Analysis of the Use of Different Heuristic Weights in the Weighted A* algorithm

Abstract

Path planning is a sequence of states to move objects from the initial state to the final state and avoid impassable areas. Objects here can be robots, autonomous cars, and others. The A* algorithm is a path search algorithm that uses distance estimation by using the closest path search to reach the destination. Weighted A* is an algorithm used to solve the pathfinding problem by changing the weight value in the heuristic function. The purpose of this study is to analyze the comparison of the Weighted A* algorithm with the A* algorithm and analyze the effect of the heuristic weight value on the Weighted A* algorithm. The tests carried out are using maze, narrow, trap, clutter environments. The results obtained in the comparison of the Weighted A* and A* algorithms, from the test results, the Weighted A* algorithm produces a better search time of 0.33 seconds, while the A* algorithm produces a time of 1.40 seconds. But the A* algorithm can produce a more optimal path of 163.69 than the Weighted A* algorithm which produces a path of 164.52. With a strategy that emphasizes choosing nodes that are closer to the goal node, Weighted A* can produce a path with a faster computation time. While the A* algorithm because it chooses the node with the smallest heuristic value, it can produce a more optimal path. Weighted A* is suitable to be implemented on systems that require shorter path-finding times but do not have to be optimal. The A* algorithm is suitable to be implemented in systems that require optimal paths even though the search time is not too fast