Merge branch 'main' of github.com:HPCL-EI/RoboWaiter

requirements.txt

@@ -1,15 +1,20 @@
 antlr4-python3-runtime
 py_trees
-shortuuid
+shortuuid~=1.0.11
 protobuf==3.20.0
 gym==0.21.0
 grpcio==1.53.0
-requests
+requests~=2.31.0
-urllib3
+urllib3~=2.0.7
-tabulate
+tabulate~=0.9.0
 autopep8
 pytorch==1.11.0
 torchvision==0.12.0
 torchaudio==0.11.0
 cudatoolkit=11.3
-loguru
+loguru~=0.5.3
+matplotlib~=3.8.0
+numpy~=1.26.0
+setuptools~=68.0.0
+pydot~=1.4.2
+colorama~=0.4.6

robowaiter/algos/explore/scene.py

@@ -1,4 +1,5 @@
 import time
+import math
 import grpc
 import numpy as np
 
@@ -358,29 +359,29 @@ class Scene:
         temp = stub.GetIKControlInfos(GrabSim_pb2.HandPostureInfos(scene=self.sceneID, handPostureObjects=HandPostureObject))
 
     # 移动到进行操作任务的指定地点
-    def move_task_area(self,op_type):
+    # def move_task_area(self,op_type):
-        if op_type==11 or op_type==12:  # 开关窗帘不需要移动
+    #     if op_type==11 or op_type==12:  # 开关窗帘不需要移动
-            return
+    #         return
-        scene = stub.Observe(GrabSim_pb2.SceneID(value=self.sceneID))
+    #     scene = stub.Observe(GrabSim_pb2.SceneID(value=self.sceneID))
-        walk_value = [scene.location.X, scene.location.Y, scene.rotation.Yaw]
+    #     walk_value = [scene.location.X, scene.location.Y, scene.rotation.Yaw]
-
+    #
-        if op_type < 8:
+    #     if op_type < 8:
-            v_list = self.op_v_list[op_type]
+    #         v_list = self.op_v_list[op_type]
-        if op_type>=8 and op_type<=10:  # 控灯
+    #     if op_type>=8 and op_type<=10:  # 控灯
-            v_list = self.op_v_list[6]
+    #         v_list = self.op_v_list[6]
-        if op_type in [13,14,15]:   # 空调
+    #     if op_type in [13,14,15]:   # 空调
-            v_list = [[240, -140.0]]  # KongTiao [300.5, -140.0]  # 250
+    #         v_list = [[240, -140.0]]  # KongTiao [300.5, -140.0]  # 250
-
+    #     print("------------------error version----------------------")
-        print("------------------move_task_area----------------------")
+    #     print("------------------move_task_area----------------------")
-        print("Current Position:", walk_value,"开始任务:",self.op_dialog[op_type])
+    #     print("Current Position:", walk_value,"开始任务:",self.op_dialog[op_type])
-        for walk_v in v_list:
+    #     for walk_v in v_list:
-            walk_v = walk_v + [scene.rotation.Yaw, 180, 0]
+    #         walk_v = walk_v + [scene.rotation.Yaw, 180, 0]
-            walk_v[2] = 0 if (op_type in [13,14,15]) else scene.rotation.Yaw   # 空调操作朝向墙面
+    #         walk_v[2] = 0 if (op_type in [13,14,15]) else scene.rotation.Yaw   # 空调操作朝向墙面
-            action = GrabSim_pb2.Action(
+    #         action = GrabSim_pb2.Action(
-                scene=self.sceneID, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v
+    #             scene=self.sceneID, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v
-            )
+    #         )
-            scene = stub.Do(action)
+    #         scene = stub.Do(action)
-        print("After Walk Position:",[scene.location.X, scene.location.Y, scene.rotation.Yaw])
+    #     print("After Walk Position:",[scene.location.X, scene.location.Y, scene.rotation.Yaw])
 
     # 相应的行动，由主办方封装
     def control_robot_action(self, type=0, action=0, message="你好"):
@@ -512,3 +513,4 @@ class Scene:
             print(scene.info)
 
 
+

robowaiter/algos/navigate/discretize_map.py

@@ -1,59 +0,0 @@
-# !/usr/bin/env python3
-# -*- encoding: utf-8 -*-
-
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pickle
-import os
-
-
-
-
-
-def draw_grid_map(grid_map):
-    # 生成新的地图图像
-    plt.imshow(grid_map, cmap='binary', alpha=0.5, origin='lower')  # 黑白网格
-
-    # 绘制坐标轴
-    plt.xlabel('y', loc='right')
-    plt.ylabel('x', loc='top')
-
-    # 显示网格线
-    plt.grid(color='black', linestyle='-', linewidth=0.5)
-
-    # 显示图像
-    plt.show()
-    #plt.pause(0.01)
-
-
-
-
-if __name__ == '__main__':
-    # control.init_world(scene_num=1, mapID=3)
-    # scene = control.Scene(sceneID=0)
-    #
-    # X = int(950/5)  # 采点数量
-    # Y = int(1850/5)
-    # map = np.zeros((X, Y))
-    #
-    # for x in range(X):
-    #     for y in range(Y):
-    #         if not scene.reachable_check(x*5-350, y*5-400, Yaw=0):
-    #             map[x, y] = 1
-    #             print(x, y)
-    #
-    #
-    # file_name = 'map_5.pkl'
-    # if not os.path.exists(file_name):
-    #     open(file_name, 'w').close()
-    # with open(file_name, 'wb') as file:
-    #     pickle.dump(map, file)
-    # print('保存成功')
-
-
-    file_name = 'map_5.pkl'
-    if os.path.exists(file_name):
-        with open(file_name, 'rb') as file:
-            map = pickle.load(file)
-            draw_grid_map(map)

robowaiter/algos/navigate/navigate.py

@@ -1,158 +0,0 @@
-#!/usr/bin/env python3
-# -*- encoding: utf-8 -*-
-import math
-import sys
-import time
-
-import matplotlib.pyplot as plt
-import numpy as np
-from mpl_toolkits.axes_grid1 import make_axes_locatable
-# from scene import control
-
-# from rrt import RRT
-# from rrt_star import RRTStar
-# from apf import APF
-from robowaiter.algos.navigate.dstar_lite import DStarLite, euclidean_distance
-
-class Navigator:
-    '''
-        导航类
-    '''
-
-    def __init__(self, scene, area_range, map, scale_ratio=5, step_length=150, velocity=150, react_radius=300):
-        self.scene = scene
-        self.area_range = area_range  # 地图实际坐标范围 xmin, xmax, ymin, ymax
-        self.map = map  # 缩放并离散化的地图 array(X,Y)
-        self.scale_ratio = scale_ratio  # 地图缩放率
-        self.step_length = step_length  # 步长(单次移动)
-        self.step_num = self.step_length // self.scale_ratio  # 单次移动地图格数
-        self.v = velocity  # 速度
-        self.react_radius = react_radius  # robot反应半径
-
-        # self.planner = RRTStar(rand_area=area_range, map=map, scale_ratio=scale_ratio, max_iter=400, search_until_max_iter=True)
-        self.planner = DStarLite(area_range=area_range, map=map, scale_ratio=scale_ratio)
-
-    @staticmethod
-    def is_reached(pos: (float, float), goal: (float, float), dis_limit=30):
-        '''
-            判断是否到达目标
-        '''
-        dis = math.hypot(pos[0]-goal[0], pos[1]-goal[1])
-        # dis = np.linalg.norm(pos - goal)
-        return dis < dis_limit
-
-    @staticmethod
-    def get_yaw(pos: (float, float), goal: (float, float)):
-        '''
-            得到移动方向
-        '''
-        return math.degrees(math.atan2((goal[1] - pos[1]), (goal[0] - pos[0])))
-
-    def legalize_goal(self, goal: (float, float)):
-        '''
-            TODO: 处理非法目标
-            目标在障碍物上：从目标开始方形向外扩展，直到找到可行点
-            目标在地图外面：起点和目标连线最靠近目标的可行点
-        '''
-        return goal
-
-    def navigate(self, goal: (float, float), animation=True):
-        '''
-            单次导航，直到到达目标
-        '''
-        if not self.scene.reachable_check(goal[0], goal[1], 0):
-            goal = self.legalize_goal(goal)
-
-        pos = (self.scene.status.location.X, self.scene.status.location.Y)  # 机器人当前: 位置 和 朝向
-        print('------------------navigation_start----------------------')
-        while not self.is_reached(pos, goal):
-            dyna_obs = [(walker.pose.X, walker.pose.Y) for walker in self.scene.status.walkers]  # 动态障碍物(顾客)位置列表
-            dyna_obs = [obs for obs in dyna_obs if euclidean_distance(obs, pos) < self.react_radius]  # 过滤观测范围外的dyna_obs
-            # 周围有dyna_obs则步长减半
-            if dyna_obs:
-                step_num = self.step_num // 2
-            else:
-                step_num = self.step_num
-            path = self.planner.planning(pos, goal, dyna_obs)
-            if path:
-                if animation:
-                    self.planner.draw_graph(step_num)  # 画出搜索路径
-                next_step = min(step_num, len(path))
-                next_pos = path[next_step - 1]
-                # print('plan pos:', next_pos, end=' ')
-                yaw = self.get_yaw(pos, next_pos)
-                # print("yaw:",yaw)
-                self.scene.walk_to(next_pos[0], next_pos[1], Yaw=yaw, velocity=self.v, dis_limit=10)
-                # pos = (self.scene.status.location.X, self.scene.status.location.Y)
-                # if self.is_reached(pos, next_pos):
-                self.planner.path = self.planner.path[next_step - 1:]  # 去除已走过的路径
-            pos = (self.scene.status.location.X, self.scene.status.location.Y)
-            # print('reach pos:', pos)
-
-        self.planner.reset()  # 完成一轮导航，重置变量
-
-        if self.is_reached(pos, goal):
-            print('The robot has achieved goal !!')
-
-
-
-
-
-
-
-
-
-
-        # def navigate(self, goal: (float, float), path_smoothing=True, animation=True):
-        #     pos = np.array((self.scene.status.location.X, self.scene.status.location.Y))  # 机器人当前: 位置 和 朝向
-        #     yaw = self.scene.status.rotation.Yaw
-        #     print('------------------navigation_start----------------------')
-        #
-        #     path = self.planner.planning(pos, goal, path_smoothing, animation)
-        #     if path:
-        #         self.planner.draw_graph(final_path=path)  # 画出探索过程
-        #         for (x, y) in path:
-        #             self.scene.walk_to(x, y, yaw, velocity=self.v)
-        #             time.sleep(self.step_time)
-        #         pos = np.array((self.scene.status.location.X, self.scene.status.location.Y))
-        #
-        #     self.planner.reset()
-        #
-        #     if self.is_reached(pos, goal):
-        #         print('The robot has achieved goal !!')
-
-        '''APF势场法暂不可用'''
-        # while not self.is_reached(pos, goal):
-        #     # 1. 路径规划
-        #     path = self.planner.planning(pos, goal, path_smoothing, animation)
-        #     self.planner.draw_graph(final_path=path)  # 画出探索过程
-        #
-        #     # 2. 使用APF导航到路径中的每个waypoint
-        #     traj = [(pos[0], pos[1])]
-        #     #self.planner.draw_graph(final_path=traj)  # 画出探索过程
-        #     for i, waypoint in enumerate(path[1:]):
-        #         print('waypoint [', i, ']:', waypoint)
-        #         # if (not self.scene.reachable_check(waypoint[0], waypoint[1], yaw)) and self.map[self.planner.real2map(waypoint[0], waypoint[1])] == 0:
-        #         #     print('error')
-        #         while not self.is_reached(pos, waypoint):
-        #             # 2.1 计算next_step
-        #             pos = np.array((self.scene.status.location.X, self.scene.status.location.Y))
-        #             Pobs = []  # 障碍物(顾客)位置数组
-        #             for walker in self.scene.status.walkers:
-        #                 Pobs.append((walker.pose.X, walker.pose.Y))
-        #             next_step, _ = APF(Pi=pos, Pg=waypoint, Pobs=Pobs, step_length=self.step_length)
-        #             traj.append((next_step[0], next_step[1]))
-        #             #self.planner.draw_graph(final_path=traj)  # 画出探索过程
-        #             while not self.scene.reachable_check(next_step[0], next_step[1], yaw):  # 取中点直到next_step可达
-        #                 traj.pop()
-        #                 next_step = (pos + next_step) / 2
-        #                 traj.append((next_step[0], next_step[1]))
-        #                 #self.planner.draw_graph(final_path=traj)  # 画出探索过程
-        #             # 2.2 移动robot
-        #             self.scene.walk_to(next_step[0], next_step[1], yaw, velocity=self.v)
-        #             # print(self.scene.status.info)  # print navigation info
-        #             # print(self.scene.status.collision)
-        #             time.sleep(self.step_time)
-        #         # print(self.scene.status.info)  # print navigation info
-        #         # print(self.scene.status.collision)
-        #     self.planner.reset()

