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3d-search-visualizer's Introduction

3D Path-Finding Visualizer

In this Project, I utilize an A* algorithm given a 3D grid to find the optimal path from a given start to finish. You can use different heuristics (manhattan, euclidean) as well as varying ranges of motion (rigid, diagonal). The final path, grid space, and obstacles are plotted using MatplotLib on 3D axes. The black dots signify obstacles, the green signifies cells that have been searched, and the path is marked in red. Some examples are below:

  • manhattan distance
  • diagonal motion enabled

Manahttan distance heuristic_cube obstacle

  • euclidean distance
  • diagonal motion enabled

euclidean_heuristic_square

  • manhattan distance
  • diagonal motion disabled

manhattan_wall

  • euclidean distance
  • diagonal motion enabled

wall_euclidean_true

  • manhattan distance
  • diagonal motion enabled

manhattan_two_true

  • euclidean distance
  • diagonal motion enabled

two_obstacles_true

  • manhattan distance
  • diagonal motion disabled

yo

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