def __init__(self, s_start, s_goal, N, heuristic_type):
self.s_start, self.s_goal = s_start, s_goal
self.heuristic_type = heuristic_type
self.Env = env.Env2()
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.N = N # number of expand nodes each iteration
self.visited = [] # order of visited nodes in planning
self.path = [] # path of each iteration
self.h_table = {} # h_value table
def __init__(self, s_start, s_goal, heuristic_type):
self.s_start = s_start
self.s_goal = s_goal
self.heuristic_type = heuristic_type
self.Env = env.Env2() # class Env
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.OPEN = [] # priority queue / OPEN set
self.CLOSED = [] # CLOSED set / VISITED order
self.PARENT = dict() # recorded parent
self.g = dict() # cost to come
def __init__(self, s_start, s_goal, heuristic_type):
self.s_start = s_start
self.s_goal = s_goal
self.heuristic_type = heuristic_type
self.Env = env.Env2() # class Env
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.OPEN_fore = [] # OPEN set for forward searching
self.OPEN_back = [] # OPEN set for backward searching
self.CLOSED_fore = [] # CLOSED set for forward
self.CLOSED_back = [] # CLOSED set for backward
self.PARENT_fore = dict() # recorded parent for forward
self.PARENT_back = dict() # recorded parent for backward
self.g_fore = dict() # cost to come for forward
self.g_back = dict() # cost to come for backward
def __init__(self, xI, xG):
self.xI, self.xG = xI, xG
self.env = env.Env2()
self.obs = self.env.obs_map()