Model-based control of an aquatic robotic platform depends on a hydrodynamic model that is costly to identify and specific to the hull, payload, and conditions it was measured in. Here, we present MOSAIC-SV, a deployable real-time adaptive dynamics identification and control system that identifies a control-sufficient dynamics model from a spec-sheet engineering prior, without dedicated identification trials, and re-estimates it at every control step of a closed-loop mission while the controller plans on it. A physically admissible unscented Kalman filter re-estimates hydrodynamic, disturbance, and actuator parameters at every control step of the closed-loop mission; while a command-dependent consider projection withholds corrections the current command cannot attribute between actuator effectiveness and external force; and a model predictive path integral controller plans on the current estimate. In simulation on a CyberShip II plant, MOSAIC-SV recovers the transit performance of the calibrated model under static mismatch and transient changes, and stays within 20% of its own transit time at the unscaled prior when its inertia or damping prior is wrong by an order of magnitude. In on-water field trials on the Blue Robotics BlueBoat, a twin-thruster catamaran, MOSAIC-SV transits at least 25% faster and predicts its own motion with at least 56% less error than its frozen engineering prior, including under an unmodeled payload. The same MOSAIC-SV system concept was also feasibly deployed on a 6.3-tonne dual outboard monohull.
Figures & tables
Fig. 1: A Blue Robotics BlueBoat, the twin-thruster catamaran evaluated in the on-water field trials with an unmodeled extra mass (a rock).
Fig. 2: (a) The general NED and FRD vessel frames. Heading ψ is defined as clockwise from North. (b) The general form of the twin-hulled catamaran vessel class evaluated in this work, with location of forces and corresponding lever arms.
Fig. 3: Simulation course in the world frame. The triangle marks the start, open circles waypoints passed under way, and the square the stop waypoint.
Family
Axis
Perturbation
Levels
Static
mass
Inertia × level
2−3,…,24
Static
cross
Inertia × , damping ÷
2−3,…,24
Static
damping
Damping × level
2−3,…,24
Static
combined
Inertia/damping × ; 45 ∘
2−3,…,24 ; force 0–3.2 N ‡
Static
current
Force at 45/0/90 ∘
0–3.2 N, step 0.4 N
Transient
mass
Inertia × level
1/4 , 1/2 , 1.5 , 2 , 3 , 4 , 6 , 8
TABLE I: Perturbation families
Fig. 4: Static mismatch. Top: median transit time per plant level. n gives the number of seeds that completed the run when there were incomplete runs. Bottom: median self-prediction error. Levels are the scale applied to the unperturbed plant.
vs. calibrated
vs. frozen
Condition
n
Δtr %
Δc pp
Δe %
Δtr %
Δc pp
Δe %
mass
96
-25.2
-11.5
+17.3
-27.5
-12.5
+3.1
cross
96
-0.6
+4.2
-29.0
-2.7
+0.0
-8.9
damping
96
+2.1
+0.0
-41.3
-26.2
+0.0
-8.5
combined
96
-5.9
+0.0
+12.8
-26.0
+0.0
+11.7
hull, all
384
-1.5
-1.8
-7.1
-26.0
-3.1
-5.8
TABLE II: Static simulation comparison
vs. calibrated
vs. frozen
Condition
n
Δtr %
Δc pp
Δe %
Δtr %
Δc pp
Δe %
Step mass
96
-11.8
+25.0
-24.2
+5.0
+12.5
-26.7
Step damping
72
+9.9
+0.0
-38.9
-31.1
+0.0
-18.2
Step current
60
+8.6
+0.0
-31.7
-30.1
-20.0
-16.1
Step port
60
+3.4
+0.0
-23.5
-29.4
+0.0
-37.8
Step stbd
60
+1.5
+0.0
-4.7
-35.5
+0.0
-22.9
TABLE III: Transient simulation comparison
Fig. 5: MOSAIC-SV sensitivity to the engineering prior. Evaluated using an unperturbed plant and scaling the prior inertia, damping, or both. The band marks ±20% of the transit time at the unscaled prior.
