A Seaglider instrumented with an inertial attitude and heading reference system was tracked for three days on an acoustic tracking range in Dabob Bay, Washington, operated by the Naval Undersea Warfare Center, Keyport, WA, USA. Inertial measurements were integrated to yield estimates of position and compared with tracked positions. Within 3 min, the integrated positioning results deviated from tracked positions by more than a kilometer. The addition of a depth constraint from pressure sensor measurements slowed the error growth over time, but even with this constraint, measurements were too noisy to accurately determine position without aid from additional sensors. Inertial data did contribute to accurate localization when used to estimate vehicle attitude and incorporated into an existing flight model; however, results did not demonstrate marked improvement over existing flight models. Although not a decisive demonstration of vehicle positioning with a standalone low-cost, low-power sensor, the results presented here provide a benchmark for comparison as MEMS inertial sensors continue to evolve using a valuable ground-truth of subsea Seaglider position not previously available.

Trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The authors introduce the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e., providing direct comparisons and quantitative evaluations between autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g., for surveying, surveillance, search and rescue). Three case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission.