From effa653e30d8c1e5f060d6d7bfb797da51b083be Mon Sep 17 00:00:00 2001 From: Caiyishuai <39987654+Caiyishuai@users.noreply.github.com> Date: Mon, 6 Nov 2023 17:31:43 +0800 Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90=E4=BA=86=E5=AE=8C=E5=85=A8?= =?UTF-8?q?=E6=9C=80=E4=BC=98=E8=A1=8C=E4=B8=BA=E6=A0=91=E6=89=A9=E5=B1=95?= =?UTF-8?q?=E7=AE=97=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 初始化传入 actions，运行时传入 goal，返回完全最优行为树的 ptml 表示 --- opt_bt_expansion/BehaviorTree.py | 95 ++++++++ opt_bt_expansion/MakeCoffee.ptml | 59 +++++ .../OptimalBTExpansionAlgorithm.py | 210 ++++++++++++++++++ .../README.assets/image-20231103191141047.png | Bin 0 -> 55821 bytes opt_bt_expansion/README.md | 121 ++++++++++ opt_bt_expansion/examples.py | 174 +++++++++++++++ opt_bt_expansion/opt_bt_exp_main.py | 121 ++++++++++ opt_bt_expansion/tools.py | 167 ++++++++++++++ 8 files changed, 947 insertions(+) create mode 100644 opt_bt_expansion/BehaviorTree.py create mode 100644 opt_bt_expansion/MakeCoffee.ptml create mode 100644 opt_bt_expansion/OptimalBTExpansionAlgorithm.py create mode 100644 opt_bt_expansion/README.assets/image-20231103191141047.png create mode 100644 opt_bt_expansion/README.md create mode 100644 opt_bt_expansion/examples.py create mode 100644 opt_bt_expansion/opt_bt_exp_main.py create mode 100644 opt_bt_expansion/tools.py diff --git a/opt_bt_expansion/BehaviorTree.py b/opt_bt_expansion/BehaviorTree.py new file mode 100644 index 0000000..4513a20 --- /dev/null +++ b/opt_bt_expansion/BehaviorTree.py @@ -0,0 +1,95 @@ + +#叶结点 +class Leaf: + def __init__(self,type,content,mincost): + self.type=type + self.content=content #conditionset or action + self.parent=None + self.parent_index=0 + self.mincost=mincost + + # tick 叶节点，返回返回值以及对应的条件或行动对象self.content + def tick(self,state): + if self.type=='cond': + if self.content <= state: + return 'success',self.content + else: + return 'failure',self.content + if self.type=='act': + if self.content.pre<=state: + return 'running',self.content #action + else: + return 'failure',self.content + + def __str__(self): + print( self.content) + return '' + + def print_nodes(self): + print(self.content) + + def count_size(self): + return 1 + + +#可能包含控制结点的行为树 +class ControlBT: + def __init__(self,type): + self.type=type + self.children=[] + self.parent=None + self.parent_index=0 + + + def add_child(self,subtree_list): + for subtree in subtree_list: + self.children.append(subtree) + subtree.parent=self + subtree.parent_index=len(self.children)-1 + + # tick行为树，根据不同控制结点逻辑tick子结点 + def tick(self,state): + if len(self.children) < 1: + print("error,no child") + if self.type =='?':#选择结点，即或结点 + for child in self.children: + val,obj=child.tick(state) + if val=='success': + return val,obj + if val=='running': + return val,obj + return 'failure','?fails' + if self.type =='>':#顺序结点，即与结点 + for child in self.children: + val,obj=child.tick(state) + if val=='failure': + return val,obj + if val=='running': + return val,obj + return 'success', '>success' + if self.type =='act':#行动结点 + return self.children[0].tick(state) + if self.type =='cond':#条件结点 + return self.children[0].tick(state) + + def getFirstChild(self): + return self.children[0] + + def __str__(self): + print(self.type+'\n') + for child in self.children: + print (child) + return '' + + def print_nodes(self): + print(self.type) + for child in self.children: + child.print_nodes() + + # 递归统计树中结点数 + def count_size(self): + result=1 + for child in self.children: + result+= child.count_size() + return result + diff --git a/opt_bt_expansion/MakeCoffee.ptml b/opt_bt_expansion/MakeCoffee.ptml new file mode 100644 index 0000000..40fc6a4 --- /dev/null +++ b/opt_bt_expansion/MakeCoffee.ptml @@ -0,0 +1,59 @@ +selector{ +cond