From mission intent to a verified, authorized plan.
KMAP compiles an operator's objective into constraints, generates and simulates candidate mission plans against a computed spacecraft state, verifies every plan against real engineering limits, and routes it through review and authorization before it becomes an operational plan. Nothing on this page is scripted — every number is produced by the mission compiler, a simulated-annealing planner, a simulator and a Monte Carlo verifier running in your browser. New in v1.0: role-based access control, a two-person review/authorize workflow, a live API console backed by the same engine, and a tamper-evident hash-chained audit trail — all persisted between visits.
What KMAP does
mission intent & requirements
spacecraft, payload & environment
generate feasible schedules
trade mission value vs. resources
test constraints in simulation
recommend with human approval
KMAP mission workflow
AI Mission Compiler · natural-language objective → structured requirement
Type a mission objective. KMAP's compiler parses it for hard constraints (battery reserve, thermal ceiling) and re-derives the requirement — no fixed template.
Planning engine · simulated annealing, not a fixed lookup
Each candidate plan is search-optimized: the planner starts from a heuristic schedule, then runs randomized local search — swapping activities in and out, re-simulating, and keeping improvements — until it converges.
Mission Control · Live What-If Analysis
Adjust priorities. KMAP's optimizer re-selects, re-simulates and re-verifies real candidate plans on every change.
Candidate mission plans · generated + simulated + verified this run
Selected plan timeline
Spacecraft state at plan completion
Why KMAP selected this plan
Engineering verification · nominal
Monte Carlo verification · confidence under uncertainty
Nominal verification checks one trajectory. Monte Carlo verification re-simulates the selected plan hundreds of times with randomized energy/thermal noise and reports the probability each constraint actually holds.
Scenario testing
Re-run the simulator for the selected plan under a stressed environment model.
Review & collaboration · maker-checker before authorization
Engineering Reviewer signs off on the selected plan; only then can the Flight Director authorize it. Anyone can leave a note — comments are attributed and logged to the audit trail.
Human authorization gate
KMAP recommends a plan; the responsible operator remains in control. Authorization requires the Flight Director role and a completed engineering sign-off on this exact plan (two-person rule).
Status: Awaiting human approval.
Version history & audit log · hash-chained
API console · same engine, real requests
v1.0 exposes mission planning as an API. These calls run against the live in-browser engine (no external network) and return exactly what the production endpoint will return.
What KMAP is building toward
v0.5 replaced the greedy planner with a search-based solver and added Monte Carlo confidence estimates and persistence. v1.0–v1.1 layered on role-based access control (Mission Operator, Engineering Reviewer, Flight Director, Viewer), a two-person maker-checker workflow, a documented API surface, expanded export formats and a hash-chained audit log. v1.2 replaces the compiler's local heuristic with an on-demand call to a real language model (via our own server-side proxy to GapGPT) — the "Refine with AI" button in the compiler panel — while keeping the local heuristic as an instant, offline-safe fallback. Next up (Year 2): real-time telemetry ingestion and closed-loop replanning.