265 lines
10 KiB
Python
265 lines
10 KiB
Python
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from utils.bt.load import load_behavior_tree_lib
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from OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm
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import copy
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from tabulate import tabulate
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import numpy as np
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import os
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from sympy import symbols, Not, Or, And, to_dnf
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from OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm
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from BTExpansionAlgorithm import BTExpAlgorithm # 调用最优行为树扩展算法
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import time
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from utils.bt.draw import render_dot_tree
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from utils.bt.load import load_bt_from_ptml
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root_path = os.path.abspath(
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os.path.join(__file__, "../../..")
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)
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def goal_transfer_str(goal):
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goal_dnf = str(to_dnf(goal, simplify=True))
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# print(goal_dnf)
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goal_set = []
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if ('|' in goal or '&' in goal or 'Not' in goal) or not '(' in goal:
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goal_ls = goal_dnf.split("|")
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for g in goal_ls:
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g_set = set()
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g = g.replace(" ", "").replace("(", "").replace(")", "")
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g = g.split("&")
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for literal in g:
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if '_' in literal:
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first_part, rest = literal.split('_', 1)
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literal = first_part + '(' + rest
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# 添加 ')' 到末尾
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literal += ')'
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# 替换剩余的 '_' 为 ','
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literal = literal.replace('_', ',')
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g_set.add(literal)
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goal_set.append(g_set)
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else:
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g_set = set()
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w = goal.split(")")
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g_set.add(w[0] + ")")
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if len(w) > 1:
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for x in w[1:]:
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if x != "":
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g_set.add(x[1:] + ")")
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goal_set.append(g_set)
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return goal_set
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def collect_action_nodes(random):
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multiple_num=2
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action_list = []
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behavior_dict = load_behavior_tree_lib()
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for cls in behavior_dict["act"].values():
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if cls.can_be_expanded:
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print(f"可扩展动作：{cls.__name__}, 存在{len(cls.valid_args)}个有效论域组合")
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if cls.num_args == 0:
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for num in range(multiple_num):
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info = cls.get_info()
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action_list.append(Action(name=cls.get_ins_name() + str(num), **info))
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if cls.num_args == 1:
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for num in range(multiple_num):
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for arg in cls.valid_args:
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info = cls.get_info(arg)
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action_list.append(Action(name=cls.get_ins_name(arg) + str(num), **info))
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if cls.num_args > 1:
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for num in range(multiple_num):
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for args in cls.valid_args:
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info = cls.get_info(*args)
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action_list.append(Action(name=cls.get_ins_name(*args) + str(num),**info))
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action_list = sorted(action_list, key=lambda x: x.name)
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for i in range(len(action_list)):
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cost = random.randint(1, 100)
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action_list[i].cost=cost
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return action_list
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def collect_action_nodes_old(random):
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action_list = []
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behavior_dict = load_behavior_tree_lib()
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behavior_ls = list()
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# behavior_ls.sort()
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behavior_ls = [cls for cls in behavior_ls]
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behavior_ls = sorted(behavior_ls, key=lambda x: x.__class__.__name__)
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for cls in behavior_ls:
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if cls.can_be_expanded:
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print(f"可扩展动作：{cls.__name__}, 存在{len(cls.valid_args)}个有效论域组合")
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if cls.num_args == 0:
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for num in range(2):
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cost = random.randint(1, 100)
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info = cls.get_info()
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info.pop('cost', None)
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action_list.append(Action(name=cls.get_ins_name()+str(num),cost=cost, **info))
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if cls.num_args == 1:
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for num in range(2):
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for arg in cls.valid_args:
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cost = random.randint(1, 100)
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info = cls.get_info(arg)
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info.pop('cost', None)
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action_list.append(Action(name=cls.get_ins_name(arg)+str(num),cost=cost, **info))
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if cls.num_args > 1:
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for num in range(2):
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for args in cls.valid_args:
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cost = random.randint(1, 100)
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info = cls.get_info(*args)
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info.pop('cost', None)
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action_list.append(Action(name=cls.get_ins_name(*args)+str(num),cost=cost, **info))
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return action_list
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def get_start():
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start_robowaiter = {'At(Robot,Bar)', 'Is(AC,Off)',
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'Exist(Yogurt)', 'Exist(BottledDrink)', 'Exist(Softdrink)', 'Exist(ADMilk)',
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'On(Yogurt,Bar)','On(BottledDrink,Bar)','On(ADMilk,Bar)','On(Chips,Bar)',
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'Exist(Milk)', 'On(Softdrink,Table1)', 'On(Softdrink,Table3)',
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'Exist(Chips)', 'Exist(NFCJuice)', 'Exist(Bernachon)', 'Exist(ADMilk)', 'Exist(SpringWater)', 'Exist(MilkDrink)',
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'Exist(ADMilk)','On(ADMilk,Bar)','On(Bernachon,Bar)','On(SpringWater,Bar2)','On(MilkDrink,Bar)',
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'Holding(Nothing)',
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'Exist(VacuumCup)', 'On(VacuumCup,Table2)',
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'Is(HallLight,Off)', 'Is(TubeLight,On)', 'Is(Curtain,On)',
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'Is(Table1,Dirty)', 'Is(Floor,Dirty)', 'Is(Chairs,Dirty)'}
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return start_robowaiter
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def print_action_data_table(goal,start,actions):
