path_planning
Path planning algorithms on grid map
installation
pip install -r requirements.txtdescription
there are astar, jps, jpsplus, bbox_pruning.
all results are described in jupyter notebook.
p.s. pruning algorithm can be applied for each solvers.
routine run
from solver.jpsplus import JPSPlus #define the solver
from solver.pruning.bbox import BBoxPruning #define pruning
from utils.distance import diagonalDistance #define h function
from solver.base import findPathBase #define search function
from graph.node import Node #define Node for start/finish
from graph.grid import GridMap #define occupancy grid map via string
from evaluation.test import simpleTest #define eval function
startNode = Node(x_start, y_start) #define start Node
finishNode = Node(x_finish, y_finish) #define finish Node
grid = GridMap() #define grid Map
grid.readFromString(mapstr, width, height) #see additionals in main.ipynb
#routine run - always call solver.doPreprocess before eval
prune = BBoxPruning()
solver = JPSPlus(diagonalDistance, prune)
solver.doPreprocess(grid)
simpleTest(solver, findPathBase, grid, startNode, goalNode, visualise=True)experimental results
MovingAI grid maps were used for experiments. We have chosen two maps lak307d as easy map and ost002d as complex map in order to show differences in results.
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