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@@ -4,29 +4,30 @@ import env
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class Astar:
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- def __init__(self, x_start, x_goal, heuristic_type):
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+ def __init__(self, x_start, x_goal, e, heuristic_type):
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self.xI, self.xG = x_start, x_goal
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+ self.e = e
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+ self.heuristic_type = heuristic_type
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self.Env = env.Env() # class Env
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- self.plotting = plotting.Plotting(self.xI, self.xG) # class Plotting
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self.u_set = self.Env.motions # feasible input set
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self.obs = self.Env.obs # position of obstacles
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- self.path, self.policy, self.visited = self.searching(self.xI, self.xG, heuristic_type)
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-
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- self.fig_name = "A* Algorithm"
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- self.plotting.animation(self.path, self.visited, self.fig_name) # animation generate
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-
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- def searching(self, xI, xG, heuristic_type):
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+ def searching(self):
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"""
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Searching using A_star.
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:return: planning path, action in each node, visited nodes in the planning process
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"""
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- q_astar = queue.QueuePrior() # priority queue
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- q_astar.put(xI, 0)
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+ xI = self.xI
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+ xG = self.xG
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+ heuristic_type = self.heuristic_type
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+ e = self.e
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+
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+ q_astar = queue.QueuePrior() # priority queue / OPEN
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+ q_astar.put(xI, e * self.Heuristic(xI, xG, heuristic_type))
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parent = {xI: xI} # record parents of nodes
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action = {xI: (0, 0)} # record actions of nodes
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visited = []
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@@ -43,7 +44,7 @@ class Astar:
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new_cost = cost[x_current] + self.get_cost(x_current, u_next)
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if x_next not in cost or new_cost < cost[x_next]: # conditions for updating cost
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cost[x_next] = new_cost
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- priority = new_cost + self.Heuristic(x_next, xG, heuristic_type)
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+ priority = new_cost + e * self.Heuristic(x_next, xG, heuristic_type)
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q_astar.put(x_next, priority) # put node into queue using priority "f+h"
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parent[x_next], action[x_next] = x_current, u_next
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@@ -108,8 +109,18 @@ class Astar:
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print("Please choose right heuristic type!")
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-if __name__ == '__main__':
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- x_Start = (5, 5) # Starting node
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- x_Goal = (49, 5) # Goal node
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+def main():
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+ x_start = (5, 5) # Starting node
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+ x_goal = (49, 5) # Goal node
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+
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+ astar = Astar(x_start, x_goal, 1, "manhattan")
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+ plot = plotting.Plotting(x_start, x_goal) # class Plotting
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- astar = Astar(x_Start, x_Goal, "manhattan")
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+ fig_name = "A* Algorithm"
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+ path, policy, visited = astar.searching()
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+
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+ plot.animation(path, visited, fig_name) # animation generate
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+
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+
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+if __name__ == '__main__':
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+ main()
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zhm-real