Axis Panels
Optimized tracker installations with AI-ready hardware. From ESP32 panel nodes to cloud optimization—a modular stack designed for long-term intelligence.
What This Module Does
Axis Panels standardizes the hardware and control stack for agrivoltaic tracking systems, giving developers and EPCs a repeatable, AI-ready pattern.
Panel-Level Sensing
ESP32 with inclinometer and actuator control at each structure
Cluster Coordination
Raspberry Pi managing ~10 panel nodes with local safety logic
Cloud Optimization
AWS IoT backend running ML models and trajectory computation
Three-Layer Architecture
Our control stack separates concerns across three levels, keeping critical real-time behavior close to hardware while enabling cloud-based optimization.
High Level
Decides desired orientation trajectory given weather, agronomic constraints, and energy objectives. Runs heavy ML models.
Mid Level
Receives setpoints, interpolates, monitors compliance, and aggregates data for uplink to cloud.
Low Level
Runs local PID control loops, reads sensors, drives actuators with safety constraints.
System Architecture
Cloud Computing System
AWS IoT + Application Backend
Cluster Controller
Raspberry Pi (~10 panels/cluster)
Panel Node
ESP32 + Sensor + Actuator
Hardware Building Blocks
Modular components that scale from single panels to utility-scale installations
Panel Node Components
Panel Node (ESP32)
- ESP32 microcontroller board
- Tilt angle sensor (inclinometer/encoder)
- Motor driver (H-bridge)
- Linear or rotary actuator
- Local wiring and connectors
Cluster Controller (Raspberry Pi)
- Handles ~10 ESP32 panel nodes
- WiFi/Ethernet connectivity
- Local safety logic
- Setpoint forwarding
- Telemetry aggregation
Power Electronics
- DC power system for actuators
- DC-DC converters (5V logic supply)
- Panel string or dedicated supply
- Overcurrent protection
Network & Data Flow
Bidirectional communication from cloud to actuator and back
Real-Time Data Flow
Command Flow (Downlink)
- 1. Cloud pulls weather forecasts and computes optimal trajectories
- 2. Pushes per-cluster setpoints via AWS IoT (MQTT/HTTPS)
- 3. Raspberry Pi broadcasts to ESP32 nodes over WiFi
- 4. ESP32 runs PID to move actuator to target angle
Telemetry Flow (Uplink)
- 1. ESP32 reads inclinometer values continuously
- 2. Reports measured angles and status to Raspberry Pi
- 3. Raspberry Pi aggregates cluster telemetry
- 4. Uploads to cloud for monitoring and ML retraining
Control Split: High vs Low Level
High Level (Cloud)
- Weather and irradiance forecasting
- Global trajectory optimization
- Coordinate entire sites or fleets
- Run heavy ML models
- Store historical data
Mid Level (Raspberry Pi)
- Receive setpoints every few minutes
- Interpolate between setpoints
- Monitor panel node compliance
- Aggregate telemetry data
- Local emergency stops
Low Level (ESP32)
- Run PID control loops
- Read inclinometer values
- Drive actuator via H-bridge
- Travel limit enforcement
- Stall detection
Why It Matters
AI-Ready
Built for ML optimization from day one, not retrofitted later
Modular
Repeatable spec for EPCs and hardware vendors
Transparent
No opaque black-box controllers—you own the logic
Upgradeable
Hardware stays constant, optimization gets smarter over time
Ready to Build?
Get the hardware specifications and integration guide.