In this study, the goal is to efficiently and actively search for a target object in a previously unknown large-scale environment. To this end, we develop a probabilistic environment model that can utilize spatial commonsense knowledge and environment-specific spatial relations. The model evaluates the merit of exploring each possible viewpoint in the environment to find the target object. Then, the path planning method incorporates the estimated value of these viewpoints and the time cost between them to generate an efficient search path that minimizes the total search time. We also describe a search space reduction method that improves the feasibility of the proposed approach in large-scale environments. To validate the approach, we compare the search times of the proposed method to those of human participants, a coverage-based search and a random search in simulation experiments. The results show that the proposed method can generate search paths with similar search times to those of human participants, while clearly outperforming the coverage-based and random search methods. We also demonstrate the applicability of the approach in real-world experiments in which the robot could find the target object without a single failure case in 70 trials.

中文翻译：

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使用常识和空间关系在未知的大规模环境中进行活动对象搜索

在这项研究中，目标是在先前未知的大规模环境中有效且主动地搜索目标对象。为此，我们开发了一个概率环境模型，可以利用空间常识知识和特定于环境的空间关系。该模型评估探索环境中每个可能的视点以找到目标对象的优点。然后，路径规划方法结合了这些视点的估计值以及它们之间的时间成本，以生成使总搜索时间最小化的有效搜索路径。我们还描述了一种搜索空间缩减方法，该方法可提高所提出方法在大规模环境中的可行性。为了验证该方法，我们将提出的方法与人类参与者的搜索时间进行了比较，模拟实验中基于覆盖的搜索和随机搜索。结果表明，该方法可以生成搜索时间与人类参与者相似的搜索路径，同时明显优于基于覆盖率的搜索方法和随机搜索方法。我们还演示了该方法在实际实验中的适用性，在该实验中，机器人可以在70次试验中找到目标对象而没有一个单独的失败案例。