Belief-based fault recovery for marine robotics

We propose a framework expanding the capabilities of underwater robots to autonomously recover from anomalous situations. The framework is built around a knowledge model developed in three stages.
First, we create a deterministic knowledge base to describe the “health” of hardware, software, and environment components involved in a mission. Next, we describe the same components probabilistically, defining probabilities of failures, faults, and fixes. Finally, we combine the deterministic and probabilistic
knowledge into a minimal ROS package designed to detect failures, isolate the underlying faults, propose fixes for the faults, and determine which is the most likely to help. We motivate the solution with a camera fault scenario and demonstrate it with a thruster failure on a real AUV and a simulated ROV.