Smart Doorbell Camera Kit v13 – Build a Bayesian AI Security Robot on Raspberry Pi 5

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

This isn’t a stationary doorbell camera; it’s a mobile security robot that patrols and reasons about uncertainty. Where typical robots rush past ambiguous sensor readings, this one employs a Bayesian neural network to gauge how confident it is about every detected obstacle. If confidence dips, the robot reroutes conservatively, avoiding blind lunges that could damage hardware or miss an intruder. For engineering students and AI enthusiasts, it’s a real-world lesson in deploying probabilistic models on edge hardware—exactly the kind of project that stands out in your portfolio or during a Smart India Hackathon submission.

What You'll Build

You’ll assemble a four-wheeled robot chassis that hosts a Raspberry Pi 5, RPLidar A1 360° scanner, Pi Camera Module 3, and an NVMe SSD over the M.2 HAT. The core software runs a Bayesian neural network that processes LIDAR point clouds to detect obstacles and quantify prediction uncertainty. When a doorway or hallway corner appears with low confidence scores, the robot defaults to a cautious path—slowing down, widening its turn radius, and relying more on visual camera data. The result is a doorbell security robot that doesn’t just say “something is here,” but also “here’s how sure I am, and here’s the plan accordingly.”

What You'll Learn

• Implement a Bayesian neural network on Raspberry Pi 5 with uncertainty quantification
• Process LIDAR point clouds in real time and fuse sensor data with camera feeds
• Design path planning algorithms that adapt motor commands based on confidence levels
• Integrate NVMe storage for high-speed data logging and model training

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Designed for B.Tech ECE/EEE students, final-year project teams, and robotics club members at IITs, NITs, VIT, or BITS Pilani who want to move beyond line-following robots. It’s ideal for Smart India Hackathon participants building security solutions, or anyone who has worked with Raspberry Pi and Python and is ready to tackle advanced sensor fusion and probabilistic models. The 10-12 hour build assumes you’re comfortable with Linux, motor wiring, and Python libraries like PyTorch or TensorFlow Probability.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.