Whitepaper v1.0
A 23-page protocol description: seven principles, hex topology, AACP, DFR, comparison with classical ATC.
Open the whitepaper →If you're prototyping a fleet, a flight controller, an air-traffic simulator, or a research tool — here's how the protocol meets you today, and what's already running behind closed doors.
The protocol is fully described in published material — enough to start evaluating, prototyping against your own stack, or citing in a paper. The Python MVP is built but not yet publicly distributed (see §2 below).
A 23-page protocol description: seven principles, hex topology, AACP, DFR, comparison with classical ATC.
Open the whitepaper →Three technical essays — altitude as information, hex-cell capacity, AACP boundaries.
Browse the blog →Two non-provisional applications (19/551,620 + 19/551,624) — pending examination, publishable Aug 2027.
View filing details →A working Python reference implementation exists today — message parser, 4D reservation primitives, deterministic fallback resolver, and a two-vehicle conflict simulator, with a full unit-test suite. Public distribution is on hold pending the licensing decision; early access is available to qualified teams on request.
Reference parser, reservation primitives, DFR resolver, 2-vehicle simulator
Browser/Node.js client SDK, integration with web tooling
Embedded SDK for flight controllers (PX4, ArduPilot)
High-throughput cloud workers, fleet operator backends
Snippets below are from the reference implementation that currently sits behind early-access. Test counts are real — request the SDK to run the full suite locally.
def resolve_via_dfr(a: NegotiateInit, b: NegotiateInit) -> str:
"""Both vehicles compute the same resolution.
Returns vehicle_id of the YIELDING vehicle.
Hierarchy:
1. Mission priority class
2. Remaining energy level
3. Current heading value
4. Vehicle identifier (lexicographic)
"""
rank_a = PRIORITY_RANK[a.mission_priority]
rank_b = PRIORITY_RANK[b.mission_priority]
if rank_a != rank_b:
return b.vehicle_id if rank_a < rank_b else a.vehicle_id
if a.energy_remaining_pct != b.energy_remaining_pct:
return (a.vehicle_id
if a.energy_remaining_pct > b.energy_remaining_pct
else b.vehicle_id)
if a.current_heading_deg != b.current_heading_deg:
return (a.vehicle_id
if a.current_heading_deg < b.current_heading_deg
else b.vehicle_id)
if a.vehicle_id != b.vehicle_id:
return a.vehicle_id if a.vehicle_id < b.vehicle_id else b.vehicle_id
return a.vehicle_id # degenerate case — same identityBuilt capabilities reflect the Python MVP in its current form. Specifications and scope subject to change pending USPTO examination.
AACP Layer 1 (Intent & State Broadcasting) and Layer 2 (Peer-to-Peer Negotiation) message schemas, pydantic-validated.
Hex-cell × floor × direction × time-slice reservations, with constant-cost cell-occupancy lookup and capacity enforcement.
Four-level DFR hierarchy (mission priority → energy → heading → vehicle ID), determinism-tested for symmetric resolution.
End-to-end scenario: conflict detection → Layer 1 broadcast → Layer 2 negotiation → DFR fallback → resolution.
Sky-floor mapping (0–150m AGL, six classes), six 60°-separated direction bands with tolerance, per-floor sub-layer math.
Five compliance levels (Basic / Standard / Advanced / Trusted / Full) modelled in schemas. Layers 3–5 not in scope of the MVP and ship in a later phase.
Scale beyond two-vehicle scenarios, asymmetric traffic patterns, capacity-saturated cells. Planned for a later phase.
AACP Layer 4 (Interoperability & Trust) and Layer 5 (Explainability & Audit) — hash-chained decision logging. Planned for a later phase.
The Python MVP is not yet on a public repository. Qualified teams — researchers, fleet operators, eVTOL programmes, regulators — can request pre-release access. Tell us what you're building, your fleet size or research scope, and your integration target. Priority goes to use cases whose feedback shapes the public release.