Autonomous science target identification and acquisition (ASTIA) for planetary exploration
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
AEGIS Automated Science Targeting for the MER Opportunity Rover
ACM Transactions on Intelligent Systems and Technology (TIST)
Fuzzy-rough feature selection aided support vector machines for Mars image classification
Computer Vision and Image Understanding
Rover-Based Autonomous Science by Probabilistic Identification and Evaluation
Journal of Intelligent and Robotic Systems
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In common with other Mars exploration missions, human supervision of Europe's ExoMars Rover will be mostly indirect via orbital relay spacecraft and thus far from immediate. The gap between issuing commands and witnessing the results of the consequent rover actions will typically be on the order of several hours or even sols. In addition, it will not be possible to observe the external environment at the time of action execution. This lengthens the time required to carry out scientific exploration and limits the mission's ability to respond quickly to favorable science events. To increase potential science return for such missions, it will be necessary to deploy autonomous systems that include science target selection and active data acquisition. In this work, we have developed and integrated technologies that we explored in previous studies and used the resulting test bed to demonstrate an autonomous, opportunistic science concept on a representative robotic platform. In addition to progressing the system design approach and individual autonomy components, we have introduced a methodology for autonomous science assessment based on terrestrial field science practice. © 2009 Wiley Periodicals, Inc.