6-DOF Interceptor (CX.TN.NW)
ADVANTAGES1 · dim 16SolvSRK 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 →
6-DOF Interceptor (CX.TN.NW) benchmark in the defense-autonomy domain.
Problem definition
Canonical benchmark implementation
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 rhs(t, y):
x, yp, z = y[0], y[1], y[2]
u, v, w = y[3], y[4], y[5]
phi, theta, psi = y[6], y[7], y[8]
p, q, r = y[9], y[10], y[11]
de, da, dr, dc = y[12], y[13], y[14], y[15]
d = np.empty(16)
cphi, sphi = np.cos(phi), np.sin(phi)
cth, sth = np.cos(theta), np.sin(theta)
cpsi, spsi = np.cos(psi), np.sin(psi)
V_body = np.sqrt(max(u**2 + v**2 + w**2, 1.0))
alpha = np.arctan2(w - wind_z, max(abs(u - wind_x), 1.0))
beta = np.arcsin(np.clip(v / V_body, -0.99, 0.99))
qbar = 0.5 * _RHO * V_body**2
CL = CL_alpha * alpha
CD = CD0 + CD_alpha2 * alpha**2
L = qbar * S_ref * CL
D = qbar * S_ref * CD
Y = qbar * S_ref * 0.5 * beta
# Body-frame aero forces
X_aero = -D * np.cos(alpha) + L * np.sin(alpha)
Y_aero = -Y
Z_aero = -D * np.sin(alpha) - L * np.cos(alpha)
# Moments
L_aero = qbar * S_ref * 0.3 * (Cl_delta_a * da + Cl_p * p * 0.3 / max(V_body, 1.0))
M_aero = qbar * S_ref * 0.3 * (Cm_alpha * alpha + Cm_delta_e * de + Cm_q * q * 0.3 / max(V_body, 1.0))
N_aero = qbar * S_ref * 0.3 * (Cn_beta * beta + Cn_delta_r * dr + Cn_r * r * 0.3 / max(V_body, 1.0))
# Translational dynamics (body frame)
d[3] = X_aero / m - (q * w - r * v) - _G * sth
d[4] = Y_aero / m - (r * u - p * w) + _G * cth * sphi
d[5] = Z_aero / m - (p * v - q * u) + _G * cth * cphi
# Rotational dynamics
d[9] = (L_aero - (Izz - Iyy) * q * r) / Ixx
d[10] = (M_aero - (Ixx - Izz) * p * r) / Iyy
d[11] = (N_aero - (Iyy - Ixx) * p * q) / Izz
# Euler angle kinematics
sec_th = 1.0 / max(abs(cth), 0.01) * np.sign(cth) if abs(cth) < 0.01 else 1.0 / cth
d[6] = p + (q * sphi + r * cphi) * sth * sec_th
d[7] = q * cphi - r * sphi
d[8] = (q * sphi + r * cphi) * sec_th
# Position (NED)
d[0] = cth * cpsi * u + (sphi * sth * cpsi - cphi * spsi) * v + (cphi * sth * cpsi + sphi * spsi) * w
d[1] = cth * spsi * u + (sphi * sth * spsi + cphi * cpsi) * v + (cphi * sth * spsi - sphi * cpsi) * w
d[2] = -sth * u + sphi * cth * v + cphi * cth * w
# Guidance: PN towards target
tgt_x = tgt_x0 + V_tgt * np.cos(omega_tgt * t) * t
tgt_y = tgt_y0 + V_tgt * np.sin(omega_tgt * t) * t
tgt_z = tgt_z0
dx_t = tgt_x - x
dy_t = tgt_y - yp
dz_t = tgt_z - z
R_los = np.sqrt(dx_t**2 + dy_t**2 + dz_t**2 + 1.0)
los_el = np.arcsin(np.clip(-dz_t / R_los, -0.99, 0.99))
los_az = np.arctan2(dy_t, dx_t + 1e-10)
# LOS error with rate limiting to prevent singularity in crossing geometry
t_go = max(R_los / max(V_body, 1.0), 0.1)
