Path-finding and Planning in a 3D Environment An Analysis Using Bidirectional Versions of Dijkstra’s, Weighted A*, and Greedy Best First Search Algorithms

Due to its intricacy, path-finding is one of the most difficult optimization issues. As a result, evolutionary algorithms are favoured when it comes to finding viable solutions. However, when the number of control points and restrictions grows, computing a possible solution in a wider area consumes too much time. At this point, increasing the number of units to find a path collaterally decreases total execution time but adds to the problem’s complexity. Therefore, this problem is NP-hard, and heuristic-based algorithms have been employed to tackle it effectively. In this study, we have used bidirectional versions of Dijkstra’s, Weighted A*, and Greedy Best First Search Algorithms to find a path in a 26-neighbor 3D grid. We further discussed the efficiency obtained due to the use of two different data structures such as priority queue and priority queue based on min-heap to store the g-values.