robowaiter/algos/navigate/readme.md

@@ -1,52 +0,0 @@
-## 默认使用RRTStar+路径平滑
-
-### apf.py: 势场法实现
-
-### discretize_map.py: 地图离散化并压缩
-
-### map_5.pkl: 地图文件(5倍压缩)
-
-### navigate.py: 导航类
-
-### pathsmoothing.py: 路径平滑
-
-### rrt.py: RRT实现
-
-### rrt_star.py: RRTStar 实现
-
-### test.py: 测试文件
-
-
-
-
-## TODO
-
-### 目标不合法
-#### 初始目标不合法
-目标在障碍物上：从目标开始方形向外扩展，直到找到可行点
-目标在地图外面：起点和目标连线最靠近目标的可行点
-对不合法的目标做单独处理，生成新的目标
-
-#### 在移动过程中目标被占据
-给出目标但不会行移动，程序会继续运行，重新计算规划路径，给出新目标
-
-
-### 规划中断情况
-
-
-###  计算转向角
-`完成`
-
-
-### 有些本来可达的位置却无法走到（系统bug?）
-例如从初始位置(247, 500) 移动到(115, -10)，无法到达
-`(已解决)将dis_limit设为小值5而非0`
-####  构型空间膨胀？？
-`不需要`
-
-### 只考虑一定范围内的行人
-观测范围 / 反应半径
-
-`完成`
-#### 观测范围内有行人步长要减小
-`完成`

robowaiter/algos/navigator/discretize_map.py

@@ -0,0 +1,90 @@
+# !/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pickle
+import os
+
+from scipy.ndimage import binary_dilation
+
+from scene import scene
+
+
+def draw_grid_map(grid_map):
+    # 生成新的地图图像
+    plt.imshow(grid_map, cmap='binary', alpha=0.5, origin='lower')  # 黑白网格
+
+    # 绘制坐标轴
+    plt.xlabel('y', loc='right')
+    plt.ylabel('x', loc='top')
+
+    # 显示网格线
+    plt.grid(color='black', linestyle='-', linewidth=0.5)
+
+    # 显示图像
+    plt.show()
+    #plt.pause(0.01)
+
+
+def discretize_map(scene, scale_ratio):
+    X = int(950 / scale_ratio)  # 采点数量
+    Y = int(1850 / scale_ratio)
+    map = np.zeros((X, Y))
+
+    for x in range(X):
+        for y in range(Y):
+            if not scene.reachable_check(x * scale_ratio - 350, y * scale_ratio - 400, Yaw=0):
+                map[x, y] = 1
+                print(x, y)
+
+    file_name = 'map_'+str(scale_ratio)+'.pkl'
+    if not os.path.exists(file_name):
+        open(file_name, 'w').close()
+    with open(file_name, 'wb') as file:
+        pickle.dump(map, file)
+    print('保存成功')
+
+
+def expand_obstacles(scale_ratio, expand_range=1):
+    '''
+        障碍物边沿扩展
+        TODO: 扩展后的地图不可用！！！
+    '''
+    file_name = 'map_'+str(scale_ratio)+'.pkl'
+    dilated_file_name = 'map_'+str(scale_ratio)+'_e'+str(expand_range)+'.pkl'
+
+    if os.path.exists(file_name):
+        with open(file_name, 'rb') as file:
+            map = pickle.load(file)
+
+    dilated_map = binary_dilation(map, iterations=expand_range)
+
+    if not os.path.exists(dilated_file_name):
+        open(dilated_file_name, 'w').close()
+    with open(dilated_file_name, 'wb') as file:
+        pickle.dump(dilated_map, file)
+    print('保存成功')
+
+
+def show_map(file_name):
+    if os.path.exists(file_name):
+        with open(file_name, 'rb') as file:
+            map = pickle.load(file)
+            draw_grid_map(map)
+
+
+if __name__ == '__main__':
+    # scene.init_world(scene_num=1, mapID=11)
+    # scene = scene.Scene(sceneID=0)
+    #
+    # # 离散化地图
+    # discretize_map(scene, scale_ratio=4)
+
+    # # 扩张构型空间
+    # expand_obstacles(scale_ratio=3, expand_range=1)
+
+    # 展示离散化地图
+    file_name = 'costMap_4.pkl'
+    show_map(file_name)

robowaiter/algos/navigator/dstar_lite.py

@@ -5,8 +5,8 @@
 '''
 
 import math
-import queue
+import os
-from functools import partial
+import pickle
 import numpy as np
 import heapq
 
@@ -115,13 +115,12 @@ class PriorityQueue:
 
 class DStarLite:
     def __init__(self,
-                 map: np.array([int, int]),  # [X, Y]
+                 map: np.array([int, int]),
-                 area_range,  # [x_min, x_max, y_min, y_max] 实际坐标范围
+                 area_range,            # [x_min, x_max, y_min, y_max] 实际坐标范围
-                 scale_ratio=5,  # 地图缩放率
+                 scale_ratio=5,         # 地图缩放率
-                 dyna_obs_radius=30,  # dyna_obs实际身位半径
+                 dyna_obs_radius=36,    # dyna_obs实际身位半径
                  ):
 
-        # self.area_bounds = area
         self.map = map
         self.background = map.copy()
         self.X = map.shape[0]
@@ -145,9 +144,13 @@ class DStarLite:
             "obstacle": float('inf'),
             "dynamic obstacle": 100
         }
-        self.cost_map = np.zeros_like(self.map)
+
+        file_name = 'costMap_'+str(self.scale_ratio)+'.pkl'
+        if os.path.exists(file_name):
+            with open(file_name, 'rb') as file:
+                cost_map = pickle.load(file)
+        self.cost_map = cost_map
         self.cost_background = self.cost_map.copy()
-        self.compute_cost_map()
 
         self.s_start = None  # (int,int) 必须是元组(元组可以直接当作矩阵索引)
         self.s_goal = None  # (int,int)
@@ -163,9 +166,11 @@ class DStarLite:
     #         设置map 和 cost_map
     #     '''
     #     self.map = map_
+    #     self.background = map_.copy()
     #     self.X = map_.shape[0]
     #     self.Y = map_.shape[1]
     #     self.compute_cost_map()
+    #     self.cost_background = self.cost_map.copy()
 
     def reset(self):
         '''
@@ -299,11 +304,6 @@ class DStarLite:
                 self.compute_shortest_path()
                 self.path = self.get_path()
             return self.path
-        # TODO: 误差抖动使robot没有到达路径上的点，导致新起点的rhs=∞，可能导致get_path失败 ( 当前版本没有该问题 )
-        # assert (self.rhs[self.s_start] != float('inf')), "There is no known path!"
-        # # debug
-        # if debug:
-        #     pass
 
     def planning(self, s_start, s_goal, dyna_obs, debug=False):
         '''
@@ -311,7 +311,10 @@ class DStarLite:
         '''
         # 实际坐标 -> 地图坐标
         s_start = self.real2map(s_start)
-        s_goal = self.real2map(s_goal)
+        if self.s_goal is None:
+            s_goal = self.real2map(s_goal)
+        else:
+            s_goal = self.s_goal
         dyna_obs = [self.real2map(obs, reachable_assurance=False) for obs in dyna_obs]
 
         self._planning(s_start, s_goal, dyna_obs, debug)
@@ -336,13 +339,6 @@ class DStarLite:
             succ = [s_ for s_ in self.get_neighbors(cur) if s_ not in path]  # 避免抖动 (不可走重复的点)
             cur = succ[np.argmin([self.c(cur, s_) + self.g[s_] for s_ in succ])]
             path.append(cur)
-        # else:
-        #     for i in range(step_num):
-        #         if cur == self.s_goal:
-        #             break
-        #         succ = self.get_neighbors(cur)
-        #         cur = succ[np.argmin([self.c(cur, s_) + self.g[s_] for s_ in succ])]
-        #         path.append(cur)
         return path
 
     def in_bounds_without_obstacle(self, pos):
@@ -357,8 +353,6 @@ class DStarLite:
             获取邻居节点, 地图范围内
         '''
         (x_, y_) = pos
-        # results = [(x_+1,y_), (x_-1,y_), (x_, y_+1), (x_,y_-1)]
-        # if mode == 8:
         neighbors = [(x_ + 1, y_), (x_ - 1, y_), (x_, y_ + 1), (x_, y_ - 1), (x_ + 1, y_ + 1), (x_ - 1, y_ + 1),
                      (x_ + 1, y_ - 1), (x_ - 1, y_ - 1)]
         neighbors = filter(self.in_bounds_without_obstacle, neighbors)  # 确保位置在地图范围内 且 不是静态障碍物
@@ -366,17 +360,26 @@ class DStarLite:
 
     def compute_cost_map(self):
         # 计算当前地图的cost_map
+        self.cost_map = np.zeros_like(self.map)
         for idx, obj in self.idx_to_object.items():
             self.cost_map[self.map == idx] = self.object_to_cost[obj]
 
-        # # TODO
+        # 扩张静态障碍物影响范围
-        # for x in range(self.X):
+        obs_pos = np.where(self.map == self.object_to_idx['obstacle'])  # 静态障碍物位置列表
-        #     for y in range(self.Y):
+        for (x, y) in zip(obs_pos[0], obs_pos[1]):
-        #         if self.cost_map[x, y] > 0:
+            start_x, end_x = max(x - 1, 0), min(x + 1, self.X - 1)
-        #             neighbors = self.get_neighbors((x, y))
+            start_y, end_y = max(y - 1, 0), min(y + 1, self.Y - 1)
-        #             for (x_, y_) in neighbors:
+            for cost in range(9, 0, -3):
-        #                 self.cost_map[x_, y_] = max(self.cost_map[x_, y_], self.cost_map[x, y] - 10)
+                for x_ in range(start_x, end_x + 1):
-
+                    self.cost_map[x_, start_y] = max(self.cost_map[x_, start_y], cost)
+                for y_ in range(start_y + 1, end_y + 1):
+                    self.cost_map[end_x, y_] = max(self.cost_map[end_x, y_], cost)
+                for x_ in range(end_x - 1, start_x - 1, -1):
+                    self.cost_map[x_, end_y] = max(self.cost_map[x_, end_y], cost)
+                for y_ in range(end_y - 1, start_y, -1):
+                    self.cost_map[start_x, y_] = max(self.cost_map[start_x, y_], cost)
+                start_x, end_x = max(start_x - 1, 0), min(end_x + 1, self.X - 1)
+                start_y, end_y = max(start_y - 1, 0), min(end_y + 1, self.Y - 1)
 
         self.cost_background = self.cost_map.copy()
 
@@ -388,8 +391,8 @@ class DStarLite:
             return:
                 update_obj: 改变的位置列表 [(x, y, obj_idx, obj_idx_old), ...]
         '''
-        # dyna_obs没有变化 (集合set可以忽略元素在列表中的位置)
+        # dyna_obs没有变化 (集合set可以忽略元素在列表中的位置) 且 robot未在dyna_obs占用位置中
-        if set(dyna_obs) == set(self.dyna_obs_list):
+        if set(dyna_obs) == set(self.dyna_obs_list) and self.s_start not in self.dyna_obs_occupy:
             return []
 
         # 当前dyna_obs占用位置列表
@@ -397,24 +400,10 @@ class DStarLite:
         for pos in dyna_obs:
             dyna_obs_occupy.extend(self.get_occupy_pos(pos))
         dyna_obs_occupy = [pos for i, pos in enumerate(dyna_obs_occupy) if pos not in dyna_obs_occupy[:i]]  # 去除重复位置
-        # 转变为free 和 转变为obs的位置列表
+        # 转变为free 和 转变为dyna_obs的位置列表
         changed_free = [pos for pos in self.dyna_obs_occupy if pos not in dyna_obs_occupy]
         changed_obs = [pos for pos in dyna_obs_occupy if pos not in self.dyna_obs_occupy]
 
