AUV 6DOF station-keeping (Fossen)
ADVANTAGES1 · dim 12SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →
12D Fossen 6DOF AUV rigid-body dynamics with hydrodynamic forces - station-keeping maneuver
Problem definition
Fossen (2011) Handbook of Marine Craft Hydrodynamics
Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.
Show canonical RHS excerpt
def _angular_rate_transform(phi, theta):
"""Transform body angular rates [p,q,r] to Euler angle rates [dphi,dtheta,dpsi]."""
cp, sp = np.cos(phi), np.sin(phi)
ct = np.cos(theta)
tt = np.tan(np.clip(theta, -1.4, 1.4))
sec_t = 1.0 / ct if abs(ct) > 1e-12 else 1e12 * np.sign(ct)
T = np.array([
[1.0, sp * tt, cp * tt],
[0.0, cp, -sp],
[0.0, sp * sec_t, cp * sec_t],
])
return T
def _rotation_matrix(phi, theta, psi):
"""Standard ZYX rotation matrix for NED frame."""
cp, sp = np.cos(phi), np.sin(phi)
ct, st = np.cos(theta), np.sin(theta)
cy, sy = np.cos(psi), np.sin(psi)
R = np.array([
[cy * ct, cy * st * sp - sy * cp, cy * st * cp + sy * sp],
[sy * ct, sy * st * sp + cy * cp, sy * st * cp - cy * sp],
[-st, ct * sp, ct * cp],
])
return R
def _auv_rhs(t, y):
"""Fossen 6DOF AUV: d_eta/dt = J(eta)*nu, M*d_nu/dt = -C*nu - D*nu - g + tau."""
eta = y[:6] # [x, y, z, phi, theta, psi]
nu = y[6:12] # [u, v, w, p, q, r]
phi, theta, psi = eta[3], eta[4], eta[5]
u, v, w, p, q, r = nu[0], nu[1], nu[2], nu[3], nu[4], nu[5]
# -- kinematics: d_eta/dt = J(eta) * nu --
R = _rotation_matrix(phi, theta, psi)
T_ang = _angular_rate_transform(phi, theta)
d_pos = R @ nu[:3]
d_ang = T_ang @ nu[3:]
# -- Coriolis + centripetal (rigid-body + added-mass cross terms) --
# Simplified: dominant cross-coupling terms
c_u = -(_M - _YV_DOT) * v * r + (_M - _ZW_DOT) * w * q
c_v = (_M - _XU_DOT) * u * r - (_M - _ZW_DOT) * w * p
c_w = -(_M - _XU_DOT) * u * q + (_M - _YV_DOT) * v * p
c_p = (_I_R - _I_Q) * q * r
c_q = (_I_P - _I_R) * p * r
c_r = (_I_Q - _I_P) * p * q
# -- damping: linear + quadratic --
d_u = _XU * u + _XUU * abs(u) * u
d_v = _YV * v + _YVV * abs(v) * v
d_w = _ZW * w + _ZWW * abs(w) * w
d_p = _KP * p
d_q = _MQ * q
d_r = _NR * r
# -- restoring forces (neutrally buoyant, BG offset) --
cp_r, sp_r = np.cos(phi), np.sin(phi)
ct_r, st_r = np.cos(theta), np.sin(theta)
g_vec = np.array([
-(_W - _B) * st_r,
(_W - _B) * ct_r * sp_r,
(_W - _B) * ct_r * cp_r,
_BG * _B * ct_r * sp_r,
_BG * _B * st_r,
0.0,
])
