Semidirectional YOLO-Based Aquatic Unit (SYBAU)

Inspiration

Imagine jokingly trolling your friend with a face-tracking water gun — that playful idea sparked our exploration into embodied AI, systems that not only perceive the world but physically interact with it in meaningful ways. The project began as a fun concept brainstormed over dinner: a face-tracking system capable of directing a high-pressure water stream. As development progressed, the idea evolved beyond novelty into a broader investigation of human-centered applications, where we believe in a strong use-case for productivity/education, allowing everyone to "lock-in" for their meetings with a fresh blast of water. How We Built It SYBAU combines real-time computer vision, robotic arm manipulation, and high-pressure fluid control into a modular platform. Vision and AI We used YOLOv8-medium for real-time object detection and tracking, running locally on an NVIDIA RTX 4070 GPU to enable low-latency edge inference. A 360° fisheye camera provides wide environmental awareness, allowing the system to detect targets anywhere within its field of view. The detection pipeline includes: Image acquisition from the fisheye camera Object detection using YOLOv8 Target selection and tracking Conversion of bounding box coordinates into servo motion commands A real-time feedback control loop that continuously adjusts the robotic arm to keep the target (e.g., a face) centered in the frame Servo positioning maps image-space coordinates into angular control: $$ \theta_x = f_x(u), \quad \theta_y = f_y(v) $$ where (u,v) are the detected object coordinates in image space. Mechanical and Control Systems The physical actuation system consists of: Dynamixel servos (daisy-chained) for smooth pan/tilt targeting OpenRB-150 microcontroller running low-level Arduino firmware for deterministic control A 12V diaphragm water pump (RV/marine-grade) A 12V relay to safely switch pump power Interchangeable nozzle tips for different water stream profiles Custom 3D-printed mount The architecture separates perception and control: High-level edge AI and computer vision are implemented in Python on the GPU Low-level firmware on the microcontroller handles real-time servo positioning and relay switching This separation allows fast visual inference while maintaining stable, responsive hardware control through a closed-loop feedback system. Challenges We Faced Selecting and sourcing the right materials and components to balance durability, safety, and performance Calibrating the fisheye camera and compensating for lens distortion in the vision pipeline Interfacing high-level AI software with low-level embedded firmware Designing reliable wiring and power systems for stable hardware operation Ensuring mechanical alignment so all moving parts operate smoothly as an integrated system Working with wide-angle optics and translating distorted image coordinates into accurate servo control Designing and CAD-ing custom 3D-printed parts robust enough to withstand motion, water pressure, and repeated use What We Learned How to design and integrate a complete moving robotic system combining AI, electronics, and mechanical engineering Practical lessons in hardware reliability, real-time control, and system integration How to build and tune a fully functional AI-controlled water delivery system Future Directions While currently focused on face tracking, SYBAU can generalize to many detection tasks in the future. Potential applications include: Fire detection and suppression assistance Agricultural or home pest mitigation Interactive entertainment and amusement use cases, and haptic feedback for movies etc.