At(Table,Coffee) +selector{ +cond At(Robot,Table), Holding(Coffee) +act PutDown(Table,Coffee) +} +selector{ +cond At(Robot,Coffee), NotHolding, At(Robot,Table) +act PickUp(Coffee) +} +selector{ +cond Available(Table), Holding(Coffee) +act MoveTo(Table) +} +selector{ +cond At(Robot,Coffee), At(Robot,Table), Holding(VacuumCup) +act PutDown(Table,VacuumCup) +} +selector{ +cond At(Robot,CoffeeMachine), NotHolding, At(Robot,Table) +act OpCoffeeMachine +} +selector{ +cond At(Robot,Coffee), Available(Table), NotHolding +act PickUp(Coffee) +} +selector{ +cond At(Robot,CoffeeMachine), At(Robot,Table), Holding(VacuumCup) +act PutDown(Table,VacuumCup) +} +selector{ +cond Available(Table), Available(Coffee), NotHolding +act MoveTo(Coffee) +} +selector{ +cond Available(Table), At(Robot,CoffeeMachine), NotHolding +act OpCoffeeMachine +} +selector{ +cond Available(Table), Available(Coffee), At(Robot,Table), Holding(VacuumCup) +act PutDown(Table,VacuumCup) +} +selector{ +cond Available(Table), NotHolding, Available(CoffeeMachine) +act MoveTo(CoffeeMachine) +} +selector{ +cond Available(Table), Available(Coffee), Holding(VacuumCup) +act MoveTo(Table) +} +selector{ +cond Available(Table), Available(CoffeeMachine), At(Robot,Table), Holding(VacuumCup) +act PutDown(Table,VacuumCup) +} +selector{ +cond Available(Table), Available(CoffeeMachine), Holding(VacuumCup) +act MoveTo(Table) +} +} diff --git a/opt_bt_expansion/OptimalBTExpansionAlgorithm.py b/opt_bt_expansion/OptimalBTExpansionAlgorithm.py new file mode 100644 index 0000000..00528cf --- /dev/null +++ b/opt_bt_expansion/OptimalBTExpansionAlgorithm.py @@ -0,0 +1,210 @@ +import copy +import random +from opt_bt_expansion.BehaviorTree import Leaf,ControlBT + +class CondActPair: + def __init__(self, cond_leaf,act_leaf): + self.cond_leaf = cond_leaf + self.act_leaf = act_leaf + +#定义行动类，行动包括前提、增加和删除影响 +class Action: + def __init__(self,name='anonymous action',pre=set(),add=set(),del_set=set(),cost=1): + self.pre=copy.deepcopy(pre) + self.add=copy.deepcopy(add) + self.del_set=copy.deepcopy(del_set) + self.name=name + self.cost=cost + + def __str__(self): + return self.name + +#生成随机状态 +def generate_random_state(num): + result = set() + for i in range(0,num): + if random.random()>0.5: + result.add(i) + return result +#从状态和行动生成后继状态 +def state_transition(state,action): + if not action.pre <= state: + print ('error: action not applicable') + return state + new_state=(state | action.add) - action.del_set + return new_state + + +#本文所提出的完备规划算法 +class OptBTExpAlgorithm: + def __init__(self,verbose=False): + self.bt = None + self.nodes=[] + self.traversed=[] + self.mounted=[] + self.conditions=[] + self.conditions_index=[] + self.verbose=verbose + + def clear(self): + self.bt = None + self.nodes = [] + self.traversed = [] + self.conditions = [] + self.conditions_index = [] + + #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 + def run_algorithm(self,goal,actions): + if self.verbose: + print("\n算法开始！") + + + self.bt = ControlBT(type='cond') + # 初始行为树只包含目标条件 + gc_node = Leaf(type='cond', content=goal,mincost=0) # 为了统一，都成对出现 + ga_node = Leaf(type='act', content=None, mincost=0) + subtree = ControlBT(type='?') + subtree.add_child([copy.deepcopy(gc_node)]) # 子树首先保留所扩展结 + self.bt.add_child([subtree]) + + # self.conditions.append(goal) + cond_anc_pair = CondActPair(cond_leaf=gc_node,act_leaf=ga_node) + self.nodes.append(copy.deepcopy(cond_anc_pair)) # the set of explored but unexpanded conditions + self.traversed = [goal] # the set of expanded conditions + + + while len(self.nodes)!