-        # # 新旧dyna_obs占用位置列表
-        # old_obs_occupy = []
-        # new_obs_occupy = []
-        # for pos in self.dyna_obs_list:
-        #     old_obs_occupy.extend(self.get_occupy_pos(pos))
-        # for pos in dyna_obs:
-        #     new_obs_occupy.extend(self.get_occupy_pos(pos))
-        # old_obs_occupy = [pos for i, pos in enumerate(old_obs_occupy) if pos not in old_obs_occupy[:i]]  # 去除重复位置
-        # new_obs_occupy = [pos for i, pos in enumerate(new_obs_occupy) if pos not in new_obs_occupy[:i]]  # 去除重复位置
-        #
-        # # 转变为free 和 转变为obs的位置列表
-        # changed_free = [pos for pos in old_obs_occupy if pos not in new_obs_occupy]
-        # changed_obs = [pos for pos in new_obs_occupy if pos not in old_obs_occupy]
-
         # 更新地图，计算changed_pos
         changed_pos = []
         for (x, y) in changed_free:
@@ -430,21 +419,15 @@ class DStarLite:
 
         return changed_pos
 
-
-
     def get_occupy_pos(self, obs_pos):
         '''
             根据dyna_obs中心位置，计算其占用的所有网格位置
         '''
         (x, y) = obs_pos
         occupy_radius = min(self.dyna_obs_radius, int(euclidean_distance(obs_pos, self.s_start) - 1))  # 避免robot被dyna_obs的占用区域包裹住
-        # for i in range(x - self.dyna_obs_radius, x + self.dyna_obs_radius + 1):  # 方形区域
-        #     for j in range(y - self.dyna_obs_radius, y + self.dyna_obs_radius + 1):
-        #         occupy_pos.append((i, j))
         occupy_pos = [(i, j) for i in range(x - occupy_radius, x + occupy_radius + 1)  # 圆形区域
                       for j in range(y - occupy_radius, y + occupy_radius + 1)
                       if euclidean_distance((i, j), obs_pos) < occupy_radius]
-
         occupy_pos = filter(self.in_bounds_without_obstacle, occupy_pos)  # 确保位置在地图范围内 且 不是静态障碍物
         return list(occupy_pos)
 
@@ -497,21 +480,40 @@ class DStarLite:
         '''
         x = round((pos[0] - self.x_min) / self.scale_ratio)
         y = round((pos[1] - self.y_min) / self.scale_ratio)
-        # 需要确保点可达
+        # 确保点不在静态障碍物上，否则就不断向外圈扩展直到找到非静态障碍物位置
-        if reachable_assurance and self.idx_to_object[self.map[x, y]] != 'free':
+        if reachable_assurance:
-            print('1')
+            return self.validate_pos((x, y))
-            x_ = math.floor((pos[0] - self.x_min) / self.scale_ratio)
-            y_ = math.floor((pos[1] - self.y_min) / self.scale_ratio)
-            candidates = [(x_, y_), (x_ + 1, y_), (x_, y_ + 1), (x_ + 1, y_ + 1)]
-            for (x, y) in candidates:
-                print(self.idx_to_object[self.map[x, y]])
-                if self.idx_to_object[self.map[x, y]] == 'free':
-                    print((x,y))
-                    return tuple((x, y))
-            raise Exception('error')
         else:
             return tuple((x, y))
 
+    def validate_pos(self, pos):
+        '''
+            对于不合法的pos，找到周围距离最近的合法坐标
+        '''
+        (x, y) = pos
+        x = max(0, min(x, self.X - 1))
+        y = max(0, min(y, self.Y - 1))
+        if self.idx_to_object[self.map[x, y]] == 'obstacle':
+            start_x, end_x = max(x - 1, 0), min(x + 1, self.X - 1)
+            start_y, end_y = max(y - 1, 0), min(y + 1, self.Y - 1)
+            while True:
+                for x_ in range(start_x, end_x + 1):
+                    if self.idx_to_object[self.map[x_, start_y]] != 'obstacle':
+                        return tuple((x_, start_y))
+                for y_ in range(start_y + 1, end_y + 1):
+                    if self.idx_to_object[self.map[end_x, y_]] != 'obstacle':
+                        return tuple((end_x, y_))
+                for x_ in range(end_x - 1, start_x - 1, -1):
+                    if self.idx_to_object[self.map[x_, end_y]] != 'obstacle':
+                        return tuple((x_, end_y))
+                for y_ in range(end_y - 1, start_y, -1):
+                    if self.idx_to_object[self.map[start_x, y_]] != 'obstacle':
+                        return tuple((start_x, y_))
+                start_x, end_x = max(start_x - 1, 0), min(end_x + 1, self.X - 1)
+                start_y, end_y = max(start_y - 1, 0), min(end_y + 1, self.Y - 1)
+            # raise Exception('invalid pos!')
+        return tuple((x, y))
+
     def draw_graph(self, step_num):
         # 清空当前figure内容，保留figure对象
         plt.clf()

robowaiter/algos/navigator/navigate.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+import math
+import os
+import pickle
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+from robowaiter.scene import scene
+
+from robowaiter.algos.navigator.dstar_lite import DStarLite, euclidean_distance
+
+
+
+class Navigator:
+    '''
+        导航类
+    '''
+
+    def __init__(self, scene, area_range, map, scale_ratio=5, step_length=150, velocity=150, react_radius=300):
+        self.scene = scene
+        self.area_range = area_range      # 地图实际坐标范围 xmin, xmax, ymin, ymax
+        self.map = map                    # 缩放并离散化的地图 array(X,Y)
+        self.scale_ratio = scale_ratio    # 地图缩放率
+        self.step_length = step_length    # 步长(单次移动)
+        self.step_num = self.step_length // self.scale_ratio  # 单次移动地图格数
+        self.v = velocity  # 速度
+        self.react_radius = react_radius  # robot反应半径
+
+        self.planner = DStarLite(area_range=area_range, map=map, scale_ratio=scale_ratio)
+
+    def validate_goal(self, goal):
+        '''
+            目标合法化
+        '''
+        return self.planner.map2real(self.planner.real2map(goal))
+
+    @staticmethod
+    def is_reached(pos: (float, float), goal: (float, float), dis_limit=50):
+        '''
+            判断是否到达目标
+        '''
+        dis = math.hypot(pos[0]-goal[0], pos[1]-goal[1])
+        # dis = np.linalg.norm(pos - goal)
+        return dis < dis_limit
+
+    @staticmethod
+    def get_yaw(pos: (float, float), goal: (float, float)):
+        '''
+            得到移动方向
+        '''
+        # return math.degrees(math.atan2(goal[0] - pos[0], -(goal[1] - pos[1])))
+        return math.degrees(math.atan2((goal[1] - pos[1]), (goal[0] - pos[0])))
+
+    def navigate(self, goal: (float, float), animation=True):
+        '''
+            单次导航，直到到达目标
+        '''
+        goal = self.validate_goal(goal)  # 目标合法化
+        pos = (self.scene.status.location.X, self.scene.status.location.Y)  # 机器人当前: 位置 和 朝向
+        print('------------------navigation_start----------------------')
+        while not self.is_reached(pos, goal):
+            dyna_obs = [(walker.pose.X, walker.pose.Y) for walker in self.scene.status.walkers]  # 动态障碍物(顾客)位置列表
+            dyna_obs = [obs for obs in dyna_obs if euclidean_distance(obs, pos) < self.react_radius]  # 过滤观测范围外的dyna_obs
+            # 周围有dyna_obs则步长减半
+            if dyna_obs:
+                step_num = self.step_num // 2
+            else:
+                step_num = self.step_num
+            path = self.planner.planning(pos, goal, dyna_obs)
+            if path:
+                if animation:
+                    self.planner.draw_graph(step_num)  # 画出搜索路径
+                next_step = min(step_num, len(path))
+                next_pos = path[next_step - 1]
+                print('plan pos:', next_pos, end=' ')
+                yaw = self.get_yaw(pos, next_pos)
+                self.scene.walk_to(next_pos[0], next_pos[1], yaw, velocity=self.v, dis_limit=10)
+                self.planner.path = self.planner.path[next_step - 1:]  # 去除已走过的路径
+            pos = (self.scene.status.location.X, self.scene.status.location.Y)
+            print('reach pos:', pos)
+
+        self.planner.reset()  # 完成一轮导航，重置变量
+
+        if self.is_reached(pos, goal):
+            print('The robot has achieved goal !!')
+
+
+
+
+
+
+if __name__ == '__main__':
+
+    # 根据map计算并保存cost_map
+
+    file_name = 'map_4.pkl'
+    if os.path.exists(file_name):
+        with open(file_name, 'rb') as file:
+            map = pickle.load(file)
+
+    scene.init_world(1, 11)
+    scene = scene.Scene(sceneID=0)
+
+    navigator = Navigator(scene=scene, area_range=[-350, 600, -400, 1450], map=map, scale_ratio=4)
+
+    navigator.planner.compute_cost_map()
+
+    file_name = 'costMap_4.pkl'
+    if not os.path.exists(file_name):
+        open(file_name, 'w').close()
+    with open(file_name, 'wb') as file:
+        pickle.dump(navigator.planner.cost_map, file)
+    print('保存成功')

robowaiter/algos/navigator/readme.md

@@ -0,0 +1,128 @@
+# D_star Lite 机器人任务规划
+## 目录结构
+### 坐标离散化
+`discretize_map.py`
+
+### 地图文件(选择缩放倍率)
+`map_3.pkl`
+`map_4.pkl`
+`map_5.pkl`
+
+ ### 导航类
+`navigate.py`
+
+### D_star Lite 算法实现
+`dstar_lite.py`
+
+### 测试文件
+`test.py` 
+
+---
+
+## 世界地图
+
+### 实际坐标范围
+
+`X: -350 ~ 600`
+
+`Y: -400 ~ 1450`
+
+### 5倍缩放后坐标范围
+
+`X: -70 ~ 120`
+
+`Y: -80 ~ 290`
+
+### 网格地图
+
+| Idx | Obj              |
+|-----|------------------|
+| 0   | free             |
+| 1   | obstacle         |
+| 2   | dynamic obstacle |
+
+### 代价地图
+| Cost    | Obj             |
+|---------|-----------------|
+| 0       | free            |
+| 15-10-5 | obs周围3格         |
+| inf     | obstacle        |
+| 100     | dynamic obstacle |
+
+
+---
+
+
+## 参数
+`机器人步长`： 150
+
+`机器人速度`： 150
+
+`机器人观测范围`： 300
+
+`行人半径`： 36
+
+`目标判达距离`： 50
+
+`机器人移动dis_limit`： 10
+
+---
+
+## 使用方法
+```python
+# 选择缩放合适的地图：3、4、5
+file_name = 'map_4.pkl'
+if os.path.exists(file_name):
+    with open(file_name, 'rb') as file:
+        map = pickle.load(file)
+
+# 初始化场景
+scene.init_world(1, 11)
+scene = scene.Scene(sceneID=0)
+
+# 舒适化导航类
+# (需要传入：场景、实际地图范围、离散化地图、缩放比例)
+navigator = Navigator(scene=scene, area_range=[-350, 600, -400, 1450], map=map, scale_ratio=4)
+
+# 设置目标
+goal = (0, 0)
+
+# 导航
+# (animation: 选择是否画出导航过程)
+navigator.navigate(goal, animation=False)
+
+```
+
+
+---
+
+## 可靠性保证
+
+`目标合法性保证`：
+
+- 目标在地图外：重置目标为最近的地图内位置
+- 目标在静态障碍物：从当前位置不断向外圈扩展直到找到合法位置，重置目标为该位置
+
+`规划sbgoal合法性保证`：
+
+- 规划subgoal被动态障碍物占据：重新规划路径
+
+
+`起点合法性保证`：
+
+- 起点在静态障碍物：从当前位置不断向外圈扩展直到找到合法位置，重置起点为该位置
+- 起点在动态障碍物范围内：缩小动态障碍物半径，保证起点位置为空闲
+
+
+`机器人朝向保证`：
+
+- 机器人始终朝向每一步的移动方向
+
+
+`规划抖动解决`：
+
+- 规划路径不允许有重复点
+
+`避免机器人沿障碍物行走`：
+
+- 障碍物扩张：在代价地图`cost_map`中，静态障碍物周围的空位也会受到影响，并产生cost

robowaiter/algos/navigator/test.py

@@ -1,22 +1,19 @@
 import os
 import pickle
-import time
-import random
-import math
 
 import matplotlib.pyplot as plt
 import numpy as np
 
 from robowaiter.scene import scene
-# from navigate import Navigator
+from robowaiter.algos.navigator.navigate import Navigator
-from robowaiter.algos.navigate.navigate import Navigator
+
 
