for solving a problem. However, there are a number of parameters to evaluate which is the best algorithm for finding a solution.

Current bottlenecks in solving “Pathfinding”

In 2020, Science Direct published an article titled “A Systematic Review of the Literature on A* Pathfinding‘, by computer scientists Daniel Foead, Alfio Gefari, Marchel Budi Kusuma, Novita Hanafiahb, and Eric Gunawan, who proposed that A* algorithms are not infallible algorithms, and in several scenarios they require additional algorithms or modification to perform more delicate tasks. Also, as discussed in Multi-Agent Path Finding Problems, A* faces many obstacles, like conflicting paths. The traditional basic A* algorithm cannot keep up with the increasing demands of path finding and is obviously dying out, especially in the context of complex problems. Still, its improved and properly crafted variants are preferred to keep pace with increased efficiency.

Overcoming the Classic A* Algorithm Problem

Experts from different sectors have also pointed out the effectiveness and flaws of the A* algorithm. In a recent research paperYogendra Arya, associate professor at JC Bose University of Science and Technology, along with other researchers: Huanwei Wang, Jing Jing, Shangjie Lou, and Wei Liu, explain it this way: “Given the speculation, we propose three methods to improve the conventional A* algorithm, including expansion distance, bidirectional search and smoothing for better path finding problems‘. The article compares previous path-finding algorithms and resolves existing A* hurdles while explaining that informed search algorithms like A* are more efficient than Dijkstra or Breadth-Finding (BFS) via A-optimization methods * to optimize its effectiveness.

(The traditional A* algorithm, the A* algorithm with expansion distance, the bidirectional A* algorithm with expansion distance, and the EBS-A* algorithm. The scale of the map is 50 × 50 in the simulation test and the size of each obstacle is 5×5 on the map.The location of obstacles is randomly generated on the map according to the center point, but there are some rules. obstacle occupies a certain proportion of the scale of the map, which is interpreted as the number of center points of the obstacles being 1% of the scale of the map.)
(Experimental results show that the speed of the EBS-A* algorithm is improved by about 328% compared to that of the classic A* algorithm)

Arya and her team fully recognize that over the past decades, several applications have demonstrated different path planning methods for automated vehicles and robots. Classical path planning algorithms consist of ant colony optimization algorithms, genetic algorithms, and the A* algorithm. At the same time, the A* algorithm is based on the concept of graph search and is one of the most widely used path finding methods.

They further focused on the research of other researchers, but pointed out current flaws regarding small path distances and right-angle turn speed change, affecting the performance of holistically planned paths. To improve the efficiency of the traditional A* algorithm, path smoothing and expansion distance are introduced in the proposed algorithm. The team also said that to avoid collisions and maintain distance from obstacles – and to keep extended nodes from moving – they designed the new algorithm to improve path planning speed.

A novel approach

The research work titled “The EBS-A* Algorithm: An Improved A* Algorithm for Path Planning”, proposed a new approach to solving the problems faced by conventional A* algorithms.

Considering the aspects and problems of the traditional A* algorithm, the method proposed in the paper formed a new algorithm called “EBS-A*”. Simulation tests conducted as part of the research support the proposed theory to show the results compared within conventional A* and EBS-A* in the context of speed and path robustness.

The study suggested another way to overcome the shortcomings of the A* algorithm, ensuring a smooth path. He proposed a comprehensive planning method that perceives the characteristics of the local environment. This method will allow the A* algorithm to design the globally optimal path in a known static atmosphere – thus eliminating redundant nodes and developing local sequence nodes on the eliminated global path to optimize it on the global path, thus guaranteeing the A* performance in a dynamic atmosphere.

Until now, there was no existing algorithm pointing the growth towards the full performance of the A* algorithm. But the improved algorithm is believed to play a crucial role in the autonomous navigation of mobile robots. Since mobile robots are widely adopted in the real world, it is important to provide a powerful and highly efficient A* algorithm that can address potential applications and bring commercial value to the industrial sector.

Juxtaposition of the efficiency of the A* algorithm with Dijkstra, DFS and BFS

When you compare Dijkstra’s old algorithm with the A* algorithm, the latter indeed outweighs the productivity of Dijkstra’s algorithm. While both address the problem of finding the shortest path solution, Dijkstra does not pay much attention to the pragmatism of the solution. Even after consisting of traversal algorithms such as DFS and BFS, A* becomes the preferred path solver in light of the drawbacks of Dijkstra, DFS and BFS in that it requires traversing the map completely, which puts the emphasis on calculation, low productivity and low collision aspect. The long computation time of earlier algorithms decreases efficiency with growing map scale, where the A* algorithm stands out for its ability to provide the shortest path across the map by bypassing nodes and implying the minimum path cost.

Sharon D. Cole