430 lines
18 KiB
Python
430 lines
18 KiB
Python
import copy
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import random
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from robowaiter.behavior_tree.obtea.BehaviorTree import Leaf,ControlBT
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class CondActPair:
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def __init__(self, cond_leaf,act_leaf):
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self.cond_leaf = cond_leaf
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self.act_leaf = act_leaf
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#定义行动类，行动包括前提、增加和删除影响
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class Action:
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def __init__(self,name='anonymous action',pre=set(),add=set(),del_set=set(),cost=1):
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self.pre=copy.deepcopy(pre)
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self.add=copy.deepcopy(add)
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self.del_set=copy.deepcopy(del_set)
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self.name=name
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self.cost=cost
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def __str__(self):
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return self.name
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# 从状态随机生成一个行动
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def generate_from_state(self,state,num):
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for i in range(0,num):
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if i in state:
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if random.random() >0.5:
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self.pre.add(i)
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if random.random() >0.5:
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self.del_set.add(i)
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continue
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if random.random() > 0.5:
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self.add.add(i)
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continue
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if random.random() >0.5:
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self.del_set.add(i)
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def print_action(self):
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print (self.pre)
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print(self.add)
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print(self.del_set)
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#生成随机状态
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def generate_random_state(num):
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result = set()
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for i in range(0,num):
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if random.random()>0.5:
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result.add(i)
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return result
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#从状态和行动生成后继状态
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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 conflict(c):
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have_at = False
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have_holding = False
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for str in c:
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if 'At' in str:
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if not have_at:
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have_at = True
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else:
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return True
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if 'Holding' in str:
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if not have_holding:
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have_holding = True
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else:
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return True
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return False
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#本文所提出的完备规划算法
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class OptBTExpAlgorithm:
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def __init__(self,verbose=False):
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self.bt = None
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self.nodes=[]
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self.traversed=[]
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self.mounted=[]
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self.conditions=[]
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self.conditions_index=[]
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self.verbose=verbose
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self.goal=None
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self.bt_merge = True
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def clear(self):
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self.bt = None
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self.goal = None
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self.nodes = []
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self.traversed = [] #存cond
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self.expanded = [] #存整个
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self.conditions = []
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self.conditions_index = []
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#运行规划算法，从初始状态、目标状态和可用行动，计算行为树self.bt
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# def run_algorithm(self,goal,actions,scene):
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def run_algorithm_selTree(self, start, goal, actions):
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# self.scene = scene
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self.goal = goal
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if self.verbose:
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print("\n算法开始！")
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bt = ControlBT(type='cond')
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# 初始行为树只包含目标条件
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gc_node = Leaf(type='cond', content=goal,mincost=0) # 为了统一，都成对出现
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ga_node = Leaf(type='act', content=None, mincost=0)
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subtree = ControlBT(type='?')
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subtree.add_child([copy.deepcopy(gc_node)]) # 子树首先保留所扩展结
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bt.add_child([subtree])
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# self.conditions.append(goal)
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cond_anc_pair = CondActPair(cond_leaf=gc_node,act_leaf=ga_node)
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self.nodes.append(copy.deepcopy(cond_anc_pair)) # the set of explored but unexpanded conditions
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self.traversed = [goal] # the set of expanded conditions
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while len(self.nodes)!=0:
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# Find the condition for the shortest cost path
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pair_node = None
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min_cost = float ('inf')
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index= -1
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for i,cond_anc_pair in enumerate(self.nodes):
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########### 剪枝操作
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# cond_tmp = cond_anc_pair.cond_leaf.content
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# valid = True
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# for pn in self.expanded: # 剪枝操作
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# if isinstance(pn.act_leaf.content,Action):
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# if pn.act_leaf.content.name==cond_anc_pair.act_leaf.content.name and cond_tmp <= pn.cond_leaf.content:
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# valid = False
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# break
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# if not valid:
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# continue
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########### 剪枝操作
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if cond_anc_pair.cond_leaf.mincost < min_cost:
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min_cost = cond_anc_pair.cond_leaf.mincost
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pair_node = copy.deepcopy(cond_anc_pair)
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index = i
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if self.verbose:
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print("选择扩展条件结点：",pair_node.cond_leaf.content)
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# Update self.nodes and self.traversed
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self.nodes.pop(index) # the set of explored but unexpanded conditions. self.nodes.remove(pair_node)
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c = pair_node.cond_leaf.content # 子树所扩展结点对应的条件（一个文字的set）
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# Mount the action node and extend BT. T = Eapand(T,c,A(c))
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if c!=goal:
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if c!=set():
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# 挂在上去的时候判断要不要挂载
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########### 剪枝操作 发现行不通
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# valid = True
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# for pn in self.expanded: # 剪枝操作
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# if isinstance(pn.act_leaf.content,Action):
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# if pn.act_leaf.content.name==pair_node.act_leaf.content.name and c <= pn.cond_leaf.content:
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# valid = False
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# break
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# if valid:
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########### 剪枝操作
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sequence_structure = ControlBT(type='>')
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sequence_structure.add_child(
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[copy.deepcopy(pair_node.cond_leaf), copy.deepcopy(pair_node.act_leaf)])
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subtree.add_child([copy.deepcopy(sequence_structure)]) # subtree 是回不断变化的，它的父亲是self.bt
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self.expanded.append(copy.deepcopy(pair_node))
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# 增加实时条件判断，满足条件就不再扩展
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# if c <= self.scene.state["condition_set"]:
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if c <= start:
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if self.bt_merge:
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bt = copy.deepcopy(self.merge_adjacent_conditions_stack(bt))
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return bt,min_cost
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# return True
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else:
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subtree.add_child([copy.deepcopy(pair_node.act_leaf)])
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if self.verbose:
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print("完成扩展 a_node= %s,对应的新条件 c_attr= %s,mincost=%d" \
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% (pair_node.act_leaf.content.name, pair_node.cond_leaf.content,
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pair_node.cond_leaf.mincost))
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if self.verbose:
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print("遍历所有动作, 寻找符合条件的动作")
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# 遍历所有动作, 寻找符合条件的动作
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current_mincost = pair_node.cond_leaf.mincost # 当前的最短路径是多少
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for i in range(0, len(actions)):
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if not c & ((actions[i].pre | actions[i].add) - actions[i].del_set) <= set():
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if (c - actions[i].del_set) == c:
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if self.verbose:
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print("———— 满足条件可以扩展:",actions[i].name)
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c_attr = (actions[i].pre | c) - actions[i].add
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# 这样剪枝存在错误性
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if conflict(c_attr):
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continue
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# 剪枝操作,现在的条件是以前扩展过的条件的超集
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valid = True
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for j in self.traversed: # 剪枝操作
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if j <= c_attr:
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valid = False
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if self.verbose:
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print("———— --被剪枝:",actions[i].name,"c_attr=",c_attr)
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break
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if valid:
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c_attr_node = Leaf(type='cond', content=c_attr, mincost=current_mincost + actions[i].cost)
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a_attr_node = Leaf(type='act', content=actions[i], mincost=current_mincost + actions[i].cost)
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cond_anc_pair = CondActPair(cond_leaf=c_attr_node, act_leaf=a_attr_node)
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self.nodes.append(copy.deepcopy(cond_anc_pair)) # condition node list
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self.traversed.append(c_attr) # 重点 the set of expanded conditions
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# 把符合条件的动作节点都放到列表里
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if self.verbose:
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print("———— -- %s 符合条件放入列表,对应的c为 %s" % (actions[i].name,c_attr),"cost=",current_mincost + actions[i].cost)
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if self.bt_merge:
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bt = copy.deepcopy(self.merge_adjacent_conditions_stack(bt))
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if self.verbose:
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print("算法结束！\n")
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return bt,min_cost
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# return True
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def run_algorithm(self, start, goal, actions):
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self.bt = ControlBT(type='cond')
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subtree = ControlBT(type='?')