 
 
 if __name__ == '__main__':
-# def navigate_test(scene):
 
-    file_name = 'map_5.pkl'
+    # 选择缩放合适的地图：3、4、5
+    file_name = 'map_4.pkl'
     if os.path.exists(file_name):
         with open(file_name, 'rb') as file:
             map = pickle.load(file)
@@ -24,21 +21,25 @@ if __name__ == '__main__':
     scene.init_world(1, 11)
     scene = scene.Scene(sceneID=0)
 
-    navigator = Navigator(scene=scene, area_range=[-350, 600, -400, 1450], map=map)
+    navigator = Navigator(scene=scene, area_range=[-350, 600, -400, 1450], map=map, scale_ratio=4)
 
     '''场景1: 无行人环境 robot到达指定目标'''
-    # goal = np.array((-100, 700))
+    # goal = (-100, 700)
+    # goal = (590, 1370)
+    # goal = (290, -240)
+    # goal = (-200, -200)
+    # goal = (-200, -50)
 
     '''场景2: 静止行人环境 robot到达指定目标'''
     # scene.clean_walker()
     # scene.add_walker(50, 300, 0)
     # scene.add_walker(-50, 500, 0)
-    # goal = np.array((-100, 700))
+    # goal = (-100, 700)
 
     '''场景3: 移动行人环境 robot到达指定目标'''
-    scene.walk_to(100, 0, -90, dis_limit=10)
+    scene.walk_to(100, 0, -90, dis_limit=5)
     scene.clean_walker()
-    scene.add_walkers([[50, 300],[-50, 500],[0, 700]])
+    scene.add_walkers([[50, 300], [-50, 500], [0, 700]])
     # scene.add_walker(50, 300, 0)
     # scene.add_walker(-50, 500, 0)
     # scene.add_walker(0, 700, 0)
@@ -47,12 +48,13 @@ if __name__ == '__main__':
     scene.control_walker([scene.walker_control_generator(walkerID=2, autowalk=False, speed=50, X=0, Y=0, Yaw=0)])
 
     # goal = (-100, 700)
-    # goal = (-300)
+    # goal = (-200, 700)  # 目标在障碍物测试
-    # goal = (340.0, 900.0)
+    # goal = (-400, 700)  # 目标在地图外测试
+    goal = (10000, 10000)  # 目标在地图外测试
+    # goal = (-220, 300)
+    # goal = (-280, 400)
+    # goal = (-230, 600)
 
-    # goal = (240.0, 1000.0)
-    # goal = (340.0, 900.0)
-    goal = (240.0, 1160.0)
 
     '''场景4: 行人自由移动 robot到达指定目标'''
     # # TODO: autowalk=True仿真器会闪退 ???
@@ -62,7 +64,7 @@ if __name__ == '__main__':
     # scene.control_walker([scene.walker_control_generator(walkerID=0, autowalk=True, speed=20, X=0, Y=0, Yaw=0)])
     # scene.control_walker([scene.walker_control_generator(walkerID=1, autowalk=True, speed=20, X=0, Y=0, Yaw=0)])
     # time.sleep(5)
-    # goal = np.array((-100, 700))
+    # goal = (-100, 700)
 
     navigator.navigate(goal, animation=True)
 

robowaiter/behavior_lib/_base/Behavior.py

@@ -13,12 +13,12 @@ class Bahavior(ptree.behaviour.Behaviour):
         '''
     scene = None
     print_name_prefix = ""
-    all_place = {'Bar', 'Bar2', 'WaterTable', 'CoffeeTable', 'Table1', 'Table2', 'Table3'}
+    # all_place = {'Bar', 'Bar2', 'WaterTable', 'CoffeeTable', 'Table1', 'Table2', 'Table3'}
     # all_object = {'Coffee', 'Water', 'Dessert', 'Softdrink', 'BottledDrink', 'Yogurt', 'ADMilk', 'MilkDrink', 'Milk',
     #               'VacuumCup'}
-    # all_place = {'Bar', 'WaterTable', 'CoffeeTable'}
+    all_place = {'Bar', 'WaterTable', 'CoffeeTable'}
     # all_object = {'Coffee', 'Water', 'Dessert', 'Softdrink', 'Yogurt'}
-    all_object = {'Coffee'}
+    all_object = {'Coffee', 'Water'}
     place_xyz_dic={
         'Bar': (247.0, 520.0, 100.0),
         'Bar2': (240.0, 40.0, 70.0),
@@ -28,6 +28,13 @@ class Bahavior(ptree.behaviour.Behaviour):
         'Table2': (-55.0, 0.0, 107),
         'Table3':(-55.0, 150.0, 107)
     }
+    container_dic={
+        'Coffee':'CoffeeCup',
+        'Water': 'Glass',
+        'Dessert':'Plate'
+    }
+
+
     
     @classmethod
     def get_ins_name(cls,*args):

robowaiter/behavior_lib/act/Make.py

@@ -14,11 +14,11 @@ class Make(Act):
         super().__init__(*args)
         self.target_obj = self.args[0]
         self.op_type = 1
-        if self.target_obj=="Coffee":
+        if self.target_obj==self.valid_args[0]:
             self.op_type = 1
-        elif self.target_obj=="Water":
+        elif self.target_obj==self.valid_args[1]:
             self.op_type = 2
-        elif self.target_obj=="Dessert":
+        elif self.target_obj==self.valid_args[2]:
             self.op_type = 3
 
 
@@ -28,12 +28,12 @@ class Make(Act):
         info["pre"]= {f'Holding(Nothing)'}
         info['del_set'] = set()
         info['add'] = {f'Exist({arg})'}
-        if arg == "Coffee":
+        if arg == cls.valid_args[0]:
-            info["add"] |= {f'On(Coffee,CoffeeTable)'}
+            info["add"] |= {f'On({arg},CoffeeTable)'}
-        elif arg == "Water":
+        elif arg == cls.valid_args[1]:
-            info["add"] |= {f'On(Water,WaterTable)'}
+            info["add"] |= {f'On({arg},WaterTable)'}
-        elif arg == "Dessert":
+        elif arg == cls.valid_args[2]:
-            info["add"] |= {f'On(Dessert,Bar)'}
+            info["add"] |= {f'On({arg},Bar)'}
         return info
 
     def _update(self) -> ptree.common.Status:
@@ -43,16 +43,20 @@ class Make(Act):
 
         # self.scene.gen_obj(type=40)
 
-        obj_dict = self.scene.status.objects
+        # obj_dict = self.scene.status.objects
-        if len(obj_dict) != 0:
+        # if len(obj_dict) != 0:
-            # 获取obj_id
+        #     # 获取obj_id
-            for id, obj in enumerate(obj_dict):
+        #     for id, obj in enumerate(obj_dict):
-                if obj.name == "Coffee":
+        #         print("id:",id,"obj",obj.name)
-                    obj_info = obj_dict[id]
+
-                    obj_x, obj_y, obj_z = obj_info.location.X, obj_info.location.Y, obj_info.location.Z
+                # if obj.name == "Coffee":
-                    print(id,obj.name,obj_x,obj_y,obj_z)
+                #     obj_info = obj_dict[id]
+                #     obj_x, obj_y, obj_z = obj_info.location.X, obj_info.location.Y, obj_info.location.Z
+                #     print(id,obj.name,obj_x,obj_y,obj_z)
 
         self.scene.state["condition_set"] |= (self.info["add"])
         self.scene.state["condition_set"] -= self.info["del_set"]
 
+        # print("condition_set:",self.scene.state["condition_set"])
+
         return Status.RUNNING

robowaiter/behavior_lib/act/MoveTo.py

@@ -1,6 +1,6 @@
 import py_trees as ptree
 from robowaiter.behavior_lib._base.Act import Act
-from robowaiter.algos.navigate.navigate import Navigator
+from robowaiter.algos.navigator.navigate import Navigator
 
 class MoveTo(Act):
     can_be_expanded = True
@@ -21,6 +21,7 @@ class MoveTo(Act):
             info['pre'] |= {f'Exist({arg})'}
         info["add"] = {f'At(Robot,{arg})'}
         info["del_set"] = {f'At(Robot,{place})' for place in cls.valid_args if place != arg}
+        info['cost']=10
         return info
 
 
@@ -29,30 +30,39 @@ class MoveTo(Act):
 
         # navigator = Navigator(scene=self.scene, area_range=[-350, 600, -400, 1450], map=self.scene.state["map"]["2d"])
         # goal = self.scene.state['map']['obj_pos'][self.args[0]]
-        # navigator.navigate(goal, animation=False)
+        # navigator.navigate_old(goal, animation=False)
 
         # 走到固定的地点
         if self.target_place in Act.place_xyz_dic:
             goal = Act.place_xyz_dic[self.target_place]
-            self.scene.walk_to(goal[0],goal[1])
+            self.scene.walk_to(goal[0]+1,goal[1])
-        else: # 走到物品边上
+        # 走到物品边上
+        else:
+            # 是否用容器装好
+            if self.target_place in Act.container_dic:
+                target_name = Act.container_dic[self.target_place]
+            else:
+                target_name = self.target_place
+            # 根据物体名字找到最近的这类物体对应的位置
             obj_id = -1
             min_dis = float('inf')
             obj_dict = self.scene.status.objects
             if len(obj_dict)!=0:
                 # 获取obj_id
                 for id,obj in enumerate(obj_dict):
-                    if obj.name == self.target_place:
+                    if obj.name == target_name:
-                        obj_id = id
+                        obj_info = obj_dict[id]
-                        # obj_info = obj_dict[id]
+                        dis = self.scene.cal_distance_to_robot(obj_info.location.X, obj_info.location.Y, obj_info.location.Z)
-                        # obj_x, obj_y, obj_z = obj_info.location.X, obj_info.location.Y, obj_info.location.Z
+                        if dis<min_dis:
-                        # ginger_x,ginger_y,ginger_z = [int(self.scene.location.X), int(self.scene.location.Y), int(self.scene.rotation.Yaw)]
+                            min_dis = dis
-                        break
+                            obj_id = id
-            if self.target_place == "CoffeeCup":
+            # if self.target_place == "CoffeeCup":
-                obj_id = 273
+            #     # obj_id = 273
+            #     obj_id = 275
             if obj_id == -1:
                 return ptree.common.Status.FAILURE
 
+            # print("self.target_place:",self.target_place,"id:",obj_id,"dis:",min_dis)
             self.scene.move_to_obj(obj_id=obj_id)
 
             # 为了演示，写死咖啡位置

robowaiter/behavior_lib/act/PickUp.py

@@ -25,14 +25,36 @@ class PickUp(Act):
 
     def _update(self) -> ptree.common.Status:
         # self.scene.test_move()
-        op_type=16
+        # op_type=16
-        obj_id = 0
+
         # 遍历场景里的所有物品，根据名字匹配位置最近的 obj-id
+        # 是否用容器装好
+        if self.target_obj in Act.container_dic:
+            target_name = Act.container_dic[self.target_obj]
+        else:
+            target_name = self.target_obj
+        # 根据物体名字找到最近的这类物体对应的位置
+        obj_id = -1
+        min_dis = float('inf')
+        obj_dict = self.scene.status.objects
+        if len(obj_dict) != 0:
+            # 获取obj_id
+            for id, obj in enumerate(obj_dict):
+                if obj.name == target_name:
+                    obj_info = obj_dict[id]
+                    dis = self.scene.cal_distance_to_robot(obj_info.location.X, obj_info.location.Y,
+                                                           obj_info.location.Z)
+                    if dis < min_dis:
+                        min_dis = dis
+                        obj_id = id
+        # if self.target_place == "CoffeeCup":
+        #     # obj_id = 273
+        #     obj_id = 275
+        if obj_id == -1:
+            return ptree.common.Status.FAILURE
 
-        if self.args=="Coffee":
+        self.scene.move_task_area(op_type=16, obj_id=obj_id)
-            obj_id = 273
+        self.scene.op_task_execute(op_type=16, obj_id=obj_id)
-
-        self.scene.op_task_execute(op_type, obj_id=obj_id)
 
         self.scene.state["condition_set"] |= (self.info["add"])
         self.scene.state["condition_set"] -= self.info["del_set"]

robowaiter/behavior_lib/act/PutDown.py

@@ -22,6 +22,8 @@ class PutDown(Act):
         info["pre"] = {f'Holding({arg[0]})',f'At(Robot,{arg[1]})'}
         info["add"] = {f'Holding(Nothing)',f'On({arg[0]},{arg[1]})'}
         info["del_set"] = {f'Holding({arg[0]})'}
+
+        info['cost'] = 100
         return info
 
 
@@ -31,8 +33,11 @@ class PutDown(Act):
         release_pos = list(Act.place_xyz_dic[self.target_place])
         # # 原始吧台处:[247.0, 520.0, 100.0], 空调开关旁吧台:[240.0, 40.0, 70.0], 水杯桌:[-70.0, 500.0, 107]
         # # 桌子2:[-55.0, 0.0, 107],桌子3:[-55.0, 150.0, 107]
+        self.scene.move_task_area(op_type, release_pos=release_pos)
         self.scene.op_task_execute(op_type, release_pos=release_pos)
 
         self.scene.state["condition_set"] |= (self.info["add"])
         self.scene.state["condition_set"] -= self.info["del_set"]
+
+        print("After PutDown condition_set:",self.scene.state["condition_set"])
         return Status.RUNNING

robowaiter/behavior_lib/cond/On.py

@@ -16,6 +16,9 @@ class On(Cond):
     def _update(self) -> ptree.common.Status:
         # if self.scene.status?
 