# -- acceleration: M * d_nu/dt = tau + f_hydro - C*nu - g --
# d_* variables use SNAME sign convention (already negative for drag),
# so they are ADDED as forces, not subtracted.
du_dt = (_TAU[0] + d_u - c_u - g_vec[0]) / _M_U
dv_dt = (_TAU[1] + d_v - c_v - g_vec[1]) / _M_V
dw_dt = (_TAU[2] + d_w - c_w - g_vec[2]) / _M_W
dp_dt = (_TAU[3] + d_p - c_p - g_vec[3]) / _I_P
dq_dt = (_TAU[4] + d_q - c_q - g_vec[4]) / _I_Q
dr_dt = (_TAU[5] + d_r - c_r - g_vec[5]) / _I_R
dy = np.empty(12)
dy[0:3] = d_pos
dy[3:6] = d_ang
dy[6] = du_dt
dy[7] = dv_dt
dy[8] = dw_dt
dy[9] = dp_dt
dy[10] = dq_dt
dy[11] = dr_dt
return dy- Parameters
- _B = 490.5
- _BG = 0.02
- _I_P = 1.5
- _I_Q = 12
- _I_R = 7
- _KP = -5
- _M = 50
- _MQ = -10
- _M_U = 55
- _M_V = 70
- _M_W = 80
- _NR = -5
- _TAU = [2, 0, 0, 0, 0, 0]
- _W = 490.5
- _XU = -10
- _XUU = -20
- _XU_DOT = -5
- _YV = -20
- _YVV = -40
- _YV_DOT = -20
- _ZW = -30
- _ZWW = -60
- _ZW_DOT = -30
- Initial condition
- y(0) = [0, 0, -10, 0, 0, 0, 0.1, 0, 0, 0, 0, 0]
- Horizon
- t ∈ [0, 60]
Canonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.
Fingerprint
Spread: low
Default noise: low
Recommendation snapshot
Clean best: Vern9
Noisy best: SolvSRK
Coverage
14 solver arms · clean + 5 noise levels
Ranked on survival, precision, and speed
Versions & freeze
Methodology →- Freeze
- 2026-08-13
- libsolvsrk
- 2.3.0
- SciPy
- 1.14
- SUNDIALS
- CVODE (bundled backend)
20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: AUV 6DOF station-keeping (Fossen) (auv-6dof-station-keeping-fossen)
Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.
Self-reported by Resonix Labs · not independently verified
Results matrix
Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.
Objective
Best overall trade-off of survival, precision, and speed.
Noise level
| # | Solver | Survival | SCD | nfev | Wall | Score |
|---|---|---|---|---|---|---|
| 1 | Vern9external | 100% | 11.9 | 290 | 4.04 s | 0.902 |
| 2 | Vern7external | 100% | 11.6 | 242 | 3.95 s | 0.895 |
| 3 | SolvSRK | 100% | 10.9 | 459 | 10 ms | 0.878 |
| 4 | SciPy DOP853SciPy | 100% | 10.5 | 158 | 4 ms | 0.869 |
| 5 | SciPy RadauSciPy | 100% | 10.3 | 441 | 18 ms | 0.863 |
| 6 | SciPy RK45SciPy | 100% | 9.0 | 194 | 5 ms | 0.834 |
| 7 | Tsit5external | 100% | 9.0 | 174 | 668 ms | 0.833 |
| 8 | SciPy RK23SciPy | 100% | 9.0 | 422 | 11 ms | 0.832 |
| 9 | SciPy LSODASciPy | 100% | 8.7 | 151 | 3 ms | 0.827 |
| 10 | FBDFexternal | 100% | 8.0 | 158 | 5.12 s | 0.809 |
| 11 | CVODE Adamsexternal | 100% | 7.8 | 111 | 11 ms | 0.806 |
| 12 | SciPy BDFSciPy | 100% | 7.8 | 214 | 12 ms | 0.804 |
| 13 | CVODE BDFexternal | 100% | 7.5 | 138 | 11 ms | 0.797 |
| 14 | TRBDF2external | 100% | 6.8 | 590 | 5.06 s | 0.782 |
At Clean, best balanced arm is Vern9 · SolvSRK survival 100%, SCD 10.9.
Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →
Cite this page
Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is - a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.
@misc{resonix_evidence_auv_6dof_station_keeping_fossen_2026,
title = {Resonix Evidence Portal: AUV 6DOF station-keeping (Fossen)},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/auv-6dof-station-keeping-fossen}},
note = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}Related
TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels
Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology
Self-reported by Resonix Labs · not independently verified · Verification status