Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring
Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring
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DOI：
10.1007/s10514-015-9500-x
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发表时间：
2015-09
影响因子：
3.5
通讯作者：
Yogesh A. Girdhar;G. Dudek
中科院分区：
文献类型：
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作者：
Yogesh A. Girdhar;G. Dudek
本文提出了一种新的方法来建模的好奇心在移动的机器人，这是有用的监测和自适应数据收集任务，特别是在长期自主任务的情况下，预编程的任务可能有有限的效用。我们使用一个实时的主题建模技术来建立一个语义感知模型的环境，使用它，我们计划一个路径，通过在世界上的位置与高语义信息含量。所提出的感知模型的终身学习行为使其适合于长期的探索任务。我们使用模拟勘探实验，使用空中和水下数据验证的方法，并演示了在各种情况下的水水下机器人的实施。我们发现，所提出的探索路径，偏向于具有高主题困惑的位置，产生更好的地形模型，具有高的判别力。此外，我们还表明，在Aqua机器人上实施的拟议算法能够执行珊瑚礁检查、潜水员跟随和海底勘探等任务，无需任何事先培训或准备。
This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility. We use a realtime topic modeling technique to build a semantic perception model of the environment, using which, we plan a path through the locations in the world with high semantic information content. The life-long learning behavior of the proposed perception model makes it suitable for long-term exploration missions. We validate the approach using simulated exploration experiments using aerial and underwater data, and demonstrate an implementation on the Aqua underwater robot in a variety of scenarios. We find that the proposed exploration paths that are biased towards locations with high topic perplexity, produce better terrain models with high discriminative power. Moreover, we show that the proposed algorithm implemented on Aqua robot is able to do tasks such as coral reef inspection, diver following, and sea floor exploration, without any prior training or preparation.