Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration and Monitoring
This addresses the need for adaptive data collection in long-term missions for robotics, though it is incremental as it builds on existing topic modeling techniques.
The paper tackles the problem of enabling long-term autonomous exploration and monitoring for mobile robots by modeling curiosity to guide path planning towards areas with high semantic information, resulting in better terrain models with high discriminative power and successful tasks like coral reef inspection without prior training.
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.