+        # print("self.name:",self.name)
+        # print("On: condition_set:",self.scene.state["condition_set"])
+
         if self.name in self.scene.state["condition_set"]:
             return ptree.common.Status.SUCCESS
         else:

robowaiter/behavior_tree/obtea/OptimalBTExpansionAlgorithm.py

@@ -2,6 +2,8 @@ import copy
 import random
 from robowaiter.behavior_tree.obtea.BehaviorTree import Leaf,ControlBT
 
+
+
 class CondActPair:
     def __init__(self, cond_leaf,act_leaf):
         self.cond_leaf = cond_leaf
@@ -54,7 +56,10 @@ class OptBTExpAlgorithm:
         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):
+    def run_algorithm(self,goal,actions,scene):
+
+        self.scene = scene
+
         if self.verbose:
             print("\n算法开始！")
 
@@ -99,8 +104,13 @@ class OptBTExpAlgorithm:
                     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
+                    # 增加实时条件判断，满足条件就不再扩展
+                    # if c <= self.scene.state["condition_set"]:
+                    #     return True
                 else:
                     subtree.add_child([copy.deepcopy(pair_node.act_leaf)])
+
+
                 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,
@@ -128,14 +138,14 @@ class OptBTExpAlgorithm:
                                 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
+                            # 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,对应的c为 %s" % (actions[i].name,c_attr))
 
         if self.verbose:
             print("算法结束！\n")
@@ -178,13 +188,23 @@ class OptBTExpAlgorithm:
 
 
     # 树的dfs
-    def dfs_ptml(self,parnode):
+    def dfs_ptml(self,parnode,is_root=False):
         for child in parnode.children:
             if isinstance(child, Leaf):
                 if child.type == 'cond':
-                    self.ptml_string += "cond "
+
-                    c_set_str = '\n cond '.join(map(str, child.content)) + "\n"
+                    if is_root and len(child.content) > 1:
-                    self.ptml_string += c_set_str
+                        # 把多个 cond 串起来
+                        self.ptml_string += "sequence{\n"
+                        self.ptml_string += "cond "
+                        c_set_str = '\n cond '.join(map(str, child.content)) + "\n"
+                        self.ptml_string += c_set_str
+                        self.ptml_string += '}\n'
+                    else:
+                        self.ptml_string += "cond "
+                        c_set_str = '\n cond '.join(map(str, child.content)) + "\n"
+                        self.ptml_string += c_set_str
+
                 elif child.type == 'act':
                     if '(' not in child.content.name:
                         self.ptml_string += 'act ' + child.content.name + "()\n"
@@ -202,7 +222,7 @@ class OptBTExpAlgorithm:
 
     def get_ptml(self):
         self.ptml_string = "selector{\n"
-        self.dfs_ptml(self.bt.children[0])
+        self.dfs_ptml(self.bt.children[0],is_root=True)
         self.ptml_string += '}\n'
         return self.ptml_string
 

robowaiter/behavior_tree/obtea/opt_bt_exp_main.py

@@ -6,7 +6,7 @@ from robowaiter.behavior_tree.obtea.examples import *
 
 # 封装好的主接口
 class BTOptExpInterface:
-    def __init__(self, action_list):
+    def __init__(self, action_list,scene):
         """
         Initialize the BTOptExpansion with a list of actions.
         :param action_list: A list of actions to be used in the behavior tree.
@@ -22,6 +22,8 @@ class BTOptExpInterface:
         self.actions = action_list
         self.has_processed = False
 
+        self.scene = scene
+
     def process(self, goal):
         """
         Process the input sets and return a string result.
@@ -31,7 +33,7 @@ class BTOptExpInterface:
         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.algo.run_algorithm(self.goal, self.actions,self.scene) # 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

robowaiter/llm_client/data/test_questions.txt

@@ -1 +1 @@
-{"测试VLM：做一杯咖啡": {"Answer": "测试VLM：做一杯咖啡", "Goal": "{\"On(Coffee,CoffeeTable)\"}"}, "测试VLM：做一杯咖啡放到吧台上": {"Answer": "测试VLM：做一杯咖啡放到吧台上", "Goal": "{\"On(Coffee,Bar)\"}"}, "测试VLM：做一杯咖啡放到水杯桌上并倒水": {"Answer": "测试VLM：做一杯咖啡放到水杯桌上并倒水", "Goal": "{\"On(Coffee,WaterTable)\"}"}, "测试VLN：前往2号桌": {"Answer": "测试VLN：前往2号桌", "Goal": "{\"At(Robot,Table2)\"}"}, "测试AEM": {"Answer": "测试AEM", "Goal": "{\"EnvExplored()\"}"}, "测试VLM：倒一杯水": {"Answer": "测试VLM：倒一杯水", "Goal": "{\"On(Water,WaterTable)\"}"}, "测试VLM：开空调": {"Answer": "测试VLM：开空调", "Goal": "{\"Is(AC,On)\"}"}, "测试VLM：关空调": {"Answer": "测试VLM：关空调", "Goal": "{\"Is(AC,Off)\"}"}, "测试VLM：关大厅灯": {"Answer": "测试VLM：关大厅灯", "Goal": "{\"Is(HallLight,Off)\"}"}, "测试VLM：开大厅灯": {"Answer": "测试VLM：开大厅灯", "Goal": "{\"Is(HallLight,On)\"}"}, "测试VLM：关筒灯": {"Answer": "测试VLM：关筒灯", "Goal": "{\"Is(TubeLight,Off)\"}"}, "测试VLM：开筒灯": {"Answer": "测试VLM：开筒灯", "Goal": "{\"Is(TubeLight,On)\"}"}, "测试VLM：关窗帘": {"Answer": "测试VLM：关窗帘", "Goal": "{\"Is(Curtain,Off)\"}"}, "测试VLM：开窗帘": {"Answer": "测试VLM：开窗帘", "Goal": "{\"Is(Curtain,On)\"}"}, "测试VLM：拖地": {"Answer": "测试VLM：拖地", "Goal": "{\"Is(Floor,Clean)\"}"}, "测试VLM：擦桌子": {"Answer": "测试VLM：擦桌子", "Goal": "{\"Is(Table1,Clean)\"}"}, "测试VLM：整理椅子": {"Answer": "测试VLM：整理椅子", "Goal": "{\"Is(Chairs,Clean)\"}"}, "测试VLM：把冰红茶放到Table2": {"Answer": "测试VLM：把冰红茶放到Table2", "Goal": "{\"On(BottledDrink,Table2)\"}"}, "我有点热，能开个空调吗？": {"Answer": "当然可以，我现在就开！", "Goal": "{\"Is(AC,On)\"}"}, "可以带我去吗": {"Answer": "当然可以，前往一号桌", "Goal": "{\"At(Robot,Table1)\"}"}}
+{"测试VLM：做一杯咖啡": {"Answer": "测试VLM：做一杯咖啡", "Goal": "{\"On(Coffee,CoffeeTable)\"}"}, "测试VLM：做一杯咖啡放到吧台上": {"Answer": "测试VLM：做一杯咖啡放到吧台上", "Goal": "{\"On(Coffee,Bar)\"}"}, "测试VLM：做一杯咖啡放到水杯桌上，再倒一杯水": {"Answer": "测试VLM：做一杯咖啡放到水杯桌上，再倒一杯水", "Goal": "{\"On(Coffee,WaterTable)\",\"On(Water,WaterTable)\"}"}, "测试VLN：前往2号桌": {"Answer": "测试VLN：前往2号桌", "Goal": "{\"At(Robot,Table2)\"}"}, "测试AEM": {"Answer": "测试AEM", "Goal": "{\"EnvExplored()\"}"}, "测试VLM：倒一杯水": {"Answer": "测试VLM：倒一杯水", "Goal": "{\"On(Water,WaterTable)\"}"}, "测试VLM：开空调": {"Answer": "测试VLM：开空调", "Goal": "{\"Is(AC,On)\"}"}, "测试VLM：关空调": {"Answer": "测试VLM：关空调", "Goal": "{\"Is(AC,Off)\"}"}, "测试VLM：关大厅灯": {"Answer": "测试VLM：关大厅灯", "Goal": "{\"Is(HallLight,Off)\"}"}, "测试VLM：开大厅灯": {"Answer": "测试VLM：开大厅灯", "Goal": "{\"Is(HallLight,On)\"}"}, "测试VLM：关筒灯": {"Answer": "测试VLM：关筒灯", "Goal": "{\"Is(TubeLight,Off)\"}"}, "测试VLM：开筒灯": {"Answer": "测试VLM：开筒灯", "Goal": "{\"Is(TubeLight,On)\"}"}, "测试VLM：关窗帘": {"Answer": "测试VLM：关窗帘", "Goal": "{\"Is(Curtain,Off)\"}"}, "测试VLM：开窗帘": {"Answer": "测试VLM：开窗帘", "Goal": "{\"Is(Curtain,On)\"}"}, "测试VLM：拖地": {"Answer": "测试VLM：拖地", "Goal": "{\"Is(Floor,Clean)\"}"}, "测试VLM：擦桌子": {"Answer": "测试VLM：擦桌子", "Goal": "{\"Is(Table1,Clean)\"}"}, "测试VLM：整理椅子": {"Answer": "测试VLM：整理椅子", "Goal": "{\"Is(Chairs,Clean)\"}"}, "测试VLM：把冰红茶放到Table2": {"Answer": "测试VLM：把冰红茶放到Table2", "Goal": "{\"On(BottledDrink,Table2)\"}"}, "我有点热，能开个空调吗？": {"Answer": "当然可以，我现在就开！", "Goal": "{\"Is(AC,On)\"}"}, "可以带我去吗": {"Answer": "当然可以，前往一号桌", "Goal": "{\"At(Robot,Table1)\"}"}}

robowaiter/llm_client/data_raw/test_questions.csv

@@ -1,7 +1,7 @@
 Question,Answer,Goal
 测试VLM：做一杯咖啡,测试VLM：做一杯咖啡,"{""On(Coffee,CoffeeTable)""}"
 测试VLM：做一杯咖啡放到吧台上,测试VLM：做一杯咖啡放到吧台上,"{""On(Coffee,Bar)""}"
-测试VLM：做一杯咖啡放到水杯桌上并倒水,测试VLM：做一杯咖啡放到水杯桌上并倒水,"{""On(Coffee,WaterTable)""}"
+测试VLM：做一杯咖啡放到水杯桌上，再倒一杯水,测试VLM：做一杯咖啡放到水杯桌上，再倒一杯水,"{""On(Coffee,WaterTable)"",""On(Water,WaterTable)""}"
 测试VLN：前往2号桌,测试VLN：前往2号桌,"{""At(Robot,Table2)""}"
 测试AEM,测试AEM,"{""EnvExplored()""}"
 测试VLM：倒一杯水,测试VLM：倒一杯水,"{""On(Water,WaterTable)""}"

