GeneticWing

Evolutionary, flatness-based planning of energy-harvesting trajectories for fixed-wing UAVs.

GeneticWing extends the ARPL flat planning pipeline for fixed-wing aircraft (Morando et al., ICRA 2025 and arXiv 2605.15619) from efficient gliding to soaring. An evolutionary search runs over Bernstein-polynomial position trajectories. Differential flatness turns each candidate into airspeed, C_L, bank and required thrust with no integration, so a whole population is scored in one vectorized pass. The best candidate then seeds a local NLP and an energy-aware NMPC.

Phase 1: simulation foundation

Module	Content
geneticwing.bernstein	Bernstein segments and chains, derivatives, batch evaluation
geneticwing.aircraft	Point-mass airframe, parabolic polar, exact steady glide; Stratosurfer (ARPL platform, placeholder mass and area) and SB-XC sailplane (estimated polar)
geneticwing.flatness	Flat map: trajectory + wind -> airspeed, C_L, bank, load factor, T_req
geneticwing.energy	Specific energy, netto variometer
geneticwing.wind	Level-N1 winds: uniform, thermal (Allen scaling), ridge lift, Dryden turbulence
geneticwing.planning.glide	Local NLP glide planner (baseline, SciPy SLSQP)
geneticwing.simulation	Closed-loop point-mass flight: path tracker + attitude lag
scenarios/s0_glide.py	Scenario S0: reproduction of the ARPL straight glide

Unpowered, position alone is not a flat output: a glider has two inputs (C_L, bank) for three outputs. The planner therefore treats T_req = 0 as a constraint, which generalizes the sink-rate constraint of arXiv 2605.15619 to non-uniform wind.

Scenario S0: reproduction of the ARPL glide

The setup is a 400 m leg from 120 m, with a 12.5 m/s airspeed reference and a 5.2 m/s tailwind. The closed-loop column is the mean of 50 Dryden realizations.

Metric	ARPL flight	Plan, uniform wind	Closed loop, Dryden
Mean airspeed (m/s)	12.50	12.50	12.61
Mean sink rate (m/s)	1.48	1.58	1.61
Glide ratio	9.83	11.15	11.27
Airspeed RMSE (m/s)	1.09	0.00	0.87
Sink-rate RMSE (m/s)	0.39	0.00	0.43
Netto RMSE (m/s)	0.69	0.00	0.72

The planner converges from 20 out of 20 perturbed initial guesses.

Known gaps, to close with the lab's data:

• Stratosurfer mass (1.3 kg) and wing area (0.30 m²) are placeholders. The polar is fitted to the best-glide point the paper reports (12 m/s, 1.51 m/s sink). As a result, the predicted sink at 12.5 m/s (1.58 m/s) sits above the measured 1.48 m/s.
• The paper gives the wind direction but not the leg direction. A pure tailwind is assumed.
• The netto filter window (1 s Savitzky-Golay) and Dryden W20 (= mean wind) are assumptions.
• The simulation is point-mass with a first-order attitude lag, not 6-DOF.
• The glide ratio is higher than measured: the model has no tracking losses beyond turbulence and no airframe imperfections.

Phase 2: offline teacher (hypothesis H1)

Module	Content
geneticwing.spline	Clamped B-splines (chains of Bernstein pieces) with initial-state control points
geneticwing.soaring	Soaring problem: turtle-style genome (turn rate, ground speed, path angle), energy fitness, hinge constraints, Deb ranking
geneticwing.benchmarks	S1 single thermal, S2 thermal field to a goal, S3 ridge lift, on the SB-XC with a 15 N motor
geneticwing.evolution	Real-coded GA, the GA of T. Zohdi's course, CMA-ES; equal-budget interface
geneticwing.planning.local	SLSQP on the same genome, single and multi-start, batched finite differences
geneticwing.policies	Reactive orbit law (10 parameters) tuned offline by the Zohdi GA
geneticwing.seeding	Fit a genome to any flight (the law's), to seed the planners
scenarios/ga_tuning.py	GA hyperparameter sweep on training instances (population 50 wins)
scenarios/phase2_teacher.py	The study: 3 scenarios x 3 test instances x 11 methods

Cost is the net onboard energy over the horizon (motor energy minus the specific energy stored), normalized by level cruise on the motor: 1 means cruising on the motor, < 0 means net energy gained from the air. Budget: 20k evaluations, 100k for the @100k rows. Mean over the 3 instances, counting feasible solutions only.

Method	S1 thermal	S2 field to goal	S3 ridge
NLP (straight start)	-2.77	infeasible	-2.11 (1/3)
NLP multi-start	0.96 (2/3)	infeasible	-2.35 (1/3)
GA (tuned, population 50)	0.15	infeasible	-0.73
Zohdi GA	infeasible	infeasible	infeasible
CMA-ES	-0.32	infeasible	-1.92
CMA-ES + NLP	-3.80	infeasible	-2.16
GA @100k	-0.99	0.47	-1.83
CMA-ES @100k	-2.14	0.78	-2.10
Reactive law (Zohdi-tuned)	-4.67	-0.15	-1.98
Law + NLP (seeded)	-6.08	infeasible	0.89
Law + CMA-ES + NLP (seeded)	-4.44	infeasible	-2.05

What this says about H1:

• H1 is not supported as stated. Even tuned, the real-coded GA is the weakest of the working planners at equal budget, and the course's GA (80 % random immigrants each generation) finds nothing feasible in 87 to 159 dimensions.
• Structure beats search. The reactive law, tuned offline Zohdi-style, encodes the orbit the planners must otherwise discover. Fitting a genome to its flight and refining it with the NLP gives the best results on S1 (-6.08, every instance), 30 % more energy than the law alone.
• S2 stays out of reach of the planners at 20k: the law is the only method that soars there without the motor. The fixed 150 s horizon with a terminal goal over 159 genes is the likely culprit; receding horizon is the fix.
• On S3 the law's flight is fitted less accurately (20 m), and the NLP from it ends in a poor optimum; CMA-ES + NLP remains the best there.
• The teacher is a portfolio (law + NLP, the law, CMA-ES + NLP), as planned in the risk table of the design document. The student does not depend on which optimizer produced the data.