Fig. 6: Field test run trajectories. Crosses mark waypoints with their 2 m arrival radius.
vs. frozen
Cell
n
Δtr %
Δe %
Single nom.
3
-40.3
-59.9
O/B nom.
3
-40.0
-69.7
Single +9.1 kg
3
-42.9
-56.9
O/B +9.1 kg
3
-25.3
-70.3
TABLE IV: Field trial comparison
Fig. 7: On-water runs of the 6.3-tonne dual outboard monohull. (a) Three-waypoint run. (b) Single-obstacle run. Crosses mark waypoints with their arrival radius; the circle marks the obstacle with its avoidance clearance.
Accurate and adaptive dynamic models are critical for underwater vehicle-manipulator systems where hydrodynamic effects induce time-varying parameters. This paper introduces a novel uncertainty-aware adaptive dynamics model framework that remains linear in lumped vehicle and manipulator parameters, and embeds convex physical consistency constraints during online estimation. Moving horizon estimation is used to stack horizon regressors, enforce realizable inertia, damping, friction, and hydrostatics, and quantify uncertainty from parameter evolution. Experiments on a BlueROV2 Heavy with a 4-DOF manipulator demonstrate rapid convergence and calibrated predictions. Manipulator fits achieve R2 = 0.88 to 0.98 with slopes near unity, while vehicle surge, heave, and roll are reproduced with good fidelity under stronger coupling and noise. Median solver time is approximately 0.023 s per update, confirming online feasibility. A comparison against a fixed parameter model shows consistent reductions in MAE and RMSE across degrees of freedom. Results indicate physically plausible parameters and confidence intervals with near 100% coverage, enabling reliable feedforward control and simulation in underwater environments.
Edward Morgan, Nenyi K Dadson, Corina Barbalata
The authors are with the Department of Mechanical & Industrial Engineering, at Louisiana State University, Baton Rouge, LA 70803, USA
In this work, we propose a structured methodology for the system identification of underwater vehicles through the design of optimal excitation trajectories. To this end, the trajectories are parameterized using Bezier curves, which ensure smooth and differentiable motion profiles while facilitating the enforcement of constraints through appropriate manipulation of the control points. An optimization problem is formulated to determine a dynamically feasible excitation trajectory that respects safety limits and maximizes the quality of the collected data, thereby enabling reliable estimation of the vehicle's dynamic parameters using least squares. The proposed methodology is experimentally validated in a laboratory water tank, where the dynamic parameters, identified from the optimized trajectory, are evaluated by predicting the vehicle's velocity through forward simulation on previously unseen trajectories.
Fotis Panetsos, Kostas J. Kyriakopoulos
Center for AI & Robotics (CAIR) and the Electrical Eng. Program, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Accurate trajectory tracking is essential for autonomous underwater vehicles operating with uncertain hydrodynamics and limited thruster authority. This paper develops a low-complexity adaptive prescribed performance controller for uncertain underwater vehicle dynamics with coupled and saturated inputs. The proposed method adapts the performance functions directly through the mismatch between the requested and saturated control inputs, enlarging the performance funnels only when the available actuation cannot meet the nominal specifications. A coupled invariant-set analysis establishes boundedness of all closed-loop signals, prescribed bounds on the filtered tracking errors, and explicit feasibility conditions. Comparative simulations on a BlueROV2 underwater vehicle show that the adaptive design maintains prescribed performance during a figure-eight maneuver, whereas conventional prescribed performance control loses feasibility under input limits. A real-time pool experiment further demonstrates stable trajectory tracking and the practical implementation of the proposed method on an underwater robotic platform.
Athanasios K. Gkesoulis, Christos Vlachos, George K. Fourlas +2
The authors are with the Robotics Institute, Athena Research Center, Marousi, Greece · The authors are with the Division of Systems and Automatic Control, Department of Electrical and Computer Engineering, University of Patras, Patras, Greece · The authors are with the Department of Informatics and Telecommunications, School of Sciences, University of Thessaly, Lamia, Greece