ACA 2014 - Workshop on Autonomous Control, Adaptation, and Learning for Underwater Vehicles

There has been a steady increase in the deployment of autonomous underwater and surface vehicles (AUVs and ASVs) for applications such as hazardous waste mitigation, inspection and recovery of marine structures, environmental monitoring, and tracking of various biological, chemical, and physical processes. These emerging applications require solving unique challenges that arise when working in the underwater environment. The lack of reliable wireless communications between robots and a base station or with other robots makes remote control difficult; underwater vehicle dynamics are tightly coupled with the environmental dynamics making controls hard; and wellunderstood perception technologies do not always apply to the underwater environment. These challenges, in addition to our limited understanding of the complexities of the fluidic environment, make closed-loop control, online learning, and adaptive decision making challenging at best.
The purpose of this workshop is to bring together experts in the highly interdisciplinary field of autonomous underwater robotics to bridge the gap between (1) modeling and prediction for closed-loop control and (2) online learning and adaptation in highly dynamic and uncertain environments. Specifically we would like to highlight new work that lies at the intersection of robotics, control theory, artificial intelligence, machine learning, ocean science, and transport theory that addresses issues in modeling and prediction of the underwater environment. The techniques developed in this workshop will lead to improvements in control, learning, and adaptation for underwater systems that are paramount to achieving prolonged persistent autonomy in these environments.