Path / trajectory tracking

ADVANTAGES1 · 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 →

Cascade path-following controller coupled to attitude loop

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 _fig8_ref(t):
    omega = 2.0 * np.pi / 30.0
    px = 5.0 * np.sin(omega * t)
    py = 2.5 * np.sin(2.0 * omega * t)
    vx = 5.0 * omega * np.cos(omega * t)
    vy = 5.0 * omega * np.cos(2.0 * omega * t)
    return np.array([px, py, 5.0, vx, vy, 0.0])

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_B3(t, y):
    body = y[:12]
    pos_int = y[12:18]
    att_int = y[18:24]

    sp = _fig8_ref(t)

    pos_err = sp[:3] - body[:3]
    vel_err = sp[3:] - body[3:6]

    ax_d = KP_POS * pos_err[0] + KD_POS * vel_err[0] + _KI_POS * pos_int[0]
    ay_d = KP_POS * pos_err[1] + KD_POS * vel_err[1] + _KI_POS * pos_int[1]
    az_d = KP_POS * pos_err[2] + KD_POS * vel_err[2] + _KI_POS * pos_int[2]

    psi = body[8]
    T_des = MASS * (G + az_d)
    phi_des = (1.0 / G) * (ax_d * np.sin(psi) - ay_d * np.cos(psi))
    theta_des = (1.0 / G) * (ax_d * np.cos(psi) + ay_d * np.sin(psi))

    phi, theta = body[6], body[7]
    p, q, r_rate = body[9], body[10], body[11]
    e_att = np.array([phi_des - phi, theta_des - theta, -psi])
    tau = KP_ATT * e_att - KD_ATT * np.array([p, q, r_rate]) + _KI_ATT * att_int[:3]
    tau = np.clip(tau, -TORQUE_CLIP, TORQUE_CLIP)
    T_des = np.clip(T_des, 0.0, THRUST_MAX)

    d_body = _quad12(body, T_des, tau[0], tau[1], tau[2])
    d_pos_int = np.clip(np.concatenate([pos_err, vel_err]), -_KI_POS_CLIP, _KI_POS_CLIP)
    d_att_int = np.clip(np.concatenate([e_att, -np.array([p, q, r_rate])]), -_INT_CLIP, _INT_CLIP)

    return np.concatenate([d_body, d_pos_int, d_att_int])
Parameters
  • CD = 0.1
  • G = 9.81
  • IXX = 0.0082
  • IYY = 0.0082
  • IZZ = 0.0148
  • KD_ATT = 2.5
  • KD_POS = 4
  • KP_ATT = 8
  • KP_POS = 6
  • MASS = 1
  • THRUST_MAX = 39.24
  • TORQUE_CLIP = 2
  • _INT_CLIP = 1
  • _KI_ATT = 1.5
  • _KI_POS = 0.5
  • _KI_POS_CLIP = 2
  • mass = None
Initial condition
y(0) = [0, 0, 5, 0, 0, 0, …] [shape=(24,), min=0, max=5]
Horizon
t ∈ [0, 120]

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: Path / trajectory tracking (path-trajectory-tracking)

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%
10.79,480203 ms0.874
2SciPy RadauSciPy
100%
8.913,562648 ms0.831
3SciPy RK23SciPy
100%
7.7139,9494.96 s0.803
4FBDFexternal
100%
7.64,4655.44 s0.801
5CVODE BDFexternal
100%
7.62,26193 ms0.799
6SciPy LSODASciPy
100%
7.59,373286 ms0.797
7Vern7external
100%
7.2189,61216.36 s0.790
8Vern9external
100%
7.2329,20224.40 s0.789
9SciPy DOP853SciPy
100%
7.1139,3104.71 s0.788
10SciPy BDFSciPy
100%
7.13,824232 ms0.788
11Tsit5external
100%
7.1138,09617.04 s0.788
12SciPy RK45SciPy
100%
7.0168,9745.64 s0.787
13CVODE Adamsexternal
100%
6.611,685417 ms0.777
14TRBDF2external
100%
5.023,1876.88 s0.739

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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SolvSRK · 30-day trial

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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_path_trajectory_tracking_2026,
  title        = {Resonix Evidence Portal: Path / trajectory tracking},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/path-trajectory-tracking}},
  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