6-DOF Interceptor (TC.ST.NW)

BOUNDARYS1 · dim 16

Partial / unstable. SolvSRK survives more than 0% but less than 90% of runs at the comparison noise level, without being beaten by a baseline there. Usable with margin and monitoring; validate on your own configuration. All verdicts →

6-DOF Interceptor (TC.ST.NW) benchmark in the defense-autonomy domain.

Defense autonomy

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
  • tau_act = 0.02
  • tgt_x0 = 695.836007379
  • tgt_y0 = 716.459582582
  • tgt_z0 = -0.0208307293217
  • 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 (TC.ST.NW) (6-dof-interceptor-tc-st-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

#SolverSurvivalSCDnfevWallScore
1SolvSRK
100%
7.3154,0413.54 s0.794
2SciPy RadauSciPy
100%
7.0152,5597.93 s0.785
3Vern9external
100%
5.6136,33811.80 s0.751
4Vern7external
100%
5.292,9029.27 s0.744
5Tsit5external
100%
5.263,9488.08 s0.742
6SciPy DOP853SciPy
100%
5.184,4343.01 s0.740
7CVODE Adamsexternal
100%
5.033,1181.21 s0.739
8SciPy LSODASciPy
100%
5.048,9561.67 s0.739
9SciPy RK45SciPy
100%
4.948,4761.79 s0.736
10FBDFexternal
100%
4.958,6878.46 s0.735
11CVODE BDFexternal
100%
4.247,2131.73 s0.718
12SciPy RK23SciPy
100%
4.1186,6237.20 s0.717
13SciPy BDFSciPy
100%
4.165,5074.30 s0.716
14TRBDF2external
100%
1.684,4969.99 s0.658

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 →

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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_tc_st_nw_2026,
  title        = {Resonix Evidence Portal: 6-DOF Interceptor (TC.ST.NW)},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/6-dof-interceptor-tc-st-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