Caveats: 3 instances per scenario; point-mass model; the law knows where the lift is, as the planners do.

Phase 3: generative student (hypotheses H2, H3, H4)

Module	Content
geneticwing.benchmarks.s1_family	Randomized single-thermal family (thermal strength, wind, start); heading-frame context and exact left/right mirror
geneticwing.benchmarks.with_margins	Tightened constraints for the teacher (constraint tightening)
geneticwing.student	Conditional flow matching (rectified flow), deterministic regression baseline, learned mutation, smoothing
scenarios/phase3_dataset.py	Teacher dataset: law flight -> fitted genome -> NLP, both orbit modes, 600 train + 80 test instances
scenarios/phase3_student.py	Training and evaluation against the teacher on held-out instances

Teacher: 1263 solutions; 593 instances with both orbit modes, 77 with one. Mean cost -5.43 under the nominal constraints, against -3.97 for the law alone. Students: MLP 4 x 512, 20k steps on CPU, trained on the data and its mirror. Evaluation on 78 held-out instances (teacher mean cost -5.19):

Method	Instances solved	Samples feasible	Mean cost	Gap to teacher	Median time
Regression (deterministic)	0 %	0 %	-	-	1 ms
Flow matching, 64 samples, 1 step	90 %	17 %	0.87	6.11	28 ms
Flow matching, 64 samples, 8 steps	67 %	15 %	-2.06	3.11	49 ms
Flow matching + learned mutations	74 %	16 %	-1.95	3.31	137 ms
Flow matching + mutations + NLP (2000 evals)	76 %	75 %	-2.51	2.72	1.0 s
Reactive law	100 %	100 %	-3.69	1.50	feedback law
NLP from straight flight (2000 evals)	8 %	8 %	1.49	6.95	0.9 s

What this says:

• H3 holds. The regression network averages the clockwise and counter-clockwise orbits into a straight line out of the thermal (red dashes) and never produces a feasible trajectory. Flow matching samples circle in the thermal, in both directions.
• H2 holds only in part. The student plans in 30 to 50 ms on a laptop CPU and finds a feasible soaring trajectory for 67 to 90 % of new instances, but keeps only about 40 % of the teacher's energy gain, and does not yet beat the reactive law it was partly distilled from.
• H4 holds in part. The NLP warm-started by the student solves 76 % of the instances, against 8 % for the same NLP from straight flight, and narrows the gap; learned mutations bring nothing measurable here.
• Feasibility is the bottleneck, as anticipated. A first teacher solved to the nominal constraints sat exactly on them: keeping 99.4 % of the genome's variance (48 principal components) already broke 80 % of its solutions, and that student solved 25 to 40 % of instances with 3 % feasible samples. Solving the teacher with margins (cost: 0.24 of the 5.4 energy units) made its solutions 8 to 16 times more tolerant to gene noise and gave the numbers above; smoothing the samples took feasibility from 2 to 7 % in a diagnostic.

Caveats: about 1.3k teacher solutions for 87 genes is little data; CPU training; point-mass model; the law's time is a simulation cost, as a feedback law it runs in microseconds.

Next steps: 10x more teacher data on the cluster, a transformer over control points (as in DynFly) or latent flow matching, constraint-aware training, DAgger and the self-improvement loop (H6), and the closed-loop evaluation of phase 4.

Usage

pip install -e ".[dev]"
python -m pytest
python scenarios/s0_glide.py --out results
python scenarios/phase2_teacher.py --out results/phase2 --workers 8
python scenarios/phase3_dataset.py --train 600 --test 80
python scenarios/phase3_student.py --steps 20000

Running phase 3 at scale on Savio (Berkeley)

The teacher's dataset is the expensive part (about 20 core-seconds per instance) and splits into independent shards; the students train on GPU. slurm/submit_all.sh chains four jobs: tune the law once, solve the shards (one node each, plus one for the test set), merge, then train and evaluate one student per training-set size (the scaling study). With the defaults, 15k training instances cost about 80 core-hours of CPU and five short GPU jobs.

On a login node, once:

git clone https://github.com/clementdumeril/GeneticWing.git
cd GeneticWing
bash slurm/check_savio.sh      # prints your accounts, partitions, QoS and GPU types
nano slurm/config.sh           # set ACCOUNT, the CPU/GPU partitions and QoS, the Python module
bash slurm/setup_env.sh        # venv in /global/scratch/users/$USER/geneticwing

Then:

bash slurm/submit_all.sh       # law -> shards -> merge -> students
squeue -u $USER
bash slurm/submit_all.sh train # later: retrain only, on the merged dataset

Results land in /global/scratch/users/$USER/geneticwing/phase3/students/n_<size>/ and logs in .../phase3/logs/. Sizes, shard counts, budgets and walltimes are all in slurm/config.sh.

Conventions: ENU frame (z up), SI units, wind is the air-mass velocity. Arrays are (..., N, 3) so populations evaluate in one call.