Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and Reachability Analysis
<|reference_start|>Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and Reachability Analysis: Marine robots must maintain precise control and ensure safety during tasks like ocean monitoring, even when encountering unpredictable disturbances that affect performance. Designing algorithms for uncrewed surface vehicles (USVs) requires accounting for these disturbances to control the vehicle and ensure it avoids obstacles. While adaptive control has addressed USV control challenges, real-world applications are limited, and certifying USV safety amidst unexpected disturbances remains difficult. To tackle control issues, we employ a model reference adaptive controller (MRAC) to stabilize the USV along a desired trajectory. For safety certification, we developed a reachability module with a moving horizon estimator (MHE) to estimate disturbances affecting the USV. This estimate is propagated through a forward reachable set calculation, predicting future states and enabling real-time safety certification. We tested our safe autonomy pipeline on a Clearpath Heron USV in the Charles River, near MIT. Our experiments demonstrated that the USV's MRAC controller and reachability module could adapt to disturbances like thruster failures and drag forces. The MRAC controller outperformed a PID baseline, showing a 45%-81% reduction in RMSE position error. Additionally, the reachability module provided real-time safety certification, ensuring the USV's safety. We further validated our pipeline's effectiveness in underway replenishment and canal scenarios, simulating relevant marine tasks.<|reference_end|>
@article{mahesh2024safe,
    title={Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and
  Reachability Analysis},
    author={Karan Mahesh, Tyler M. Paine, Max L. Greene, Nicholas Rober, Steven
  Lee, Sildomar T. Monteiro, Anuradha Annaswamy, Michael R. Benjamin, Jonathan
  P. How},
    journal={arXiv preprint arXiv:2410.01038},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01038},
    primaryClass={cs.RO cs.SY eess.SY}
}