=0: + + # Find the condition for the shortest cost path + pair_node = None + min_cost = float ('inf') + index= -1 + for i,cond_anc_pair in enumerate(self.nodes): + if cond_anc_pair.cond_leaf.mincost < min_cost: + min_cost = cond_anc_pair.cond_leaf.mincost + pair_node = copy.deepcopy(cond_anc_pair) + index = i + break + + if self.verbose: + print("选择扩展条件结点：",pair_node.cond_leaf.content) + # Update self.nodes and self.traversed + self.nodes.pop(index) # the set of explored but unexpanded conditions. self.nodes.remove(pair_node) + c = pair_node.cond_leaf.content # 子树所扩展结点对应的条件（一个文字的set） + + # Mount the action node and extend BT. T = Eapand(T,c,A(c)) + if c!=goal: + sequence_structure = ControlBT(type='>') + sequence_structure.add_child( + [copy.deepcopy(pair_node.cond_leaf), copy.deepcopy(pair_node.act_leaf)]) + subtree.add_child([copy.deepcopy(sequence_structure)]) # subtree 是回不断变化的，它的父亲是self.bt + if self.verbose: + print("完成扩展 a_node= %s,对应的新条件 c_attr= %s,mincost=%d" \ + % (cond_anc_pair.act_leaf.content.name, cond_anc_pair.cond_leaf.content, + cond_anc_pair.cond_leaf.mincost)) + + if self.verbose: + print("遍历所有动作, 寻找符合条件的动作") + # 遍历所有动作, 寻找符合条件的动作 + current_mincost = pair_node.cond_leaf.mincost # 当前的最短路径是多少 + + for i in range(0, len(actions)): + if not c & ((actions[i].pre | actions[i].add) - actions[i].del_set) <= set(): + if (c - actions[i].del_set) == c: + if self.verbose: + print("———— 满足条件可以扩展") + c_attr = (actions[i].pre | c) - actions[i].add + + # 剪枝操作,现在的条件是以前扩展过的条件的超集 + valid = True + for j in self.traversed: # 剪枝操作 + if j <= c_attr: + valid = False + if self.verbose: + print("———— --被剪枝") + break + + if valid: + # 把符合条件的动作节点都放到列表里 + if self.verbose: + print("———— -- %s 符合条件放入列表" % actions[i].name) + c_attr_node = Leaf(type='cond', content=c_attr, mincost=current_mincost + actions[i].cost) + a_attr_node = Leaf(type='act', content=actions[i], mincost=current_mincost + actions[i].cost) + cond_anc_pair = CondActPair(cond_leaf=c_attr_node, act_leaf=a_attr_node) + self.nodes.append(copy.deepcopy(cond_anc_pair)) # condition node list + self.traversed.append(c_attr) # 重点 the set of expanded conditions + + if self.verbose: + print("算法结束！\n") + return True + + def print_solution(self): + print("========= BT ==========") # 树的bfs遍历 + nodes_ls = [] + nodes_ls.append(self.bt) + while len(nodes_ls) != 0: + parnode = nodes_ls[0] + print("Parrent:", parnode.type) + for child in parnode.children: + if isinstance(child, Leaf): + print("---- Leaf:", child.content) + elif isinstance(child, ControlBT): + print("---- ControlBT:", child.type) + nodes_ls.append(child) + print() + nodes_ls.pop(0) + print("========= BT ==========\n") + + # 返回所有能到达目标状态的初始状态 + def get_all_state_leafs(self): + state_leafs=[] + + nodes_ls = [] + nodes_ls.append(self.bt) + while len(nodes_ls) != 0: + parnode = nodes_ls[0] + for child in parnode.children: + if isinstance(child, Leaf): + if child.type == "cond": + state_leafs.append(child.content) + elif isinstance(child, ControlBT): + nodes_ls.append(child) + nodes_ls.pop(0) + + return state_leafs + + + # 树的dfs + def dfs_ptml(self,parnode): + for child in parnode.children: + if isinstance(child, Leaf): + if child.type == 'cond': + self.ptml_string += "cond " + c_set_str = ', '.join(map(str, child.content)) + "\n" + self.ptml_string += c_set_str + elif child.type == 'act': + self.ptml_string += 'act '+child.content.name+"\n" + elif isinstance(child, ControlBT): + if parnode.type == '?': + self.ptml_string += "selector{\n" + self.dfs_ptml(parnode=child) + elif parnode.type == '>': + self.ptml_string += "sequence{\n" + self.dfs_ptml( parnode=child) + self.ptml_string += '}\n' + + + def get_ptml(self): + self.ptml_string = "selector{\n" + self.dfs_ptml(self.bt.children[0]) + self.ptml_string += '}\n' + return self.ptml_string + + + def save_ptml_file(self,file_name): + self.ptml_string = "selector{\n" + self.dfs_ptml(self.bt.children[0]) + self.ptml_string += '}\n' + with open(f'./{file_name}.ptml', 'w') as file: + file.write(self.ptml_string) + return self.ptml_string diff --git a/opt_bt_expansion/README.assets/image-20231103191141047.png b/opt_bt_expansion/README.assets/image-20231103191141047.png new file mode 100644 index 0000000000000000000000000000000000000000..6453647b76058878bfa6767963b630e8410b921c GIT binary patch literal 55821 zcmb@tRd5`^)~#7$Sr)S_w8bo0%#tl;W(Lb*W@ct)w3wNhnVFdxEt>W@_n$ivHzr~p z=BYcftFy8yt8(x4?Uf<2(jo|OU*G@$KoAoZlm`HCKL7wLf&~Y))I>Q~0{{^qCMclj zoPO5sC5vX#^7OKoSV*?elJJE8IJ3;eg{`dDgTg#F6g|hU#ffW*fMy0Cc9;y$HTY>v zll*8Q`KUbRsJwqbfx5dqp>LU(fk}kS28qVzbD*=oMAJ@lNRMJJyUw>0RMqvkgGjg8 zc} 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ZoE*=jX`）、动作节点（`act`）或条件节点（`cond`）。 +- 上述两个类都包含 `tick` 方法。 + +### 2. `OptimalBTExpansionAlgorithm.py` 实现最优行为树扩展算法 + +![image-20231103191141047](README.assets/image-20231103191141047.png) + +定义行动类 +```python +#定义行动类，行动包括前提、增加和删除影响 +class Action: + def __init__(self,name='anonymous action',pre=set(),add=set(),del_set=set(),cost=1): + self.pre=copy.deepcopy(pre) + self.add=copy.deepcopy(add) + self.del_set=copy.deepcopy(del_set) + self.name=name + self.cost=cost + + def __str__(self): + return self.name +``` + +调用算法 +```python +algo = OptBTExpAlgorithm(verbose=True) +algo.clear() +algo.run_algorithm(start, goal, actions) # 使用算法得到行为树在 algo.bt +algo.print_solution() # 打印行为树 +val, obj = algo.bt.tick(state) # 执行行为树 +algo.save_ptml_file("bt.ptml") # 保存行为树为 ptml 文件 +``` + +### 3. **`tools.py`** 实现打印数据、行为树测试等模块 + +使用方法 + +```python +print_action_data_table(goal,start,actions) # 打印所有变量 + +# 行为树鲁棒性测试，随机生成规划问题 +# 设置生成规划问题集的超参数：文字数、解深度、迭代次数 +seed=1 +literals_num=10 +depth = 10 +iters= 10 +BTTest(seed=seed,literals_num=literals_num,depth=depth,iters=iters) +``` + +### 4. `example.py` 中设计规划案例 goals, start,actions + +```python +def MoveBtoB (): + actions=[] + a = Action(name="Move(b,ab)") + a.pre={'Free(ab)','WayClear'} + a.add={'At(b,ab)'} + a.del_set= {'Free(ab)','At(b,pb)'} + a.cost = 1 + actions.append(a) + + a=Action(name="Move(s,ab)") + a.pre={'Free(ab)'} + a.add={'Free(ab)','WayClear'} + a.del_set={'Free(ab)','At(s,ps)'} + a.cost = 1 + actions.append(a) + + a=Action(name="Move(s,as)") + a.pre={'Free(as)'} + a.add={'At(s,ps)','WayClear'} + a.del_set={'Free(as)','At(s,ps)'} + a.cost = 1 + actions.append(a) + + start = {'Free(ab)','Free(as)','At(b,pb)','At(s,ps)'} + goal= {'At(b,ab)'} + return goal,start,actions +``` + +### 5. `opt_bt_exp_main.py` 为主函数，在此演示如何调用最优行为树扩展算法得到完全扩展最优行为树 +```python +actions=[ + Action(name='PutDown(Table,Coffee)', pre={'Holding(Coffee)','At(Robot,Table)'}, add={'At(Table,Coffee)','NotHolding'}, del_set={'Holding(Coffee)'}, cost=1) + ………… +] +algo = BTOptExpInterface(actions) + +goal = {'At(Table,Coffee)'} +ptml_string = algo.process(goal,start) +print(ptml_string) + +``` +两种检测方法，用于检测当前状态 `start` 能否到达目标状态 `goal` + +```python +# 判断初始状态能否到达目标状态 +start = {'At(Robot,Bar)', 'Holding(VacuumCup)', 'Available(Table)', 'Available(CoffeeMachine)','Available(FrontDesk)'} +# 方法一：算法返回所有可能的初始状态，在里面看看有没有对应的初始状态 +right_bt = algo.find_all_leaf_states_contain_start(start) +if not right_bt: + print("ERROR1: The current state cannot reach the goal state!") +else: + print("Right1: The current state can reach the goal state!") + + +# 方法二：预先跑一边行为树，看能否到达目标状态 +right_bt2 = algo.run_bt_from_start(goal,start) +if not right_bt2: + print("ERROR2: The current state cannot reach the goal state!") +else: + print("Right2: The current state can reach the goal state!") + +``` + diff --git a/opt_bt_expansion/examples.py b/opt_bt_expansion/examples.py new file mode 100644 index 0000000..743f554 --- /dev/null +++ b/opt_bt_expansion/examples.py @@ -0,0 +1,174 @@ + +from opt_bt_expansion.OptimalBTExpansionAlgorithm import Action + + + +def MakeCoffee(): + actions=[ + Action(name='Put(Table,Coffee)', pre={'Holding(Coffee)','At(Table)'}, add={'At(Table,Coffee)','NotHolding'}, del_set={'Holding(Coffee)'}, cost=1), + Action(name='Put(Table,VacuumCup)', pre={'Holding(VacuumCup)','At(Table)'}, add={'At(Table,VacuumCup)','NotHolding'}, del_set={'Holding(VacuumCup)'}, cost=1), + + Action(name='Grasp(Coffee)', pre={'NotHolding','At(Coffee)'}, add={'Holding(Coffee)'}, del_set={'NotHolding'}, cost=1), + + Action(name='MoveTo(Table)', pre={'Exist(Table)'}, add={'At(Table)'}, del_set={'At(FrontDesk)','At(Coffee)','At(CoffeeMachine)'}, cost=1), + Action(name='MoveTo(Coffee)', pre={'Exist(Coffee)'}, add={'At(Coffee)'}, del_set={'At(FrontDesk)','At(Table)','At(CoffeeMachine)'}, cost=1), + Action(name='MoveTo(CoffeeMachine)', pre={'Exist(CoffeeMachine)'}, add={'At(CoffeeMachine)'}, del_set={'At(FrontDesk)','At(Coffee)','At(Table)'}, cost=1), + + Action(name='OpCoffeeMachine', pre={'At(CoffeeMachine)','NotHolding'}, add={'Exist(Coffee)','At(Coffee)'}, del_set=set(), cost=1), + ] + + start = {'At(FrontDesk)','Holding(VacuumCup)','Exist(Table)','Exist(CoffeeMachine)','Exist(FrontDesk)'} + goal = {'At(Table,Coffee)'} + return goal,start,actions + +# 本例子中，将 VacuumCup 放到 FrontDesk，比 MoveTo(Table) 再 Put(Table,VacuumCup) 的 cost 要小 +def MakeCoffeeCost(): + actions=[ + Action(name='PutDown(Table,Coffee)', pre={'Holding(Coffee)','At(Robot,Table)'}, add={'At(Table,Coffee)','NotHolding'}, del_set={'Holding(Coffee)'}, cost=1), + Action(name='PutDown(Table,VacuumCup)', pre={'Holding(VacuumCup)','At(Robot,Table)'}, add={'At(Table,VacuumCup)','NotHolding'}, del_set={'Holding(VacuumCup)'}, cost=1), + + Action(name='PickUp(Coffee)', pre={'NotHolding','At(Robot,Coffee)'}, add={'Holding(Coffee)'}, del_set={'NotHolding'}, cost=1), + + Action(name='MoveTo(Table)', pre={'Available(Table)'}, add={'At(Robot,Table)'}, del_set={'At(Robot,FrontDesk)','At(Robot,Coffee)','At(Robot,CoffeeMachine)'}, cost=1), + Action(name='MoveTo(Coffee)', pre={'Available(Coffee)'}, add={'At(Robot,Coffee)'}, del_set={'At(Robot,FrontDesk)','At(Robot,Table)','At(Robot,CoffeeMachine)'}, cost=1), + Action(name='MoveTo(CoffeeMachine)', pre={'Available(CoffeeMachine)'}, add={'At(Robot,CoffeeMachine)'}, del_set={'At(Robot,FrontDesk)','At(Robot,Coffee)','At(Robot,Table)'}, cost=1), + + Action(name='OpCoffeeMachine', pre={'At(Robot,CoffeeMachine)','NotHolding'}, add={'Available(Coffee)','At(Robot,Coffee)'}, del_set=set(), cost=1), + ] + + start = {'At(Robot,Bar)','Holding(VacuumCup)','Available(Table)','Available(CoffeeMachine)','Available(FrontDesk)'} + goal = {'At(Table,Coffee)'} + + return goal,start,actions + + +# test +def Test(): + actions=[ + Action(name='a1', pre={6}, add={0,2,4}, del_set={1,5}, cost=1), + Action(name='a2', pre=set(), add={0,1}, del_set=set(), cost=1), + Action(name='a3', pre={1,6}, add={0,2,3,5}, del_set={1,6}, cost=1), + Action(name='a4', pre={0,2,3}, add={4,5}, del_set={0,6}, cost=1), + Action(name='a5', pre={0,1,4}, add={2,3,6}, del_set={0}, cost=1), + ] + + start = {1,2,6} + goal={0,1,2,4,6} + return goal,start,actions + +# def Test(): +# actions=[ +# Action(name='a1', pre={2}, add={1}, del_set=set(), cost=1), +# Action(name='a2', pre=set(), add={1}, del_set={0,2}, cost=1), +# Action(name='a3', pre={1}, add=set(), del_set={0,2}, cost=1), +# Action(name='a4', pre=set(), add={0}, del_set=set(), cost=1), +# Action(name='a5', pre={1}, add={0,2}, del_set={1}, cost=1), +# Action(name='a6', pre={1}, add=set(), del_set={0,1,2}, cost=1), +# Action(name='a7', pre={1}, add={2}, del_set={0, 2}, cost=1), +# ] +# +# start = {1,2} +# goal={0,1} +# return goal,start,actions + + +# todo: 最原始的例子 +def MoveBtoB_num (): + actions=[] + a = Action(name='a1') + a.pre={1,4} + a.add={"c_goal"} + a.del_set={1,4} + a.cost = 1 + actions.append(a) + + a=Action(name='a2') + a.pre={1,2,3} + a.add={"c_goal"} + a.del_set={1,2,3} + a.cost = 1 + actions.append(a) + + a=Action(name='a3') + a.pre={1,2} + a.add={4} + a.del_set={2} + a.cost = 1 + actions.append(a) + + a=Action(name='a4') + a.pre={"c_start"} + a.add={1,2,3} + a.del_set={"c_start",4} + a.cost = 1 + actions.append(a) + + start = {"c_start"} + goal={"c_goal"} + return goal,start,actions + + +# todo: 最原始的例子 +def MoveBtoB (): + actions=[] + a = Action(name="Move(b,ab)") #'movebtob' + a.pre={'Free(ab)','WayClear'} #{1,2} + a.add={'At(b,ab)'} #{3} + a.del_set= {'Free(ab)','At(b,pb)'} #{1,4} + a.cost = 1 + actions.append(a) + + a=Action(name="Move(s,ab)") #'moveatob' + a.pre={'Free(ab)'} #{1} + a.add={'Free(ab)','WayClear'} #{5,2} + a.del_set={'Free(ab)','At(s,ps)'} #{1,6} + a.cost = 1 + actions.append(a) + + a=Action(name="Move(s,as)") #'moveatoa' + a.pre={'Free(as)'} #{7} + a.add={'At(s,ps)','WayClear'} #{8,2} + a.del_set={'Free(as)','At(s,ps)'} #{7,6} + a.cost = 1 + actions.append(a) + + start = {'Free(ab)','Free(as)','At(b,pb)','At(s,ps)'} #{1,7,4,6} + goal= {'At(b,ab)'} #{3} + return goal,start,actions + + +# 小蔡师兄论文里的例子 +def Cond2BelongsToCond3(): + actions=[] + a = Action(name='a1') + a.pre={1,4} + a.add={"c_goal"} + a.del_set={1,4} + a.cost = 1 + actions.append(a) + + a=Action(name='a2') + a.pre={1,2,3} + a.add={"c_goal"} + a.del_set={1,2,3} + a.cost = 100 + actions.append(a) + + a=Action(name='a3') + a.pre={1,2} + a.add={4} + a.del_set={2} + a.cost = 1 + actions.append(a) + + a=Action(name='a4') + a.pre={"c_start"} + a.add={1,2,3} + a.del_set={"c_start",4} + a.cost = 1 + actions.append(a) + + start = {"c_start"} + goal={"c_goal"} + return goal,start,actions + diff --git a/opt_bt_expansion/opt_bt_exp_main.py b/opt_bt_expansion/opt_bt_exp_main.py new file mode 100644 index 0000000..5047370 --- /dev/null +++ b/opt_bt_expansion/opt_bt_exp_main.py @@ -0,0 +1,121 @@ +from opt_bt_expansion.BehaviorTree import Leaf,ControlBT # 行为结点类：叶子结点和非叶子节点 +from opt_bt_expansion.OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm,state_transition # 调用最优行为树扩展算法 +from opt_bt_expansion.tools import print_action_data_table,BTTest +from opt_bt_expansion.examples import MoveBtoB_num,MoveBtoB,Cond2BelongsToCond3 # 导入三个例子 +from opt_bt_expansion.examples import * + + +# 封装好的主接口 +class BTOptExpInterface: + def __init__(self, action_list): + """ + Initialize the BTOptExpansion with a list of actions. + :param action_list: A list of actions to be used in the behavior tree. + """ + # self.actions = [] + # for act in action_list: + # a = Action(name=act.name) + # a.pre=act['pre'] + # a.add=act['add'] + # a.del_set= act['del_set'] + # a.cost = 1 + # self.actions.append(a) + self.actions = action_list + self.has_processed = False + + def process(self, goal): + """ + Process the input sets and return a string result. + :param input_set: The set of goal states and the set of initial states. + :return: A PTML string representing the outcome of the behavior tree. + """ + self.goal = goal + self.algo = OptBTExpAlgorithm(verbose=False) + self.algo.clear() + self.algo.run_algorithm(self.goal, self.actions) # 调用算法得到行为树保存至 algo.bt + self.ptml_string = self.algo.get_ptml() + self.has_processed = True + # algo.print_solution() # print behavior tree + + return self.ptml_string + + # 方法一：查找所有初始状态是否包含当前状态 + def find_all_leaf_states_contain_start(self,start): + if not self.has_processed: + raise RuntimeError("The process method must be called before find_all_leaf_states_contain_start!") + # 返回所有能到达目标状态的初始状态 + state_leafs = self.algo.get_all_state_leafs() + for state in state_leafs: + if start >= state: + return True + return False + + # 方法二：模拟跑一遍行为树，看 start 能够通过执行一系列动作到达 goal + def