Predictive Exploration for Autonomous Science

Often remote investigations use autonomous agents to observe an environment on behalf of absent scientists. Predictive exploration improves these systems’ efficiency with onboard data analysis. Agents can learn the structure of the environment and predict future observations, reducing the remote exploration problem to one of experimental design. In our formulation information gain over a map guides exploration decisions, while a similar criterion suggests the most informative data products for downlink. Ongoing work will develop appropriate models for surface exploration by planetary robots. Experiments will demonstrate these algorithms on kilometer-scale autonomous geology tasks. On Remote Autonomous Science In general today’s planetary exploration robots do not travel beyond the previous day’s imagery. However, advances in autonomous navigation will soon permit traverses of multiple kilometers. This promises significant benefits for planetary science: rovers can visit multi...

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: David R. Thompson
• Keywords: AAAI 2007, Autonomous, Exploration, Intelligent Agents, Long Autonomous Traverses