pyhpp 0.1.5

Algorithms for Three Dimensional Path Planning

pyhpp

Python Package for Path Planning Algorithms

Steps

• Step 1: import A* algorithm

from pyhpp.a_star import AStar

• Step 2: prepare a JSON type scenario, e.g.,

scenario = {
    "dimension": {"x": 10, "y": 10, "z": 10},
    "waypoint": {
        "start": {"x": 5, "y": 9, "z": 2},
        "stop": {"x": 5, "y": 0, "z": 4},
        "allowDiagonal": False
    },
    "data": {
        "size": 16,
        "x": [4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7],
        "y": [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
        "z": [2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5]
    },
    "boundary": {
        "zCeil": 6,
        "zFloor": 1
    }
}

dimension: [required] whole dimension of the scenario. Two dimensional scenario can be set up when "z": 0
waypoint: [required] start and stop positions (default allowDiagonal is False)
data: obstacle data (set as empty if none)
boundary: the z-axis boundary of path for calculation

• Step 3: create an A* instance

a_star = AStar(scenario)

• Step 4: calculate and get the results

result = a_star.calculate_path()

visited_Q = result["visited_Q"]
final_Q = result["final_Q"]
path = result["path"]

This returned result contains the following main properties

visited_Q: all the visited positions
final_Q: all the positions in the A* path
path: the A* path array from start to stop
refined_path: the A* path with minimum number of points

and some useful information

message: the information about path planning
elapsed_ms: the running milliseconds of path planning