Autonomous adaptive exploration using realtime online spatiotemporal topic modeling
Autonomous adaptive exploration using realtime online spatiotemporal topic modeling
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DOI：
10.1177/0278364913507325
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发表时间：
2014-04
期刊：
影响因子：
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通讯作者：
Yogesh A. Girdhar;P. Giguère;G. Dudek
中科院分区：
文献类型：
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作者：
Yogesh A. Girdhar;P. Giguère;G. Dudek
探索水下珊瑚礁和沉船等危险环境对人类来说是一项艰巨且可能危及生命的任务，这自然使得自主机器人系统的使用非常有吸引力。本文提出了这样一个能够自主探索的自主系统，并展示了它在一系列实验中的用途，以在具有挑战性的水下海洋环境中收集图像数据。我们在三个方面提出了新颖的贡献。首先，我们提出了一种基于在线主题建模的技术来使用低维语义描述符来描述所观察到的内容。该描述符试图对属于同一物种的不同珊瑚的观察或从不同角度观察的相似类型岩石的观察保持不变。其次，我们使用主题描述符来计算当前观察的惊喜分数。这是通过维护迄今为止的观察的在线摘要，然后将惊喜分数计算为当前观察与主题空间中的摘要的距离来完成的。最后，我们提出了一种新颖的水下机器人控制策略，该策略允许智能遍历、悬停在令人惊讶的观察结果上，以及快速游过以前见过的珊瑚和岩石。
The exploration of dangerous environments such as underwater coral reefs and shipwrecks is a difficult and potentially life-threatening task for humans, which naturally makes the use of an autonomous robotic system very appealing. This paper presents such an autonomous system, which is capable of autonomous exploration, and shows its use in a series of experiments to collect image data in challenging underwater marine environments. We present novel contributions on three fronts. First, we present an online topic-modeling-based technique to describe what is being observed using a low-dimensional semantic descriptor. This descriptor attempts to be invariant to observations of different corals belonging to the same species, or observations of similar types of rocks observed from different viewpoints. Second, we use the topic descriptor to compute the surprise score of the current observation. This is done by maintaining an online summary of observations thus far, and then computing the surprise score as the distance of the current observation from the summary in the topic space. Finally, we present a novel control strategy for an underwater robot that allows for intelligent traversal, hovering over surprising observations, and swimming quickly over previously seen corals and rocks.