Aggressive maneuvers (high angular rates)

ADVANTAGES2 · dim 18

SolvSRK 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 →

Near-saturation torque, frequent state jumps; S2 common under high rates

Drone dynamics & 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 _body_forces(T, phi, theta, psi):
    """Thrust-to-inertial force components."""
    cp, sp = np.cos(phi), np.sin(phi)
    ct, st = np.cos(theta), np.sin(theta)
    cy, sy = np.cos(psi), np.sin(psi)
    Fx = T * (cy * st * cp + sy * sp)
    Fy = T * (sy * st * cp - cy * sp)
    Fz = T * ct * cp
    return Fx, Fy, Fz

def _euler_kinematics(phi, theta, p, q, r):
    """Euler-angle rates from body rates. Returns (dphi, dtheta, dpsi)."""
    cp, sp = np.cos(phi), np.sin(phi)
    theta_c = np.clip(theta, -1.39, 1.39)
    tan_th = np.tan(theta_c)
    cos_th = np.cos(theta_c)
    sec_th = 1.0 / cos_th if abs(cos_th) > 1e-12 else 1e12 * np.sign(cos_th)
    dphi = p + q * sp * tan_th + r * cp * tan_th
    dtheta = q * cp - r * sp
    dpsi = (q * sp + r * cp) * sec_th
    return dphi, dtheta, dpsi

def _quad12(y, T, tau_x, tau_y, tau_z, mass=None):
    """Core 12-state quadrotor dynamics. Returns d[0:12].

    BA-2 (2026-04-29): added optional ``mass`` kwarg so the
    factory variants can override the module-global ``MASS`` for the
    translational acceleration / drag terms.
    """
    eff_mass = MASS if mass is None else mass
    phi, theta, psi = y[6], y[7], y[8]
    p, q, r = y[9], y[10], y[11]
    Fx, Fy, Fz = _body_forces(T, phi, theta, psi)

    d = np.empty(12)
    d[0] = y[3]; d[1] = y[4]; d[2] = y[5]
    d[3] = (Fx - CD * y[3]) / eff_mass
    d[4] = (Fy - CD * y[4]) / eff_mass
    d[5] = (Fz - CD * y[5]) / eff_mass - G
    d[6], d[7], d[8] = _euler_kinematics(phi, theta, p, q, r)
    d[9] = (tau_x + (IYY - IZZ) * q * r) / IXX
    d[10] = (tau_y + (IZZ - IXX) * p * r) / IYY
    d[11] = (tau_z + (IXX - IYY) * p * q) / IZZ
    return d

def rhs_B4(t, y):
    body = y[:12]
    integ = y[12:18]

    phi, theta, psi = body[6], body[7], body[8]
    p, q, r_rate = body[9], body[10], body[11]

    flip_phase = min(t / 2.0, 1.0)
    target_phi = 2.0 * np.pi * flip_phase if t < 2.0 else 0.0

    e_att = np.array([target_phi - phi, -theta, -psi])
    e_rate = np.array([-p, -q, -r_rate])

    tau = (_KP_ATT_AGG * e_att + _KD_ATT_AGG * e_rate
           + _KI_ATT * integ[:3] + _KI_RATE * integ[3:])
    tau = np.clip(tau, -TORQUE_CLIP * 2.0, TORQUE_CLIP * 2.0)
    T_cmd = np.clip(MASS * G * 1.5, 0.0, THRUST_MAX)

    d_body = _quad12(body, T_cmd, tau[0], tau[1], tau[2])
    d_int_att = np.clip(e_att, -_INT_CLIP, _INT_CLIP)
    d_int_rate = np.clip(e_rate, -_INT_CLIP, _INT_CLIP)

    return np.concatenate([d_body, d_int_att, d_int_rate])
Parameters
  • CD = 0.1
  • G = 9.81
  • IXX = 0.0082
  • IYY = 0.0082
  • IZZ = 0.0148
  • MASS = 1
  • THRUST_MAX = 39.24
  • TORQUE_CLIP = 2
  • _INT_CLIP = 1
  • _KD_ATT_AGG = 6
  • _KI_ATT = 1.5
  • _KI_RATE = 0.3
  • _KP_ATT_AGG = 25
  • mass = None
Initial condition
y(0) = [0, 0, 5, 0, 0, 0, …] [shape=(18,), min=0, max=5]
Horizon
t ∈ [0, 15]

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: low

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: Aggressive maneuvers (high angular rates) (aggressive-maneuvers-high-angular-rates)

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%
9.94,59882 ms0.854
2SciPy DOP853SciPy
100%
8.022,250433 ms0.810
3SciPy RadauSciPy
100%
7.75,709256 ms0.803
4SciPy RK23SciPy
100%
7.215,212458 ms0.791
5Tsit5external
100%
7.024,5523.62 s0.785
6SciPy LSODASciPy
100%
6.93,29079 ms0.783
7CVODE BDFexternal
100%
6.91,43756 ms0.782
8SciPy RK45SciPy
100%
6.823,702693 ms0.780
9SciPy BDFSciPy
100%
6.61,984100 ms0.776
10CVODE Adamsexternal
100%
6.53,484112 ms0.774

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_aggressive_maneuvers_high_angular_rates_2026,
  title        = {Resonix Evidence Portal: Aggressive maneuvers (high angular rates)},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/aggressive-maneuvers-high-angular-rates}},
  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