run_bt_from_start(self,goal,start): + if not self.has_processed: + raise RuntimeError("The process method must be called before run_bt_from_start!") + # 检查是否能到达目标 + right_bt = True + state = start + steps = 0 + val, obj = self.algo.bt.tick(state) + while val != 'success' and val != 'failure': + state = state_transition(state, obj) + val, obj = self.algo.bt.tick(state) + if (val == 'failure'): + # print("bt fails at step", steps) + right_bt = False + steps += 1 + if not goal <= state: + # print("wrong solution", steps) + right_bt = False + else: + pass + # print("right solution", steps) + return right_bt + + + + +if __name__ == '__main__' : + + # todo: Example Cafe + # todo: Define goal, start, actions + actions=[ + Action(name='PutDown(Table,Coffee)', pre={'Holding(Coffee)','At(Robot,Table)'}, add={'At(Table,Coffee)','NotHolding'}, del_set={'Holding(Coffee)'}, cost=1), + Action(name='PutDown(Table,VacuumCup)', pre={'Holding(VacuumCup)','At(Robot,Table)'}, add={'At(Table,VacuumCup)','NotHolding'}, del_set={'Holding(VacuumCup)'}, cost=1), + + Action(name='PickUp(Coffee)', pre={'NotHolding','At(Robot,Coffee)'}, add={'Holding(Coffee)'}, del_set={'NotHolding'}, cost=1), + + Action(name='MoveTo(Table)', pre={'Available(Table)'}, add={'At(Robot,Table)'}, del_set={'At(Robot,FrontDesk)','At(Robot,Coffee)','At(Robot,CoffeeMachine)'}, cost=1), + Action(name='MoveTo(Coffee)', pre={'Available(Coffee)'}, add={'At(Robot,Coffee)'}, del_set={'At(Robot,FrontDesk)','At(Robot,Table)','At(Robot,CoffeeMachine)'}, cost=1), + Action(name='MoveTo(CoffeeMachine)', pre={'Available(CoffeeMachine)'}, add={'At(Robot,CoffeeMachine)'}, del_set={'At(Robot,FrontDesk)','At(Robot,Coffee)','At(Robot,Table)'}, cost=1), + + Action(name='OpCoffeeMachine', pre={'At(Robot,CoffeeMachine)','NotHolding'}, add={'Available(Coffee)','At(Robot,Coffee)'}, del_set=set(), cost=1), + ] + algo = BTOptExpInterface(actions) + + + goal = {'At(Table,Coffee)'} + ptml_string = algo.process(goal) + print(ptml_string) + + file_name = "MakeCoffee" + with open(f'./{file_name}.ptml', 'w') as file: + file.write(ptml_string) + + + # 判断初始状态能否到达目标状态 + start = {'At(Robot,Bar)', 'Holding(VacuumCup)', 'Available(Table)', 'Available(CoffeeMachine)','Available(FrontDesk)'} + # 方法一：算法返回所有可能的初始状态，在里面看看有没有对应的初始状态 + right_bt = algo.find_all_leaf_states_contain_start(start) + if not right_bt: + print("ERROR1: The current state cannot reach the goal state!") + else: + print("Right1: The current state can reach the goal state!") + # 方法二：预先跑一边行为树，看能否到达目标状态 + right_bt2 = algo.run_bt_from_start(goal,start) + if not right_bt2: + print("ERROR2: The current state cannot reach the goal state!") + else: + print("Right2: The current state can reach the goal state!") diff --git a/opt_bt_expansion/tools.py b/opt_bt_expansion/tools.py new file mode 100644 index 0000000..cf84229 --- /dev/null +++ b/opt_bt_expansion/tools.py @@ -0,0 +1,167 @@ + + +from tabulate import tabulate +import numpy as np +import random +from opt_bt_expansion.OptimalBTExpansionAlgorithm import Action,OptBTExpAlgorithm +import time + + +def print_action_data_table(goal,start,actions): + data = [] + for a in actions: + data.append([a.name ,a.pre ,a.add ,a.del_set ,a.cost]) + data.append(["Goal" ,goal ," " ,"Start" ,start]) + print(tabulate(data, headers=["Name", "Pre", "Add" ,"Del" ,"Cost"], tablefmt="fancy_grid")) # grid plain simple github fancy_grid + + +# 从状态随机生成一个行动 +def generate_from_state(act,state,num): + for i in range(0,num): + if i in state: + if random.random() >0.5: + act.pre.add(i) + if random.random() >0.5: + act.del_set.add(i) + continue + if random.random() > 0.5: + act.add.add(i) + continue + if random.random() >0.5: + act.del_set.add(i) + +def print_action(act): + print (act.pre) + print(act.add) + print(act.del_set) + + + +#行为树测试代码 +def BTTest(seed=1,literals_num=10,depth=10,iters=10,total_count=1000): + print("============= BT Test ==============") + random.seed(seed) + # 设置生成规划问题集的超参数：文字数、解深度、迭代次数 + literals_num=literals_num + depth = depth + iters= iters + total_tree_size = [] + total_action_num = [] + total_state_num = [] + total_steps_num=[] + #fail_count=0 + #danger_count=0 + success_count =0 + failure_count = 0 + planning_time_total = 0.0 + # 实验1000次 + for count in range (total_count): + + action_num = 1 + + # 生成一个规划问题，包括随机的状态和行动，以及目标状态 + states = [] + actions = [] + start = generate_random_state(literals_num) + state = start + states.append(state) + #print (state) + for i in range (0,depth): + a = Action() + generate_from_state(a,state,literals_num) + if not a in actions: + a.name = "a"+str(action_num) + action_num+=1 + actions.append(a) + state = state_transition(state,a) + if state in states: + pass + else: + states.append(state) + #print(state) + + goal = states[-1] + state = start + for i in range (0,iters): + a = Action() + generate_from_state(a,state,literals_num) + if not a in actions: + a.name = "a"+str(action_num) + action_num+=1 + actions.append(a) + state = state_transition(state,a) + if state in states: + pass + else: + states.append(state) + state = random.sample(states,1)[0] + + # 选择测试本文算法btalgorithm，或对比算法weakalgorithm + algo = OptBTExpAlgorithm() + #algo = Weakalgorithm() + start_time = time.time() + # print_action_data_table(goal, start, list(actions)) + if algo.run_algorithm(start, goal, actions):#运行算法，规划后行为树为algo.bt + total_tree_size.append( algo.bt.count_size()-1) + # algo.print_solution() # 打印行为树 + else: + print ("error") + end_time = time.time() + planning_time_total += (end_time-start_time) + + #开始从初始状态运行行为树，测试 + state=start + steps=0 + val, obj = algo.bt.tick(state)#tick行为树，obj为所运行的行动 + while val !='success' and val !='failure':#运行直到行为树成功或失败 + state = state_transition(state,obj) + val, obj = algo.bt.tick(state) + if(val == 'failure'): + print("bt fails at step",steps) + steps+=1 + if(steps>=500):#至多运行500步 + break + if not goal <= state:#错误解，目标条件不在执行后状态满足 + #print ("wrong solution",steps) + failure_count+=1 + + else:#正确解，满足目标条件 + #print ("right solution",steps) + success_count+=1 + total_steps_num.append(steps) + algo.clear() + total_action_num.append(len(actions)) + total_state_num.append(len(states)) + + print ("success:",success_count,"failure:",failure_count)#算法成功和失败次数 + 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 + print ("Total Steps Num: mean=",np.mean(total_steps_num),"std=",np.std(total_steps_num,ddof=1)) + print ("Average number of states:",np.mean(total_state_num))#1000次问题的平均状态数 + print ("Average number of actions",np.mean(total_action_num))#1000次问题的平均行动数 + print("Planning Time Total:",planning_time_total,planning_time_total/1000.0) + print("============ End BT Test ===========") + + # xiao cai + # success: 1000 failure: 0 + # Total Tree Size: mean= 35.303 std= 29.71336526001515 + # Total Steps Num: mean= 1.898 std= 0.970844240101644 + # Average number of states: 20.678 + # Average number of actions 20.0 + # Planning Time Total: 0.6280641555786133 0.0006280641555786133 + + # our start + # success: 1000 failure: 0 + # Total Tree Size: mean= 17.945 std= 12.841997192488865 + # Total Steps Num: mean= 1.785 std= 0.8120556843187752 + # Average number of states: 20.678 + # Average number of actions 20.0 + # Planning Time Total: 1.4748523235321045 0.0014748523235321046 + + # our + # success: 1000 failure: 0 + # Total Tree Size: mean= 48.764 std= 20.503626574406358 + # Total Steps Num: mean= 1.785 std= 0.8120556843187752 + # Average number of states: 20.678 + # Average number of actions 20.0 + # Planning Time Total: 3.3271877765655518 0.0033271877765655516 +