Head-Controlled Drone Navigation System
Inspiration
The idea for this project came from wanting to explore more intuitive ways to interact with robotics. We'd been working with Crazyflie drones for coursework and had some experience with computer vision through MediaPipe, and we started thinking about alternative control schemes that didn't rely on traditional joysticks or controllers. Head tracking seemed like a natural fit—it's intuitive, hands-free, and could potentially make drone operation more accessible. We wanted to see if we could create a system where controlling a drone felt as simple as looking where you want it to go.
What We Learned
This project pushed us to understand real-time system integration in a way we hadn't experienced before. Working with physical hardware that's actually flying meant we couldn't afford sloppy timing or untested assumptions. The Crazyflie requires precise command spacing to maintain stable flight, which taught us a lot about rate limiting and state management.
We gained practical experience with MediaPipe's face mesh detection, learning how to extract meaningful data from the 468 facial landmarks it provides. Most of our work involved identifying which landmarks actually mattered for detecting head orientation and filtering out noise from natural micro-movements.
The calibration challenge was unexpected but valuable. We learned that user-specific baselines are essential for gesture recognition systems—what works for one person's posture doesn't necessarily work for another's. Building a robust calibration system showed us the importance of designing for real users, not ideal conditions.
How We Built It
System Architecture
The system integrates three core components:
Vision Processing Module
We used OpenCV to capture video frames from a webcam and processed them through MediaPipe's Face Mesh model. The key was identifying which facial landmarks provided reliable head orientation data. We focused on tracking the nose tip relative to the face center, with additional reference points on the forehead, chin, and cheeks for stability.
Gesture Recognition Engine
To determine head tilt, we calculated the offset between the nose position and face center:
$$\Delta y = y_{\text{nose}} - y_{\text{center}}$$ $$\Delta x = x_{\text{nose}} - x_{\text{center}}$$
Since everyone's neutral head position differs, we implemented a calibration system that measures baseline offsets. The adjusted values relative to neutral become:
$$\Delta y_{\text{adj}} = \Delta y - \Delta y_{\text{neutral}}$$ $$\Delta x_{\text{adj}} = \Delta x - \Delta x_{\text{neutral}}$$
We used threshold-based classification to map adjusted offsets to commands:
- Up: Δy_adj < -15
- Down: Δy_adj > 15
- Left: Δx_adj < -15
- Right: Δx_adj > 15
Drone Control Interface
The Crazyflie library's SyncCrazyflie and MotionCommander APIs handled the low-level radio communication and flight control. We implemented command rate limiting with a 1-second delay between movements to prevent flight instability from rapid command sequences.
Implementation Approach
We structured the code around a single event loop that processes video frames and sends drone commands sequentially. The SyncCrazyflie context manager ensures proper connection initialization and cleanup:
with SyncCrazyflie(uri, cf=Crazyflie(rw_cache='./cache')) as scf:
mc = MotionCommander(scf, default_height=0.5)
# Main control loop
Challenges We Faced
Command Timing and Flight Stability
The most critical issue was managing command frequency. Our initial implementation sent commands as soon as head tilts were detected, which overwhelmed the drone's control system and caused erratic flight behavior. We solved this by implementing strict rate limiting—only one command is processed per second, with state tracking to prevent duplicate commands for sustained head tilts.
Architecture Simplification
We initially designed a multi-threaded system with separate threads for video processing and drone control. This introduced race conditions where commands could be sent during incomplete movements or concurrent connection access. After debugging these timing issues, we simplified to a single-threaded architecture that processes frames and sends commands sequentially, which eliminated the synchronization problems.
User Calibration
Different users have different natural head positions, and our initial hardcoded thresholds didn't account for individual variation. We added an explicit calibration step where users define their neutral position, which dramatically improved detection accuracy across different people.
Sensitivity Tuning
Finding the right threshold for tilt detection required significant experimentation. Values that were too low (10 pixels) triggered on natural micro-movements, while values too high (25 pixels) required exaggerated gestures. We settled on 15 pixels as a threshold that reliably detects intentional tilts while ignoring involuntary movements.
Hardware Dependencies
The Crazyflie requires a positioning deck (lighthouse or flow deck) for stable flight. We added deck detection callbacks and status verification before allowing takeoff to prevent flight instability from missing hardware.
Results
The final system maintains 30 FPS video processing while controlling the drone with high reliability after calibration. It includes safety features like emergency landing, connection monitoring, and visual feedback showing real-time offset values and detected gestures. The interface provides clear status indicators for calibration state and flight status.
Future Development
Given more time, we'd explore additional gesture mappings for forward/backward movement, implement machine learning for personalized gesture profiles, add trajectory smoothing to reduce jitter, and integrate the drone's onboard sensors for autonomous obstacle avoidance.
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