Adaptive Introspection and Deployment for Robust Long Duration Autonomy
CARNEGIE-MELLON UNIV PITTSBURGH PA
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Long duration autonomy for unmanned systems is difficult to achieve as current systems are limited to anticipated exceptions and do not adapt to long-term changes in the environment. The project goal is to enable long-term operation in unpredictable environments through adaptive introspection and deployment approaches, emphasizing vehicle and system level adaptation and robustness as a robotic team cooperates toward a common and persistent mission goal. At the vehicle level, we address the problems of identifying unexpected vehicle states and developing robust mitigation policies and behaviors through introspection. At the system level, we propose long-term planning methodologies that coordinate the robot team toward a common mission objective while learning, adapting to, and anticipating changing vehicle and environment conditions. The proposed research outcomes will be broadly applicable to unmanned vehicle systems operating over long time horizons to achieve a common and persistent mission. In order to evaluate the performance of the proposed methods, we also pursue an integration and experimentation design that will provoke exceptions over long durations.
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