Data-Driven Damage Detection and Control Adaptation for an Autonomous Underwater Vehicle

Abstract

Underwater robotic exploration missions typically involve traveling long distances without any human contact. The robots that go on such missions risk getting damaged by the unknown environment, accruing great costs and missed opportunities. Thus it is important for the robot to be able to accommodate unknown changes to its dynamics as much as possible and attempt to finish the given mission, or at the very least move itself to a retrievable position.In this paper, we show how we can detect physical changes to the robot reliably (79% on real robot data) and then incorporate these changes through adapting the model to the data followed by automated control redesign. We adopt a piecewise-affine (PWA) modelling of the dynamics that is well suited for low data regime learning of the dynamics and provides a structure for computationally efficient control synthesis. We demonstrate the effectiveness of the proposed method on a combination of real robot data and simulated scenarios.