robowaiter/llm_client/data_tools/csv2dict.py

@@ -24,4 +24,3 @@ with open(csv_file_path, mode='r', encoding='gbk') as csv_file, \
     json_str = json.dumps(output_dict, ensure_ascii=False)
     # 将JSON字符串写入JSONL文件，并添加换行符
     jsonl_file.write(json_str + '\n')
-s

robowaiter/llm_client/test_llm/validate_data.txt

@@ -0,0 +1,40 @@
+你能到另一个吧台这边来吗？	create_sub_task	{At(Robot,Bar2)}	当然可以，我现在过去，请您稍等
+哈喽！服务员，在吧台那里借用下充电宝行不行?	create_sub_task	{At(Robot,Bar)}	没问题!我马上就过去.
+麻烦拿个空杯子过来。	create_sub_task	{At(Robot,WaterTable)}	没问题，马上给您拿一个空杯子过来。
+你好，我想点一杯咖啡。	create_sub_task	{At(Robot,Table3)}	当然，请稍等片刻，我马上为您准备。
+你好，请问有没有空位？	create_sub_task	{At(Robot,Table1)}	是的，我们还有空座位。请随意选择一个位置坐下即可。
+Hi! there! Could you bring me a milkdrink? Thankyou!	create_sub_task	{On(MilkDrink,CoffeeTable)}	Certainly! I will bring it to you right away.
+请将甜点和牛奶饮料放到2号桌上。	create_sub_task	{On(Dessert,Table2),On(MilkDrink,Table2)}	好的，甜点和牛奶饮料将马上送到2号桌上。
+请把AD钙奶放到咖啡台上。	create_sub_task	{On(ADMilk,CoffeeTable)}	好的，马上为您放置AD钙奶。
+请给我一瓶瓶装冰红茶，我在旁边的吧台这里。	create_sub_task	{On(BottledDrink,Bar2)}	好的，我会马上为您准备一瓶瓶装冰红茶。
+请给我来杯酸奶，在吧台处。	create_sub_task	{On(Yogurt,Bar)}	当然可以，请稍等片刻，我们将为您准备杯酸奶并放置于吧台处。
+可以打开大厅灯并且关掉筒灯吗?	create_sub_task	{Is(HallLight,On),Is(TubeLight,Off)}	当然,我会打开大厅的灯并关闭筒灯.
+请打开空调并调低温度。	create_sub_task	{Is(AC,On),Is(ACTemperature,Down)}	好的，空调已经打开，并且温度已经调低了。
+你好，能不能给我整理一下这边的椅子？	create_sub_task	{Is(Chairs,Clean)}	当然，请稍等片刻。
+可以帮我打开空调吗？这里太热了。	create_sub_task	{Is(AC,On)}	当然可以，我会为您打开空调并将温度降低到适宜的程度。
+这里太暗了，你能帮我打开筒灯吗？	create_sub_task	{Is(TubeLight,On)}	当然可以，请稍等片刻。马上为您打开筒灯。
+请给我一个酸奶。	create_sub_task	{Holding(Yogurt)}	好的，请稍等片刻，马上就会有酸奶送到您手中。
+我想要酸奶和牛奶饮料/真果粒	create_sub_task	{Holding(Yogurt),Holding(MilkDrink)}	当然可以。这是您的酸奶和牛奶饮料/真果粒。享用吧！
+抱歉打扰了，请问有什么推荐的甜点吗？	create_sub_task	{Holding(Nothing)}	当然！我们有很多美味的甜点供您选择。让我向您介绍一下...
+可以帮忙递给我那瓶瓶装冰红茶吗？	create_sub_task	{Holding(BottledDrink)}	没问题，这是您要的瓶装冰红茶。
+你能给我一个保温杯吗？	create_sub_task	{Holding(VacuumCup)}	当然，这是您的保温杯。
+只需要打扫地板就行了，在这里喝我的咖啡。	create_sub_task	{On(Coffee,Bar2),Is(Floor,Dirty)}	当然没问题！让我们先打扫地板再享受你的美味咖啡吧！
+在这个桌子上放置我的咖啡之前能帮忙把它清理下吗？	create_sub_task	{On(Coffee,Bar2),Is(Table1,Dirty)}	当然没问题！让我们先将桌子清洁干净再放置您的咖啡。
+你好!可以给我一个甜点吗?	create_sub_task	{On(Dessert,CoffeeTable)}	当然!我会为您准备一个甜点.
+你好，请给我来杯咖啡。可以从吧台那里拿过来吗？	create_sub_task	{On(Coffee,Bar)}	当然，请稍等片刻。让我为您准备一杯咖啡并放在吧台上供您享用。
+嘿，在吧台那里有甜点。麻烦拿一个过来。	create_sub_task	{On(Dessert,Bar)}	当然，请稍等片刻，我会为您取一个甜点过来。
+请问这里有没有牙膏?	get_env_info	牙膏，None	不好意思，我们是咖啡厅，暂时没有提供牙膏。如果您需要的话，可以去附近的超市购买。
+你们这里卫生纸用完了吗?我需要拿几张。	get_env_info	卫生纸，柜台	非常抱歉，目前我们暂时缺货。但是您可以在柜台处领取湿巾来代替使用。给您带来不便深感抱歉。
+我想要一杯Bernachon牛奶热巧克力,这里能做得到吧?	get_env_info	Bernachon牛奶热巧克力，咖啡机	非常抱歉，我们的咖啡机暂时不支持制作Bernachon牛奶热巧克力。但是您可以尝试其他口味的饮品或者选择其他喜欢的热巧克力。
+请问我可以用洗手间吗?	get_env_info	洗手间（卫生间）	卫生间门旁边的走廊上方指示牌上标注了“卫生间”字样。当然可以，请沿着卫生间门旁边的走廊前行即可看到指示牌。
+请问这里有面包卖吗?	get_env_info	面包，橱柜	是的，我们这里有面包出售。您可以去橱柜那边看看，选择自己喜欢口感和口味的面包哦！
+我要买蛋糕,能告诉我蛋糕柜在哪里吗?	get_env_info	蛋糕柜，吧台	当然可以！如果您想要购买蛋糕,我们的蛋糕柜就位于咖啡厅的吧台旁边.您只需要沿着吧台走一小段路,就能找到我们精心准备的美味甜点了.希望您享受这个愉快而甜蜜的时刻!
+请问冰箱在哪里?我需要存放我的食物。	get_env_info	冰箱，橱柜	非常抱歉，我们咖啡厅并没有提供冰箱来为客人存放食物。不过您可以将食物交给服务员，并告知他们需要保存的时间和要求，他们会为您做好安排。
+我想找一个舒适的沙发坐一下，能告诉我在哪里吗?	get_env_info	沙发，休闲区	当然可以！我们咖啡厅内设有专门供客人使用的舒适沙发，您可以前往咖啡厅中央附近的休闲区寻找。
+你们这里提供免费Wi-Fi服务吗?我需要上网工作。	get_env_info	无线网络，服务台	当然！我们提供免费Wi-Fi服务给所有顾客使用。最接近无线网络信号强度最高处是在服务台周围,如果您需要连接互联网进行工作或其他事情，请尽管前去。
+请问这里有没有香柜?	get_env_info	香柜	非常抱歉，我们这里暂时没有提供香柜服务。不过您可以在附近的商店中寻找哦。
+能告诉我今天特别推荐哪种咖啡吗？	—	—	今天特别推荐我们自家调配的手冲单品咖啡。每日精选不同产区豆子，在专业师傅细心烘焙后现场为您沏制出最佳风味。
+请问你们在什么时间段营业？	—	—	我们从早上8点开始营业到晚上10点结束。欢迎随时来店享用美味的咖啡和舒适环境。
+今天下午来得人很多,我需要等很久吗？	—	—	很抱歉给您带来不便。由于人流较多，可能会有一些等待时间。我们会尽快为您提供服务，请耐心稍候。
+请问你们还接受支付宝或微信支付吗？	—	—	是的，我们支持支付宝和微信支付两种方式。方便快捷地完成付款流程，并确保了安全性。
+请问附近是否有停车场？如果没有，那么最近哪个停车场离得比较近？	—	—	很抱歉，在咖啡馆周围暂时没有专门停车场。但距离不远处就有一个大型商业中心停车场可供选择。

robowaiter/llm_client/test_llm/validate_llm.py

@@ -0,0 +1,19 @@
+import os
+import sys
+from tqdm import tqdm
+
+sys.path.append(os.path.join('../../llm_client'))
+
+from tool_api import run_conversation
+from tool_register import get_tools
+
+functions = get_tools()
+
+with open(os.path.join('./validate_data.txt'), 'r', encoding='utf-8') as file:
+    lines = file.readlines()
+    lines = [line[:-1].split('\t') for line in lines]
+
+with open(os.path.join('./validate_llm_1.txt'), 'w', encoding='utf-8') as file:
+    for line in tqdm(lines):
+        query = line[0]
+        file.write(str(run_conversation(query=query, stream=False)) + '\n')

robowaiter/llm_client/test_llm/validate_llm_1.txt

@@ -0,0 +1,40 @@
+{'Answer': '\n 好的，请问您需要我帮忙做什么呢？', 'Goal': None}
+{'Answer': '好的', 'Goal': 'At(ChargingPower,Bar)'}
+{'Answer': '\n 好的，请告诉我您需要把杯子放在哪里？', 'Goal': None}
+{'Answer': '\n 好的，我可以帮你点一杯咖啡。请告诉我你想要什么口味的咖啡？', 'Goal': None}
+{'Answer': '\n 好的，请问您想坐在哪一张桌子呢？', 'Goal': None}
+{'Answer': "\n Sure! I'll get you a milk drink. What size would you like?<|assistant|> \n How about a large one?", 'Goal': None}
+{'Answer': '好的', 'Goal': 'At(Dessert, Table2)'}
+{'Answer': '好的', 'Goal': 'At(Coffee, Stage)'}
+{'Answer': '\n 根据您的要求，我已经在旁边找到了一瓶瓶装冰红茶，放在了对应的桌子上。请您享用！', 'Goal': None}
+{'Answer': '好的', 'Goal': 'At(Bar,Yogurt)'}
+{'Answer': '\n 当前场景中没有找到名称为“HallLight”的物体。', 'Goal': None}
+{'Answer': '好的', 'Goal': 'Is(AC,On)'}
+{'Answer': '\n 当然可以，请问你需要整理多少把椅子呢？', 'Goal': None}
+{'Answer': '\n 好的，我可以帮你打开空调。请问你在哪个房间？', 'Goal': None}
+{'Answer': '\n 好的，我会为您打开筒灯。请告诉我筒灯在哪里。', 'Goal': None}
+{'Answer': '好的', 'Goal': 'Have(yogurt,1)'}
+{'Answer': '好的', 'Goal': 'Have Yogurt and Milk Beverage with True Fruit Grape'}
+{'Answer': '\n 你好，我可以帮你推荐一些甜点。请问你喜欢的口味是什么？', 'Goal': None}
+{'Answer': '\n 很抱歉，我无法找到您要求的瓶装冰红茶。', 'Goal': None}
+{'Answer': '\n 好的，请告诉我您的位置，我将为您送过去。', 'Goal': None}
+{'Answer': '好的', 'Goal': 'Is(Floor,Clean)'}
+{'Answer': '好的', 'Goal': 'At(Coordinates,OneTable)'}
+{'Answer': '\n 当然可以，请问您想要什么类型的甜点呢？', 'Goal': None}
+{'Answer': '\n 当然可以，您想要什么口味的咖啡呢？', 'Goal': None}
+{'Answer': '\n 好的，请告诉我您想要哪个口味的甜点呢？', 'Goal': None}
+{'Answer': '\n 这里的牙膏放在了桌子的抽屉里。', 'Goal': None}
+{'Answer': '\n 好的，请问你需要多少张卫生纸呢？', 'Goal': None}
+{'Answer': '好的', 'Goal': 'At(Bar,Bernachon牛奶热巧克力)'}
+{'Answer': '\n 您不能使用洗手间。', 'Goal': None}
+{'Answer': '\n 很抱歉，根据您的查询，我们无法找到任何面包。', 'Goal': None}
+{'Answer': '\n 很抱歉，我无法找到蛋糕柜的位置。', 'Goal': None}
+{'Answer': '\n 您可以到厨房里找一下冰箱，一般来说它都会放在 cabinets 和 countertop 之间。', 'Goal': None}
+{'Answer': '\n 很抱歉，我无法找到舒适的沙发。', 'Goal': None}
+{'Answer': '\n 你好，是的，我们这里提供免费的Wi-Fi服务。请问你需要连接吗？', 'Goal': None}
+{'Answer': '\n 根据您的询问，我查询了当前场景中的物品信息，但是没有找到香柜的存在。', 'Goal': None}
+{'Answer': '\n 你好，我可以帮你推荐一些咖啡。请问你想要什么口味的咖啡呢？', 'Goal': None}
+{'Answer': '\n 根据我所了解到的信息，这家商店的营业时间没有具体的规定。不过，通常来说，商店的营业时间一般是在早上8点至晚上8点之间。如果您需要了解更多详细的信息，建议您直接联系商店的管理人员。', 'Goal': None}
+{'Answer': '\n 根据当前情况，您可能需要等待一段时间。', 'Goal': None}
+{'Answer': '\n 我目前没有收微信或支付宝的功能。不过我们这里支持的是现金支付和银行卡支付。', 'Goal': None}
+{'Answer': '\n 很抱歉，我无法找到附近的停车场和最近的一个停车场。建议您使用地图应用或询问周围的居民来获取相关信息。', 'Goal': None}

robowaiter/llm_client/tool_api.py

@@ -1,6 +1,6 @@
 import json
+import re
 
-import openai
 from colorama import init, Fore
 from loguru import logger
 import json
@@ -9,6 +9,7 @@ import requests
 import json
 
 import urllib3
+
 init(autoreset=True)
 
