🔑 How It Works
🔑 How It Works
- Sensors feed the onboard compute:
- GPS gives global position (horizontal), IMU gives gravity alignment and slope, stereo camera / LiDAR builds 3D map, wheel odometry tracks motion.
- Compute fuses the data:
- EKF/UKF sensor fusion creates robust localization even if GPS drops or vision fails.
- SLAM builds/update a 3D terrain map.
- Path Planning & AI:
- Generates efficient mowing paths, handles multi-zone mowing, and dynamically avoids obstacles.
- Uses reinforcement learning to adapt to seasonal changes or lawn growth patterns.
- Actuation & Safety:
- Compute sends motor commands for wheels and blade deck.
- Safety systems (tilt, lift, bumper, LiDAR) override motors if danger detected.
- User Interface & Cloud:
- Mobile app allows remote control, visualization, scheduling.
- OTA updates improve algorithms and maps over time.
⚡ Competitive Advantages vs Toro Haven
| Feature | Toro Haven | Next-Gen Stack |
|---|---|---|
| Sensor Fusion | Single forward camera + GPS | GPS + IMU + Stereo Vision ± LiDAR + wheel odometry |
| Slope / Gravity Awareness | Limited | Full IMU + fusion stack |
| Obstacle Detection | Camera-based | Vision + optional LiDAR + collision safety |
| Adaptivity | Limited | Reinforcement learning, map updates, OTA |
| Reliability | Medium | High (multi-sensor redundancy, EKF fusion) |
| Design | Premium aesthetic | Industrial/techy, focused on function but can be refined |
also sketch a simplified budget + BOM (Bill of Materials) for this stack, showing which sensors and compute units to pick and approximate cost per unit — basically a ready-to-go “Haven competitor blueprint” from hardware perspective.
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