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subtree_with_costs_ls=[]
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if len(goal) > 1:
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for g in goal:
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bt_sel_tree,mincost = self.run_algorithm_selTree(start, g, actions)
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subtree_with_costs_ls.append((bt_sel_tree,mincost))
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# 要排个序再一次add
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# subtree.add_child([copy.deepcopy(bt_sel_tree.children[0])])
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# self.bt.add_child([subtree])
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sorted_trees = sorted(subtree_with_costs_ls, key=lambda x: x[1])
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for tree,cost in sorted_trees:
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subtree.add_child([copy.deepcopy(tree.children[0])])
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self.bt.add_child([subtree])
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else:
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self.bt,mincost = self.run_algorithm_selTree(start, goal[0], actions)
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return True
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def merge_adjacent_conditions_stack(self,bt_sel):
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# 只针对第一层合并，之后要考虑层层递归合并
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bt = ControlBT(type='cond')
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sbtree = ControlBT(type='?')
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# gc_node = Leaf(type='cond', content=self.goal, mincost=0) # 为了统一，都成对出现
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# sbtree.add_child([copy.deepcopy(gc_node)]) # 子树首先保留所扩展结
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bt.add_child([sbtree])
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parnode = copy.deepcopy(bt_sel.children[0])
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stack=[]
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for child in parnode.children:
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if isinstance(child, ControlBT) and child.type == '>':
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if stack==[]:
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stack.append(child)
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continue
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# 检查合并的条件，前面一个的条件包含了后面的条件，把包含部分提取出来
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last_child = stack[-1]
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if isinstance(last_child, ControlBT) and last_child.type == '>':
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set1 = last_child.children[0].content
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set2 = child.children[0].content
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inter = set1 & set2
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if inter!=set():
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c1 = set1-set2
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c2 = set2-set1
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inter_node = Leaf(type='cond', content=inter)
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c1_node = Leaf(type='cond', content=c1)
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c2_node = Leaf(type='cond', content=c2)
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a1_node = copy.deepcopy(last_child.children[1])
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a2_node = copy.deepcopy(child.children[1])
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# set1<=set2,此时set2对应的动作永远不会执行
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if (c1==set() and isinstance(last_child.children[1], Leaf) and isinstance(child.children[1], Leaf) \
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and isinstance(last_child.children[1].content, Action) and isinstance(child.children[1].content, Action)):
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continue
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# 再写一个特殊情况处理，三个结点动作last 遇到 两个结点 且动作相同
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if len(last_child.children)==3 and \
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isinstance(last_child.children[2], Leaf) and isinstance(child.children[1], Leaf) \
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and isinstance(last_child.children[2].content, Action) and isinstance( child.children[1].content, Action) \
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and last_child.children[2].content.name == child.children[1].content.name \
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and c1==set() and c2!=set():
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last_child.children[1].add_child([copy.deepcopy(c2_node)])
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continue
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elif len(last_child.children)==3:
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stack.append(child)
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continue
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# 判断动作相不相同
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if isinstance(last_child.children[1], Leaf) and isinstance(child.children[1], Leaf) \
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and isinstance(last_child.children[1].content, Action) and isinstance(child.children[1].content, Action) \
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and last_child.children[1].content.name == child.children[1].content.name:
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if c2==set():
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tmp_tree = ControlBT(type='>')
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tmp_tree.add_child(
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[copy.deepcopy(inter_node), copy.deepcopy(a1_node)])
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else:
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_sel = ControlBT(type='?')