 ########################################
@@ -18,7 +19,8 @@ init(autoreset=True)
 # 忽略https的安全性警告
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
-base_url = "https://45.125.46.134:25344" # 本地部署的地址,或者使用你访问模型的API地址
+base_url = "https://45.125.46.134:25344"  # 本地部署的地址,或者使用你访问模型的API地址
+
 
 def get_response(**kwargs):
     data = kwargs
@@ -27,12 +29,14 @@ def get_response(**kwargs):
     decoded_line = response.json()
     return decoded_line
 
+
 functions = get_tools()
 
-def run_conversation(query: str, stream=False,  max_retry=5):
+
+def run_conversation(query: str, stream=False, max_retry=5):
     params = dict(model="chatglm3", messages=[{"role": "user", "content": query}], stream=stream)
     params["functions"] = functions
-    print(params)
+    # print(params)
     response = get_response(**params)
 
     for _ in range(max_retry):
@@ -69,7 +73,42 @@ def run_conversation(query: str, stream=False,  max_retry=5):
         response = get_response(**params)
 
 
+def run_conversation_for_test_only(query: str, stream=False, max_retry=5):
+    params = dict(model="chatglm3", messages=[{"role": "user", "content": query}], stream=stream)
+    params["functions"] = functions
+    response = get_response(**params)
+
+    response_string = ''
+
+    for _ in range(max_retry):
+        if response["choices"][0]["message"].get("function_call"):
+            function_call = response["choices"][0]["message"]["function_call"]
+            response_string += f"Function Call: {function_call} \t"
+
+            function_args = json.loads(function_call["arguments"])
+            if function_call["name"]:
+                tool_response = dispatch_tool(function_call["name"], function_args)
+                response_string += f"Tool Call: %s \t" % (re.sub(r'\n', '', tool_response))
+            else:
+                response_string += f"LLM Cannot find the function call."
+
+            params["messages"].append(response["choices"][0]["message"])
+            params["messages"].append(
+                {
+                    "role": "function",
+                    "name": function_call["name"],
+                    "content": tool_response,  # 调用函数返回结果
+                }
+            )
+            response = get_response(**params)['choices'][0]
+            return response_string + "\tResponse: " + str(response)
+        else:
+            reply = response["choices"][0]["message"]["content"]
+            response_string += f"Final Reply: %s" % (re.sub(r'\n', '', reply))
+            response = get_response(**params)['choices'][0]
+            return response_string + "\tResponse: " + str(response)
+
 
 if __name__ == "__main__":
     query = "可以带我去吗"
-    print(run_conversation(query, stream=False))
+    print(run_conversation_for_test_only(query, stream=False))

robowaiter/proto/camera.py

@@ -240,7 +240,7 @@ def get_semantic_map(camera, cur_objs, objs_name):
     scene = Observe(0)
     objs = scene.objects
     img_data = get_camera([camera])
-    show_image(img_data, scene)
+    # show_image(img_data, scene)
     objs_name = save_obj_info(img_data, objs_name)
     for obj_name in list(objs_name):
         for obj in objs:

robowaiter/robot/robot.py

@@ -62,7 +62,7 @@ class Robot(object):
             print("--------------------\n")
 
 
-        algo = BTOptExpInterface(self.action_list)
+        algo = BTOptExpInterface(self.action_list,self.scene)
 
         ptml_string = algo.process(goal)
 

robowaiter/scene/scene.py

@@ -1,3 +1,4 @@
+import sys
 import time
 import grpc
 import numpy as np
@@ -108,6 +109,7 @@ class Scene:
         self.visited = set()
         self.all_frontier_list = set()
         self.semantic_map = semantic_map
+        self.auto_map = np.ones((800, 1550))
 
 
     def reset(self):
@@ -410,8 +412,12 @@ class Scene:
             walk_v = [obj_x + 40, obj_y - 35, 130, 180, 0]
             obj_x += 3
             obj_y += 2.5
+        walk_v[0]+=1
+        print("walk:",walk_v)
         action = GrabSim_pb2.Action(scene=self.sceneID, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v)
         scene = stub.Do(action)
+        print("After Walk Position:", [scene.location.X, scene.location.Y, scene.rotation.Yaw])
+
 
     # 移动到进行操作任务的指定地点
     def move_task_area(self,op_type,obj_id=0, release_pos=[247.0, 520.0, 100.0]):
@@ -441,7 +447,7 @@ class Scene:
             walk_v = release_pos[:-1] + [180, 180, 0]
             if release_pos == [340.0, 900.0, 99.0]:
                 walk_v[2] = 130
-
+        print("walk_v:",walk_v)
         action = GrabSim_pb2.Action(scene=self.sceneID, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v)
         scene = stub.Do(action)
         print("After Walk Position:", [scene.location.X, scene.location.Y, scene.rotation.Yaw])
@@ -540,7 +546,7 @@ class Scene:
         return True
 
     # 执行过程：输出"开始(任务名)" -> 按步骤数执行任务 -> Robot输出成功或失败的对话
-    def op_task_execute(self,op_type,obj_id=0,release_pos=[240,-140]):
+    def op_task_execute(self,op_type,obj_id=0,release_pos=[247.0, 520.0, 100.0]):
         self.control_robot_action(0, 1, "开始"+self.op_dialog[op_type])   # 开始制作咖啡
         if op_type<8: result = self.control_robot_action(op_type, 1)
         if op_type>=8 and op_type<=12: result = self.control_robot_action(self.op_typeToAct[op_type][0], self.op_typeToAct[op_type][1])
@@ -587,7 +593,7 @@ class Scene:
         #     v_list = [[0.0, 0.0]]
 
         for walk_v in v_list:
-            walk_v = walk_v + [scene.rotation.Yaw - 90, 250, 60]
+            walk_v = walk_v + [scene.rotation.Yaw - 90, 250, 10]
             print("walk_v", walk_v)
             action = GrabSim_pb2.Action(scene=scene_id, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v)
             scene = stub.Do(action)
@@ -596,7 +602,7 @@ class Scene:
             print(scene.info)
         return cur_objs, objs_name_set
 
-    def isOutMap(self, pos, min_x=-350, max_x=600, min_y=-400, max_y=1450):
+    def isOutMap(self, pos, min_x=-200, max_x=600, min_y=-250, max_y=1300):
         if pos[0] <= min_x or pos[0] >= max_x or pos[1] <= min_y or pos[1] >= max_y:
             return True
         return False
@@ -607,11 +613,13 @@ class Scene:
         '''
         # x = round((x - self.min_x) / self.scale_ratio)
         # y = round((y - self.min_y) / self.scale_ratio)
-        x = math.floor((x + 350) / 5)
+        x = math.floor((x + 200))
-        y = math.floor((y + 400) / 5)
+        y = math.floor((y + 250))
         return x, y
 
-    def explore(self, map, cur_pos, explore_range):
+    def explore(self, map, explore_range):
+        scene = stub.Observe(GrabSim_pb2.SceneID(value=0))
+        cur_pos = [int(scene.location.X), int(scene.location.Y)]
         for i in range(cur_pos[0] - explore_range, cur_pos[0] + explore_range + 1):
             for j in range(cur_pos[1] - explore_range, cur_pos[1] + explore_range + 1):
                 if self.isOutMap((i, j)):
@@ -619,6 +627,7 @@ class Scene:
                 x, y = self.real2map(i, j)
                 if map[x, y] == 0:
                     self.visited.add((i, j))
+                    self.auto_map[x][y] = 0
         for i in range(cur_pos[0] - explore_range, cur_pos[0] + explore_range + 1):
             for j in range(cur_pos[1] - explore_range, cur_pos[1] + explore_range + 1):
                 if self.isOutMap((i, j)):
@@ -627,11 +636,26 @@ class Scene:
                 if map[x, y] == 0:
                     if self.isNewFrontier((i, j), map):
                         self.all_frontier_list.add((i, j))
-        if len(self.all_frontier_list) <= 400:
+        if len(self.all_frontier_list) == 0:
             free_list = list(self.visited)
             free_array = np.array(free_list)
-            print(f"探索完成！以下是场景中可以到达的点：{free_array}；其余点均是障碍物不可达")
+            print(f"主动探索完成！以下是场景中可以到达的点：{free_array}；其余点均是障碍物不可达")
+
+            # 画地图: X行Y列，第一行在下面
+            plt.clf()
+            plt.imshow(self.auto_map, cmap='binary', alpha=0.5, origin='lower',
+                       extent=(-250, 1300,
+                               -200, 600))
+            plt.show()
+            print("已绘制完成地图！！！")
+
             return None
+        # 画地图: X行Y列，第一行在下面
+        plt.imshow(self.auto_map, cmap='binary', alpha=0.5, origin='lower',
+                   extent=(-250, 1300,
+                           -200, 600))
+        plt.show()
+        print("已绘制部分地图！")
         return self.getNearestFrontier(cur_pos, self.all_frontier_list)
 
     def isNewFrontier(self, pos, map):
@@ -639,10 +663,37 @@ class Scene:
 
         for node in around_nodes:
             x, y = self.real2map(node[0], node[1])
-            if node not in self.visited and map[x, y] == 0:
+            if not self.isOutMap((node[0], node[1])) and node not in self.visited and map[x, y] == 0:
                 return True
         if (pos[0], pos[1]) in self.all_frontier_list:
             self.all_frontier_list.remove((pos[0], pos[1]))
         return False
 
+    def getDistance(self, pos1, pos2):
+        return math.sqrt((pos1[0] - pos2[0]) ** 2 + (pos1[1] - pos2[1]) ** 2)
 
