Loosely-Coupled Human-Robot Teams for Enhanced Undersea Operations

Abstract

This paper presents improved algorithms for localization and navigation in which an autonomous underwater vehicle (AUV) supports a human diver. Our initial efforts validated state estimation algorithms and communication protocols for accurate diver navigation based on subsurface teaming with no ocean current data or exact diver speeds. By leveraging acoustic modem messaging and iterative ranging between the AUV and diver, this collaborative team maintains a loosely-coupled support structure that does not rely on close proximity or maintaining sight of a teammate. Range and odometry measurements comprise a factor graph structure that leverages the incremental smoothing and mapping 2 (iSAM2) algorithm for state estimation. However, this approach suffers from decreased accuracy in environments with heavy ocean currents. This requires an updated measurement strategy for ocean currents and new communication protocols to allow a diver to compensate for ocean currents. Extensive simulation results and comparisons to previous non-adaptive techniques show that these updates enable more efficient diver paths to a known target, decreased workload on the diver, and increased accuracy and robustness to ocean currents at the limits of human diver capability.