Perfect! Here’s a single-page Startup Blueprint combining the key elements — it’s essentially a visual & conceptual “cheat sheet” for building a drone-grade AI mower from scratch.

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🌿 Drone-Grade AI Mower: Startup Blueprint

┌─────────────────────────────────────────────┐
│                USER INTERFACE               │
│ - Mobile App: iOS/Android                   │
│ - Scheduling, zone management               │
│ - Maintenance alerts & OTA updates          │
└─────────────────────────┬──────────────────┘
                          │ Cloud sync
                          ▼
┌─────────────────────────────────────────────┐
│               CLOUD BACKEND                 │
│ - Map storage & telemetry analytics         │
│ - Continuous AI tuning & fleet learning    │
└───────────────┬─────────────────────────────┘
                │
                ▼
┌─────────────────────────────────────────────┐
│               ONBOARD COMPUTE               │
│ - NVIDIA Jetson Orin / NX                   │
│ - Sensor fusion engine (IMU + GPS + camera)│
│ - SLAM / 3D mapping / path planning        │
│ - Obstacle detection & AI classification   │
└───────────────┬─────────────────────────────┘
                │
   ┌────────────┼─────────────┬─────────────┐
   ▼            ▼             ▼             ▼
┌─────────┐ ┌───────────┐ ┌─────────┐ ┌─────────┐
│ Sensors │ │ Actuation │ │ Safety  │ │ Chassis │
│ - RTK   │ │ - Wheel   │ │ - Lift/ │ │ - Mowing│
│   GPS   │ │   drives  │ │   tilt  │ │   deck  │
│ - IMU   │ │ - Blades  │ │ - Collision│ - Wheels│
│ - Stereo│ │   motor   │ │   detect │ - Battery│
│   camera│ └───────────┘ └─────────┘ │ - Encl. │
│ - LiDAR │                        └─────────┘
└─────────┘

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🔹 Disruption Map Highlights

Challenge	Drone-Grade Tech Solution
Gravity alignment / slope	Visual-inertial odometry + stereo depth + IMU fusion
Vertical drift / IMU drift	SLAM + camera features + wheel odometry
GPS unreliability	RTK GPS + sensor fusion + visual landmarks
Obstacle detection	Stereo + optional LiDAR + AI classification
Terrain mapping / coverage	3D map via SLAM + depth sensing
Auto-calibration / reliability	Automated IMU calibration with visual cues
Updates / adaptability	Cloud backend + OTA updates + AI path optimization

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🗓 18–24 Month Roadmap

Phase	Duration	Goal
Planning & Feasibility	1–2 mo	Tech stack, sensors, chassis selection
Prototype Electronics & Compute	2–3 mo	Sensor integration, motor tests, safety interlocks
Core Perception Software	4–5 mo	SLAM, VIO, sensor fusion, slope & drift correction
Obstacle Detection & Path Planning	3–4 mo	AI-based obstacle detection, optimized mowing paths
Safety & Auto-Calibration	2 mo	Tilt/lift cutoffs, IMU auto-calibration, fail-safes
Cloud & OTA Integration	2 mo	Backend for maps, telemetry, AI updates
Field Testing & Iteration	2–3 mo	Full terrain testing, SLAM tuning, obstacle refinement
Pilot Production & Go-to-Market	2–3 mo	Refined chassis, app finalization, small-scale launch

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🔹 Key Takeaways

1. Software-first approach is the main competitive edge.
2. Hardware can be off-the-shelf, just robust and reliable.
3. Mature drone/robotics tech solves the perception and navigation gap incumbents ignore.
4. Market window is open — Toro, Husqvarna, and others are behind on true autonomy.
5. Result: Fully autonomous, slope-aware, drift-free, obstacle-smart mower in ~18–24 months.

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If you like, I can also create a fully visual “one-page infographic” version of this blueprint, combining stack, disruption map, and roadmap into a single diagram that looks like a startup-ready poster. It would be easy to share or use as a planning document.