los_rate_el = np.clip((los_el - theta), -0.5, 0.5) / t_go
los_rate_az = np.clip((los_az - psi), -0.5, 0.5) / t_go
# PN acceleration commands (rate-limited)
a_cmd_z = N_pn * V_body * los_rate_el
a_cmd_y = N_pn * V_body * los_rate_az
de_cmd = np.clip(-a_cmd_z / max(abs(Cm_delta_e * qbar * S_ref * 0.3 / Iyy), 0.1), -0.5, 0.5)
dr_cmd = np.clip(a_cmd_y / max(abs(Cn_delta_r * qbar * S_ref * 0.3 / Izz), 0.1), -0.5, 0.5)
# Actuator dynamics (1st-order lag)
d[12] = (de_cmd - de) / tau_act
d[13] = (0.0 - da) / tau_act # wings-level
d[14] = (dr_cmd - dr) / tau_act
d[15] = (0.0 - dc) / tau_act
return d- Parameters
- CD0 = 0.15
- CD_alpha2 = 2
- CL_alpha = 8
- Cl_delta_a = 1.5
- Cl_p = -0.5
- Cm_alpha = -4
- Cm_delta_e = -6
- Cm_q = -3
- Cn_beta = -1
- Cn_delta_r = 2
- Cn_r = -0.8
- Ixx = 0.05
- Iyy = 0.8
- Izz = 0.8
- N_pn = 4
- S_ref = 0.02
- V_tgt = 15
- _G = 9.81
- _RHO = 1.225
- m = 5
- omega_tgt = 0.2
- tau_act = 0.02
- tgt_x0 = 877.58256189
- tgt_y0 = 479.425538604
- tgt_z0 = -50
- wind_x = 0
- wind_z = 0
- Initial condition
- y(0) = [0, 0, -50, 120, 0, 0, …] [shape=(16,), min=-50, max=120]
- Horizon
- t ∈ [0, 10]
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: high
Default noise: high
Recommendation snapshot
Clean best: SolvSRK
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: 6-DOF Interceptor (CX.TN.NW) (6-dof-interceptor-cx-tn-nw)
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 | SolvSRK | 100% | 6.8 | 174,141 | 5.72 s | 0.781 |
| 2 | FBDFexternal | 100% | 5.9 | 63,804 | 6.49 s | 0.760 |
| 3 | SciPy RadauSciPy | 100% | 5.4 | 166,485 | 6.14 s | 0.748 |
| 4 | SciPy RK45SciPy | 100% | 4.9 | 51,314 | 1.16 s | 0.735 |
| 5 | SciPy LSODASciPy | 100% | 4.5 | 54,512 | 1.14 s | 0.726 |
| 6 | Vern7external | 100% | 4.4 | 100,122 | 7.21 s | 0.723 |
| 7 | SciPy DOP853SciPy | 100% | 4.3 | 88,982 | 2.12 s | 0.722 |
| 8 | SciPy RK23SciPy | 100% | 3.9 | 194,846 | 5.10 s | 0.711 |
| 9 | Vern9external | 100% | 3.8 | 146,338 | 9.14 s | 0.709 |
| 10 | SciPy BDFSciPy | 100% | 3.8 | 70,854 | 3.02 s | 0.709 |
| 11 | CVODE Adamsexternal | 100% | 3.6 | 35,842 | 833 ms | 0.704 |
| 12 | Tsit5external | 100% | 3.5 | 67,902 | 6.12 s | 0.703 |
| 13 | CVODE BDFexternal | 100% | 3.5 | 50,503 | 1.18 s | 0.701 |
| 14 | TRBDF2external | 100% | 1.0 | 87,729 | 7.70 s | 0.642 |
At Clean, best balanced arm is SolvSRK.
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_6_dof_interceptor_cx_tn_nw_2026,
title = {Resonix Evidence Portal: 6-DOF Interceptor (CX.TN.NW)},
author = {{Resonix Labs (Canada) Inc.}},
year = {2026},
howpublished = {\url{https://resonixusa.com/evidence/problems/6-dof-interceptor-cx-tn-nw}},
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