+    def getNearestFrontier(self, cur_pos, frontiers):
+        dis_min = sys.maxsize
+        frontier_best = None
+        for frontier in frontiers:
+            dis = self.getDistance(frontier, cur_pos)
+            if dis <= dis_min:
+                dis_min = dis
+                frontier_best = frontier
+        return frontier_best
+
+
+
+    def cal_distance_to_robot(self,objx,objy,objz):
+        scene = self.status
+        ginger_x, ginger_y, ginger_z = [int(scene.location.X), int(scene.location.Y),100]
+        return math.sqrt((ginger_x - objx) ** 2 + (ginger_y - objy) ** 2 + (ginger_z - objz) ** 2)
+
+    # def test_yaw(self):
+    #     walk_v = [247.0, 480.0, 180.0, 180, 0]
+    #     action = GrabSim_pb2.Action(scene=self.sceneID, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v)
+    #     scene = stub.Do(action)
+    #     time.sleep(4)
+    #     walk_v = [247.0, 500.0, 0.0, 180, 0]
+    #     action = GrabSim_pb2.Action(scene=self.sceneID, action=GrabSim_pb2.Action.ActionType.WalkTo, values=walk_v)
+    #     scene = stub.Do(action)

robowaiter/scene/tasks/AEM.py

@@ -2,6 +2,7 @@
 环境主动探索和记忆
 要求输出探索结果（语义地图）对环境重点信息记忆。生成环境的语义拓扑地图，和不少于10个环境物品的识别和位置记忆，可以是图片或者文字或者格式化数据。
 """
+import pickle
 
 from robowaiter.scene.scene import Scene
 class SceneAEM(Scene):
@@ -12,9 +13,25 @@ class SceneAEM(Scene):
         pass
     def _run(self):
         cur_objs = []
+        objs_name_set = set()
+        file_name = '../../proto/map_1.pkl'
+        if os.path.exists(file_name):
+            with open(file_name, 'rb') as file:
+                map = pickle.load(file)
         print('------------ 自主探索 ------------')
-        cur_objs = self.semantic_map.navigation_move(cur_objs, 0, 11)
+        # cur_objs = self.semantic_map.navigation_move(cur_objs, 0, 11)
-        print("物品列表如下：")
+        # print("物品列表如下：")
+        # print(cur_objs)
+        # cur_pos = [int(scene.location.X), int(scene.location.Y)]
+        # print(reachable([237,490]))
+        # navigation_move([[237,490]], i, map_id)
+        # navigation_test(i,map_id)
+        while True:
+            goal = self.explore(map, 120)  # cur_pos 指的是当前机器人的位置，场景中应该也有接口可以获取
+            if goal is None:
+                break
+            cur_objs, objs_name_set = self.navigation_move(cur_objs, objs_name_set, [[goal[0], goal[1]]], 0, 11)
+        print("------------物品信息--------------")
         print(cur_objs)
 
         pass

robowaiter/scene/tasks/VLM/VLM.py

@@ -22,7 +22,10 @@ class SceneVLM(Scene):
             # (5, self.create_chat_event("测试VLM：把冰红茶放到Table2")),
             # (5, self.create_chat_event("测试VLM：关大厅灯"))
             # (5, self.create_chat_event("测试VLM：做一杯咖啡放到吧台上")),
-            (5, self.create_chat_event("测试VLM：做一杯咖啡放到水杯桌上并倒水")),
+            (5, self.create_chat_event("测试VLM：做一杯咖啡放到水杯桌上，再倒一杯水")),
+            (10, self.create_chat_event("测试VLM：关窗帘")),
+            # (5, self.create_chat_event("测试VLN：前往2号桌")),
+
         ]
 
     def _reset(self):
@@ -35,6 +38,7 @@ class SceneVLM(Scene):
 
     def _run(self, op_type=10):
 
+
         # 共17个操作
         # "制作咖啡","倒水","夹点心","拖地","擦桌子","开筒灯","搬椅子",    # 1-7
         # "关筒灯","开大厅灯","关大厅灯","关闭窗帘","打开窗帘",            # 8-12
@@ -58,27 +62,28 @@ class SceneVLM(Scene):
         # 流程测试
         # 抓握放置:抓吧台前生成的酸奶，放到抹布桌上
         self.gen_obj()
-        self.move_task_area(16, obj_id=0)
+        # self.move_task_area(16, obj_id=0)
-        self.op_task_execute(16, obj_id=0)
+        # self.op_task_execute(16, obj_id=0)
-        pos = [340.0, 900.0, 99.0]
+        # pos = [340.0, 900.0, 99.0]
-        self.move_task_area(17, release_pos=pos)
+        # self.move_task_area(17, release_pos=pos)
-        self.op_task_execute(17, release_pos=pos)
+        # self.op_task_execute(17, release_pos=pos)
+        #
+        # # 做咖啡:做完的咖啡放到水杯桌上
+        # self.move_task_area(1)
+        # self.op_task_execute(1)
+        #
+        # self.find_obj("CoffeeCup")
+        #
+        # self.move_task_area(16, obj_id=275)
+        # self.op_task_execute(16, obj_id=275)
+        # pos = [-70.0, 500.0, 107]
+        # self.move_task_area(17, release_pos=pos)
+        # self.op_task_execute(17, release_pos=pos)
+        #
+        # # 倒水:倒完的水放到旁边桌子上
+        # self.move_task_area(2)
+        # self.op_task_execute(2)
 
-        # 做咖啡:做完的咖啡放到水杯桌上
-        self.move_task_area(1)
-        self.op_task_execute(1)
-
-        self.find_obj("CoffeeCup")
-
-        self.move_task_area(16, obj_id=275)
-        self.op_task_execute(16, obj_id=275)
-        pos = [-70.0, 500.0, 107]
-        self.move_task_area(17, release_pos=pos)
-        self.op_task_execute(17, release_pos=pos)
-
-        # 倒水:倒完的水放到旁边桌子上
-        self.move_task_area(2)
-        self.op_task_execute(2)
         #
         # self.move_task_area(16, obj_id=190)
         # self.op_task_execute(16, obj_id=190)
@@ -86,6 +91,8 @@ class SceneVLM(Scene):
         # self.move_task_area(17, release_pos=pos)
         # self.op_task_execute(17, release_pos=pos)
 
+        # self.test_yaw()
+
         pass
     
     def _step(self):

robowaiter/scene/tasks/VLM/VLM2.py

@@ -0,0 +1,123 @@
+"""
+视觉语言操作
+机器人根据指令人的指令调节空调，自主探索环境导航到目标点，通过手臂的运动规划能力操作空调，比如开关按钮、调温按钮、显示面板
+"""
+
+import time
+from robowaiter.scene.scene import Scene
+
+class SceneVLM(Scene):
+    def __init__(self, robot):
+        super().__init__(robot)
+        # 在这里加入场景中发生的事件， (事件发生的时间，事件函数)
+        self.event_list = [
+            # (5, self.create_chat_event("测试VLM：做一杯咖啡")),
+            # (5, self.create_chat_event("测试VLM：倒一杯水")),
+            # (5, self.create_chat_event("测试VLM：开空调")),
+            # (5, self.create_chat_event("测试VLM：关空调")),
+            # (5, self.create_chat_event("测试VLM：开大厅灯")),
+            # (5, self.create_chat_event("测试VLM：拖地")),
+            # (7, self.create_chat_event("测试VLM：擦桌子")),
+            # (5, self.create_chat_event("测试VLM：整理椅子")),
+            # (5, self.create_chat_event("测试VLM：把冰红茶放到Table2")),
+            # (5, self.create_chat_event("测试VLM：关大厅灯"))
+            # (5, self.create_chat_event("测试VLM：做一杯咖啡放到吧台上")),
+            # (5, self.create_chat_event("测试VLM：做一杯咖啡放到水杯桌上并倒水")),
+            # (8, self.create_chat_event("测试VLN：前往1号桌")),
+
+        ]
+
+    def _reset(self):
+
+        # self.gen_obj(type=5)
+        # self.gen_obj(type=9)
+        # self.op_task_execute(op_type=16, obj_id=0)
+        # self.move_task_area(op_type=4)
+        pass
+
+    def _run(self, op_type=10):
+        # 一个行人从门口走到 吧台
+        # 打招呼需要什么
+        # 行人说 哪里有位置，想晒个太阳
+        # 带领行人去有太阳的地方
+        # 行人说 有点热
+        # 好的，这就去开空调
+
+        scene = self.add_walkers([[0, 0]])
+        self.control_walker(
+            [self.walker_control_generator(walkerID=1, autowalk=False, speed=50, X=100, Y=150, Yaw=0)])
+
+        cont = scene.walkers[0].name+":我有点热，能开个空调吗？"
+        self.control_robot_action(0,3,cont)
+
+
+        # 共17个操作
+        # "制作咖啡","倒水","夹点心","拖地","擦桌子","开筒灯","搬椅子",    # 1-7
+        # "关筒灯","开大厅灯","关大厅灯","关闭窗帘","打开窗帘",            # 8-12
+        # "调整空调开关","调高空调温度","调低空调温度",                   # 13-15
+        # "抓握物体","放置物体"                                       # 16-17
+
+        # self.gen_obj()
+        # if op_type <=15:
+        #     self.move_task_area(op_type)
+        #     self.op_task_execute(op_type)
+        # if op_type == 16:   # 16: 抓操作需要传入物品id
+        #     self.move_task_area(op_type, obj_id=0)
+        #     self.op_task_execute(op_type, obj_id=0)
+        # # 原始吧台处:[247.0, 520.0, 100.0], 空调开关旁吧台:[240.0, 40.0, 100.0], 水杯桌:[-70.0, 500.0, 107]
+        # # 桌子1:[-55.0, 0.0, 107],抹布桌:[340.0, 900.0, 99.0]   # 桌子2:[-55.0, 150.0, 107],
+        # if op_type == 17:   # 17: 放操作需要传入放置位置周围的可达区域
+        #     pos = [240.0, 40.0, 100.0]
+        #     self.move_task_area(op_type, release_pos=pos)
+        #     self.op_task_execute(op_type, release_pos=pos)   # [325.0, 860.0, 100]
+
+        # 流程测试
+        # 抓握放置:抓吧台前生成的酸奶，放到抹布桌上
+        self.gen_obj()
+        # self.move_task_area(16, obj_id=0)
+        # self.op_task_execute(16, obj_id=0)
+        # pos = [340.0, 900.0, 99.0]
+        # self.move_task_area(17, release_pos=pos)
+        # self.op_task_execute(17, release_pos=pos)
+        #
+        # # 做咖啡:做完的咖啡放到水杯桌上
+        # self.move_task_area(1)
+        # self.op_task_execute(1)
+        #
+        # self.find_obj("CoffeeCup")
+        #
+        # self.move_task_area(16, obj_id=275)
+        # self.op_task_execute(16, obj_id=275)
+        # pos = [-70.0, 500.0, 107]
+        # self.move_task_area(17, release_pos=pos)
+        # self.op_task_execute(17, release_pos=pos)
+        #
+        # # 倒水:倒完的水放到旁边桌子上
+        # self.move_task_area(2)
+        # self.op_task_execute(2)
+
+        #
+        # self.move_task_area(16, obj_id=190)
+        # self.op_task_execute(16, obj_id=190)
+        # pos = [-55.0, 0.0, 107]
+        # self.move_task_area(17, release_pos=pos)
+        # self.op_task_execute(17, release_pos=pos)
+
+        # self.test_yaw()
+
+        pass
+    
+    def _step(self):
+        pass
+
+
+if __name__ == '__main__':
+    import os
+    from robowaiter.robot.robot import Robot
+
+    robot = Robot()
+
+    # create task
+    task = SceneVLM(robot)
+    task.reset()
+    task.run()

robowaiter/scene/tasks/VLN.py

@@ -16,8 +16,8 @@ from robowaiter.scene.scene import Scene,init_world  # TODO: 文件名改成Scen
 
 from robowaiter.scene.scene import Scene
 from robowaiter.utils import get_root_path
-from robowaiter.algos.navigate.navigate import Navigator
+from robowaiter.algos.navigator.navigate import Navigator
-from robowaiter.algos.navigate import test
+from robowaiter.algos.navigator import test
 
 class SceneVLN(Scene):
     def __init__(self, robot):
@@ -29,7 +29,7 @@ class SceneVLN(Scene):
 
     def _reset(self):
         root_path = get_root_path()
-        file_name = os.path.join(root_path,'robowaiter/algos/navigate/map_5.pkl')
+        file_name = os.path.join(root_path,'robowaiter/algos/navigator/map_5.pkl')
         with open(file_name, 'rb') as file:
             map = pickle.load(file)
 
@@ -37,7 +37,7 @@ class SceneVLN(Scene):
         self.state['map']['obj_pos']['Table'] = np.array((-100, 700))
 
     def _run(self):
-        file_name = '../../algos/navigate/map_5.pkl'
+        file_name = '../../algos/navigator/map_5.pkl'
         if os.path.exists(file_name):
             with open(file_name, 'rb') as file:
                 map = pickle.load(file)

sub_task.ptml

@@ -1,5 +1,10 @@
 selector{
-cond On(Coffee,CoffeeTable)
+
+sequence{
+    cond On(Coffee,CoffeeTable)
+    cond On(Coffee,CoffeeTable)
+    }
+
 sequence{
 cond Holding(Nothing)
 act Make(Coffee)