Auto-Aiming Pan/Tilt Turret

Building a responsive electromechanical tracker requires closing the loop between perception latency and actuator bandwidth. Detection delay, network/serial latency, and mechanical lag all compound into pointing error.

• Sustain ~60 FPS object tracking
• Keep detection latency under 50 ms
• Keep motor command latency under 10 ms
• Support predictive tracking and manual override
• Expose live PID tuning through a dashboard

Pipeline:

1. Camera capture → YOLOv8 detection
2. Bounding-box centroid extraction
3. Kalman filter prediction of target motion
4. PID computation of pan/tilt setpoints
5. Serial commands to Teensy 4.1 actuators

A Flask dashboard provides mode selection, live PID gains, and system monitoring.

Two-axis pan/tilt mechanics driven by motors controlled from a Teensy 4.1. Homing and motion-stabilization routines keep the platform calibrated for closed-loop tracking.

• Vision process runs detection and filtering
• Control process issues low-latency actuator commands
• Dashboard process exposes telemetry and tuning controls
• Manual and automatic modes share the same command path

• Kalman filtering to bridge detection gaps and reduce jitter
• Embedded PID on Teensy for deterministic actuator timing
• Flask dashboard for rapid experiment iteration

Validated tracking frame rate, detection latency, and command latency during live target motion. Homing and stabilization routines were exercised before closed-loop runs.

Achieved approximately 60 FPS object tracking with less than 50 ms detection latency and less than 10 ms motor-command latency on the Teensy actuator path.

• Balancing model accuracy against latency
• Mechanical backlash and axis coupling
• Keeping serial command streams non-blocking

End-to-end latency budgeting matters more than peak FPS. Predictive filtering significantly improved smoothness when detections momentarily dropped.

• Multi-target selection
• On-device inference optimization
• Improved mechanical stiffness and encoder feedback