Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring

详细信息    查看全文

• 作者：Yogesh Girdhar ; Gregory Dudek
• 关键词：Autonomous exploration ; Topic modeling ; Marine robotics ; Long ; term autonomy
• 刊名：Autonomous Robots
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：40
• 期：7
• 页码：1267-1278
• 全文大小：3,676 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Automation and Robotics
  Electronic and Computer Engineering
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Mechanical Engineering
  Simulation and Modeling
  
• 出版者：Springer Netherlands
• ISSN：1573-7527
• 卷排序：40

文摘

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.