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259 lines
8.0 KiB
Python
259 lines
8.0 KiB
Python
"""
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https://en.wikipedia.org/wiki/Bidirectional_search
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"""
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from __future__ import annotations
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import time
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from math import sqrt
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# 1 for manhattan, 0 for euclidean
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HEURISTIC = 0
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grid = [
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[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[1, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 1, 0, 0],
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]
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delta = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
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TPosition = tuple[int, int]
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class Node:
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"""
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>>> k = Node(0, 0, 4, 3, 0, None)
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>>> k.calculate_heuristic()
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5.0
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>>> n = Node(1, 4, 3, 4, 2, None)
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>>> n.calculate_heuristic()
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2.0
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>>> l = [k, n]
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>>> n == l[0]
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False
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>>> l.sort()
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>>> n == l[0]
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True
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"""
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def __init__(
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self,
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pos_x: int,
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pos_y: int,
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goal_x: int,
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goal_y: int,
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g_cost: int,
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parent: Node | None,
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) -> None:
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self.pos_x = pos_x
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self.pos_y = pos_y
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self.pos = (pos_y, pos_x)
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self.goal_x = goal_x
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self.goal_y = goal_y
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self.g_cost = g_cost
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self.parent = parent
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self.h_cost = self.calculate_heuristic()
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self.f_cost = self.g_cost + self.h_cost
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def calculate_heuristic(self) -> float:
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"""
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Heuristic for the A*
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"""
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dy = self.pos_x - self.goal_x
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dx = self.pos_y - self.goal_y
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if HEURISTIC == 1:
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return abs(dx) + abs(dy)
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else:
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return sqrt(dy**2 + dx**2)
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def __lt__(self, other: Node) -> bool:
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return self.f_cost < other.f_cost
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class AStar:
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"""
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>>> astar = AStar((0, 0), (len(grid) - 1, len(grid[0]) - 1))
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>>> (astar.start.pos_y + delta[3][0], astar.start.pos_x + delta[3][1])
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(0, 1)
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>>> [x.pos for x in astar.get_successors(astar.start)]
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[(1, 0), (0, 1)]
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>>> (astar.start.pos_y + delta[2][0], astar.start.pos_x + delta[2][1])
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(1, 0)
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>>> astar.retrace_path(astar.start)
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[(0, 0)]
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>>> astar.search() # doctest: +NORMALIZE_WHITESPACE
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[(0, 0), (1, 0), (2, 0), (2, 1), (2, 2), (2, 3), (3, 3),
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(4, 3), (4, 4), (5, 4), (5, 5), (6, 5), (6, 6)]
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"""
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def __init__(self, start: TPosition, goal: TPosition):
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self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
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self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
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self.open_nodes = [self.start]
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self.closed_nodes: list[Node] = []
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self.reached = False
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def search(self) -> list[TPosition]:
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while self.open_nodes:
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# Open Nodes are sorted using __lt__
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self.open_nodes.sort()
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current_node = self.open_nodes.pop(0)
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if current_node.pos == self.target.pos:
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return self.retrace_path(current_node)
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self.closed_nodes.append(current_node)
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successors = self.get_successors(current_node)
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for child_node in successors:
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if child_node in self.closed_nodes:
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continue
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if child_node not in self.open_nodes:
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self.open_nodes.append(child_node)
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else:
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# retrieve the best current path
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better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
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if child_node.g_cost < better_node.g_cost:
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self.open_nodes.append(child_node)
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else:
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self.open_nodes.append(better_node)
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return [self.start.pos]
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def get_successors(self, parent: Node) -> list[Node]:
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"""
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Returns a list of successors (both in the grid and free spaces)
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"""
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successors = []
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for action in delta:
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pos_x = parent.pos_x + action[1]
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pos_y = parent.pos_y + action[0]
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if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
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continue
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if grid[pos_y][pos_x] != 0:
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continue
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successors.append(
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Node(
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pos_x,
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pos_y,
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self.target.pos_y,
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self.target.pos_x,
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parent.g_cost + 1,
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parent,
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)
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)
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return successors
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def retrace_path(self, node: Node | None) -> list[TPosition]:
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"""
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Retrace the path from parents to parents until start node
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"""
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current_node = node
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path = []
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while current_node is not None:
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path.append((current_node.pos_y, current_node.pos_x))
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current_node = current_node.parent
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path.reverse()
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return path
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class BidirectionalAStar:
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"""
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>>> bd_astar = BidirectionalAStar((0, 0), (len(grid) - 1, len(grid[0]) - 1))
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>>> bd_astar.fwd_astar.start.pos == bd_astar.bwd_astar.target.pos
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True
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>>> bd_astar.retrace_bidirectional_path(bd_astar.fwd_astar.start,
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... bd_astar.bwd_astar.start)
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[(0, 0)]
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>>> bd_astar.search() # doctest: +NORMALIZE_WHITESPACE
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[(0, 0), (0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (2, 4),
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(2, 5), (3, 5), (4, 5), (5, 5), (5, 6), (6, 6)]
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"""
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def __init__(self, start: TPosition, goal: TPosition) -> None:
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self.fwd_astar = AStar(start, goal)
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self.bwd_astar = AStar(goal, start)
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self.reached = False
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def search(self) -> list[TPosition]:
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while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
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self.fwd_astar.open_nodes.sort()
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self.bwd_astar.open_nodes.sort()
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current_fwd_node = self.fwd_astar.open_nodes.pop(0)
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current_bwd_node = self.bwd_astar.open_nodes.pop(0)
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if current_bwd_node.pos == current_fwd_node.pos:
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return self.retrace_bidirectional_path(
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current_fwd_node, current_bwd_node
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)
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self.fwd_astar.closed_nodes.append(current_fwd_node)
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self.bwd_astar.closed_nodes.append(current_bwd_node)
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self.fwd_astar.target = current_bwd_node
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self.bwd_astar.target = current_fwd_node
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successors = {
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self.fwd_astar: self.fwd_astar.get_successors(current_fwd_node),
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self.bwd_astar: self.bwd_astar.get_successors(current_bwd_node),
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}
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for astar in [self.fwd_astar, self.bwd_astar]:
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for child_node in successors[astar]:
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if child_node in astar.closed_nodes:
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continue
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if child_node not in astar.open_nodes:
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astar.open_nodes.append(child_node)
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else:
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# retrieve the best current path
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better_node = astar.open_nodes.pop(
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astar.open_nodes.index(child_node)
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)
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if child_node.g_cost < better_node.g_cost:
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astar.open_nodes.append(child_node)
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else:
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astar.open_nodes.append(better_node)
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return [self.fwd_astar.start.pos]
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def retrace_bidirectional_path(
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self, fwd_node: Node, bwd_node: Node
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) -> list[TPosition]:
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fwd_path = self.fwd_astar.retrace_path(fwd_node)
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bwd_path = self.bwd_astar.retrace_path(bwd_node)
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bwd_path.pop()
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bwd_path.reverse()
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path = fwd_path + bwd_path
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return path
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if __name__ == "__main__":
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# all coordinates are given in format [y,x]
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init = (0, 0)
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goal = (len(grid) - 1, len(grid[0]) - 1)
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for elem in grid:
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print(elem)
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start_time = time.time()
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a_star = AStar(init, goal)
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path = a_star.search()
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end_time = time.time() - start_time
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print(f"AStar execution time = {end_time:f} seconds")
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bd_start_time = time.time()
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bidir_astar = BidirectionalAStar(init, goal)
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bd_end_time = time.time() - bd_start_time
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print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")
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