|Adaptive Introspection for Robust Long Duration Autonomy
Long duration autonomy for unmanned systems is difficult to achieve as current systems are design limited to anticipated exceptions and do not adapt to long-term changes in the environment. In addition, the challenge of designing experiments for long durations that provoke unanticipated exceptions is difficult. In this project we will enable long-term operation in unpredictable environments by developing an adaptive introspection and deployment approach and evaluating the ideas in an experimental setup that will provoke exceptions.
|The Aerial Robotic Infrastructure Analyst (ARIA)
The Aerial Robotic Infrastructure Analyst (ARIA) rapidly creates comprehensive, high-resolution, semantically rich 3D models of infrastructure – an interactive assistant for infrastructure inspection.
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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