A Comparative Analysis of Heuristic Approaches in the A* Algorithm for Path Finding in Autonomous Vehicles
Author : Rishita Tiwari, Ashwani Dwivedi and Shweta Sinha
Abstract :
In this article, we will give a more in-depth comparison of different heuristic approaches for A* pathfinding algorithm with respect to their implications to effectiveness calculated by computation and optimality of path. An investigation of four different heuristics—Manhattan, Euclidean, Diagonal and a Custom Hybrid where compared based on important performance metrics: computation time and path length —in simulation using a grid with characteristics representative of problems generally faced in the paths computed for an autonomous vehicle. The paper serves to enlighten the different kinds of heuristics and their effects on A* algorithm efficiency, which basically expresses a trade-off between speed and accuracy. The findings suggest that there is no one-size-fits-all heuristic; rather, it will be a custom solution tuned to the environmental constraints that yields better performance. Such potential implications are exciting for applications in areas such as robotics, gaming and autonomous vehicle navigation.
Keywords :
A* path finding algorithm, heuristic comparison, autonomous vehicle navigation, computation time and path length.
