50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
"""
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环境主动探索和记忆
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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。
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"""
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import pickle
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from robowaiter.scene.scene import Scene
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class SceneAEM(Scene):
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def __init__(self, robot):
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super().__init__(robot)
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def _reset(self):
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pass
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def _run(self):
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cur_objs = []
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objs_name_set = set()
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file_name = '../../proto/map_1.pkl'
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if os.path.exists(file_name):
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with open(file_name, 'rb') as file:
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map = pickle.load(file)
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print('------------ 自主探索 ------------')
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# cur_objs = self.semantic_map.navigation_move(cur_objs, 0, 11)
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# print("物品列表如下：")
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# print(cur_objs)
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# cur_pos = [int(scene.location.X), int(scene.location.Y)]
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# print(reachable([237,490]))
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# navigation_move([[237,490]], i, map_id)
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# navigation_test(i,map_id)
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while True:
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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
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if goal is None:
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break
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cur_objs, objs_name_set = self.navigation_move(cur_objs, objs_name_set, [[goal[0], goal[1]]], 0, 11)
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print("------------物品信息--------------")
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print(cur_objs)
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pass
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if __name__ == '__main__':
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import os
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from robowaiter.robot.robot import Robot
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robot = Robot()
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# create task
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task = SceneAEM(robot)
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task.reset()
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task.run()
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