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_sel.add_child([copy.deepcopy(c1_node), copy.deepcopy(c2_node)])
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tmp_tree = ControlBT(type='>')
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tmp_tree.add_child(
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[copy.deepcopy(inter_node), copy.deepcopy(_sel),copy.deepcopy(a1_node)])
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else:
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if c1 == set():
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seq1 = copy.deepcopy(last_child.children[1])
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else:
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seq1 = ControlBT(type='>')
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seq1.add_child([copy.deepcopy(c1_node), copy.deepcopy(a1_node)])
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if c2 == set():
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seq2 = copy.deepcopy(child.children[1])
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else:
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seq2 = ControlBT(type='>')
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seq2.add_child([copy.deepcopy(c2_node), copy.deepcopy(a2_node)])
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sel = ControlBT(type='?')
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sel.add_child([copy.deepcopy(seq1), copy.deepcopy(seq2)])
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tmp_tree = ControlBT(type='>')
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tmp_tree.add_child(
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[copy.deepcopy(inter_node), copy.deepcopy(sel)])
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stack.pop()
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stack.append(tmp_tree)
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else:
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stack.append(child)
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else:
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stack.append(child)
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else:
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stack.append(child)
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for tree in stack:
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sbtree.add_child([tree])
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bt_sel = copy.deepcopy(bt)
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return bt_sel
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def print_solution(self):
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print("========= BT ==========") # 树的bfs遍历
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nodes_ls = []
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nodes_ls.append(self.bt)
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while len(nodes_ls) != 0:
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parnode = nodes_ls[0]
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print("Parrent:", parnode.type)
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for child in parnode.children:
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if isinstance(child, Leaf):
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print("---- Leaf:", child.content)
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elif isinstance(child, ControlBT):
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print("---- ControlBT:", child.type)
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nodes_ls.append(child)
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print()
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nodes_ls.pop(0)
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print("========= BT ==========\n")
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# 返回所有能到达目标状态的初始状态
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def get_all_state_leafs(self):
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state_leafs=[]
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nodes_ls = []
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nodes_ls.append(self.bt)
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while len(nodes_ls) != 0:
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parnode = nodes_ls[0]
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for child in parnode.children:
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if isinstance(child, Leaf):
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if child.type == "cond":
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state_leafs.append(child.content)
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elif isinstance(child, ControlBT):
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nodes_ls.append(child)
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nodes_ls.pop(0)
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return state_leafs
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# 树的dfs
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def dfs_ptml(self,parnode,is_root=False):
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for child in parnode.children:
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if isinstance(child, Leaf):
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if child.type == 'cond':
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if is_root and len(child.content) > 1:
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# 把多个 cond 串起来
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self.ptml_string += "sequence{\n"
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self.ptml_string += "cond "
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c_set_str = '\n cond '.join(map(str, child.content)) + "\n"
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self.ptml_string += c_set_str
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self.ptml_string += '}\n'
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else:
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self.ptml_string += "cond "
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c_set_str = '\n cond '.join(map(str, child.content)) + "\n"
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self.ptml_string += c_set_str
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elif child.type == 'act':
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if '(' not in child.content.name:
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self.ptml_string += 'act ' + child.content.name + "()\n"
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else:
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self.ptml_string += 'act ' + child.content.name + "\n"
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elif isinstance(child, ControlBT):
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if child.type == '?':
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self.ptml_string += "selector{\n"
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self.dfs_ptml(parnode=child)
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elif child.type == '>':
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self.ptml_string += "sequence{\n"
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self.dfs_ptml( parnode=child)
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self.ptml_string += '}\n'
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def get_ptml(self):
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self.ptml_string = "selector{\n"
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self.dfs_ptml(self.bt.children[0],is_root=True)
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self.ptml_string += '}\n'
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return self.ptml_string
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def save_ptml_file(self,file_name):
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self.ptml_string = "selector{\n"
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self.dfs_ptml(self.bt.children[0])
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self.ptml_string += '}\n'
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with open(f'./{file_name}.ptml', 'w') as file:
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file.write(self.ptml_string)
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return self.ptml_string
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