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data = []
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for a in actions:
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data.append([a.name ,a.pre ,a.add ,a.del_set ,a.cost])
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data.append(["Goal" ,goal ," " ,"Start" ,start])
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print(tabulate(data, headers=["Name", "Pre", "Add" ,"Del" ,"Cost"], tablefmt="fancy_grid")) # grid plain simple github fancy_grid
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def state_transition(state,action):
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if not action.pre <= state:
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print ('error: action not applicable')
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return state
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new_state=(state | action.add) - action.del_set
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return new_state
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def BTTest(bt_algo_opt,goal_states,action_list,start_robowaiter):
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if bt_algo_opt:
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print("============= OptBT Test ==============")
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else:
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print("============= XiaoCai BT Test ==============")
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total_tree_size = []
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total_action_num = []
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total_state_num = []
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total_steps_num = []
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total_cost = []
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total_tick = []
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success_count = 0
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failure_count = 0
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planning_time_total = 0.0
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states=[] ####？？？
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actions = copy.deepcopy(action_list)
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start = copy.deepcopy(start_robowaiter)
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error=False
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for count, goal_str in enumerate(goal_states):
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goal = copy.deepcopy(goal_transfer_str(goal_str))
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print("count:", count, "goal:", goal)
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if bt_algo_opt:
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# if count==874:
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# algo = OptBTExpAlgorithm(verbose=False)
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# else:
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algo = OptBTExpAlgorithm(verbose=False)
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else:
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algo = BTExpAlgorithm(verbose=False)
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algo.clear()
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# algo = Weakalgorithm()
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start_time = time.time()
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# if count == 11 : #874:
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# print_action_data_table(goal, start, list(actions))
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# print_action_data_table(goal, start, list(actions))
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if algo.run_algorithm(start, goal, actions): # 运行算法，规划后行为树为algo.bt
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total_tree_size.append(algo.bt.count_size() - 1)
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# if count==10:
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# algo.print_solution()
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# algo.print_solution() # 打印行为树
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# 画出行为树
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# if count == 11:
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# ptml_string = algo.get_ptml_many_act()
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# file_name = "sub_task"
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# file_path = f'./{file_name}.ptml'
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# with open(file_path, 'w') as file:
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# file.write(ptml_string)
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# ptml_path = os.path.join(root_path, 'BTExpansionCode/EXP/sub_task.ptml')
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# behavior_lib_path = os.path.join(root_path, 'BTExpansionCode/EXP/behavior_lib')
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# bt = load_bt_from_ptml(None, ptml_path, behavior_lib_path)
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# if bt_algo_opt:
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# render_dot_tree(bt.root, target_directory="", name="expanded_bt_obt", png_only=False)
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# else:
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# render_dot_tree(bt.root, target_directory="", name="expanded_bt_xiaocai", png_only=False)
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else:
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print("error")
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end_time = time.time()
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planning_time_total += (end_time - start_time)
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# 开始从初始状态运行行为树，测试
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state = start
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steps = 0
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current_cost = 0
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current_tick_time = 0
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val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0) # tick行为树，obj为所运行的行动
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current_tick_time += tick_time
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current_cost += cost
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while val != 'success' and val != 'failure': # 运行直到行为树成功或失败
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state = state_transition(state, obj)
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val, obj, cost, tick_time = algo.bt.cost_tick(state, 0, 0)
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current_cost += cost
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current_tick_time += tick_time
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if (val == 'failure'):
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print("bt fails at step", steps)
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error = True
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break
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steps += 1
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if (steps >= 500): # 至多运行500步
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break
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if not goal[0] <= state: # 错误解，目标条件不在执行后状态满足
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# print ("wrong solution",steps)
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failure_count += 1
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error = True
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else: # 正确解，满足目标条件
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# print ("right solution",steps)
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success_count += 1
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total_steps_num.append(steps)
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if error:
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print_action_data_table(goal, start, list(actions))
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algo.print_solution()
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break
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algo.clear()
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total_action_num.append(len(actions))
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total_state_num.append(len(states))
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total_cost.append(current_cost)
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total_tick.append(current_tick_time)
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print("success:", success_count, "failure:", failure_count) # 算法成功和失败次数
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print("Total Tree Size: mean=", np.mean(total_tree_size), "std=", np.std(total_tree_size, ddof=1)) # 1000次测试树大小
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print("Total Steps Num: mean=", np.mean(total_steps_num), "std=", np.std(total_steps_num, ddof=1))
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print("Average Number of States:", np.mean(total_state_num)) # 1000次问题的平均状态数
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print("Average Number of Actions", np.mean(total_action_num)) # 1000次问题的平均行动数
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print("Planning Time Total:", planning_time_total, planning_time_total / 1000.0)
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print("Average Number of Ticks", np.mean(total_tick), "std=", np.std(total_tick, ddof=1))
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print("Average Cost of Execution:", np.mean(total_cost), "std=", np.std(total_cost, ddof=1))
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