This letter presents a precise navigation technique for a scuba diver using an autonomous underwater vehicle (AUV) as a positioning aid. We develop and evaluate the state estimation algorithms and communication architecture for diver navigation based on subsurface human-AUV teaming with no requirement for ocean current data or exact diver speeds. By depending on acoustic communication and commercial AUV navigation capabilities, our method provides a unique capability for localizing a diver's position during an extended mission, without the requirement to maintain visual contact with the diver during the mission, or to deploy acoustic beacons. We utilize the Woods Hole Oceanographic Institution Micromodem 2 for range-only single-beacon navigation between two kayaks serving as proxies for the diver and REMUS 100 AUV. Range and odometry measurements are fused in a factor graph using incremental smoothing and mapping 2 (iSAM2) with appropriate motion and measurement models to provide real-time diver position estimates given unknown ocean currents. Field experiments demonstrate an average online endpoint error of 4.53 meters after 400-meter transits.

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.

Diving operations are inherently complex due to navigation and communication limitations. Until recently, fixed-beacon acoustic localization techniques have served as the primary means of improving diver navigation. However, modern artificial intelligence and acoustic modem technologies have enabled accurate relative navigation methods between a diver and an autonomous vehicle. Human-robot collaboration takes advantage of each member’s strengths to create the most effective team. This concept proves especially advantageous within the ocean domain, where humans are naturally deficient navigators. Yet humans serve as the team’s creative spirit, offering the critical thinking and flexibility needed to succeed in an unpredictable and dynamic environment. Recent underwater human-robot cooperative navigation systems typically rely on autonomous surface vehicles (ASVs), specially designed underwater vehicles, or stereo cameras. This thesis proposes a diver navigation method exhibiting significantly improved accuracy over dead reckoning without relying on a surface presence, cameras, or fixed acoustic beacons. Specifically, we develop and evaluate the communication architecture and autonomous behaviors required to guide a diver to a target location using subsurface humanautonomous underwater vehicle (AUV) teaming with no requirement for ocean current data or exact diver speeds. By depending on acoustic communication and commercial AUV navigation capabilities, our method has increased accessibility, applicability, and robustness over former techniques. We utilize the Woods Hole Oceanographic Institution (WHOI) Micromodem 2’s twoway-travel-time (TWTT) capability to enable range-only single-beacon navigation between two kayaks serving as proxies for the diver and Remote Environmental Monitoring Units (REMUS) 100 AUV. During processing, a nonlinear least-squares (NLS) method, called incremental smoothing and mapping 2 (iSAM2), utilizes odometry and range measurements to provide real-time diver position estimates given unknown ocean currents. Field experiments demonstrate an average online endpoint error of 4.53 meters after transits four hundred meters long. Additionally, simulations test our method’s performance in more challenging situations than those experienced in the field. Overall, this research progresses the interoperability of divers and AUVs.