Human-Autonomy Teaming for Improved Diver Navigation
Jesse Pelletier, S.M., 2022
John Leonard, Co-Advisor
Lee Freitag, Co-Advisor
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 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. This thesis develops and evaluates the communication architecture and autonomous behaviors for a diver navigation method based on subsurface human-autonomous 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 Micromodem 2 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 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.