Manufacturing QC Vision Kit v14 — Bayesian Neural Network 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.

Assemble a mobile robot equipped with a Raspberry Pi 5, RPLidar A1, and Pi Camera Module 3 to run a Bayesian neural network that quantifies its own uncertainty in obstacle detection. When confidence drops, the robot takes conservative paths—critical for real-world manufacturing QC where a single collision can halt a production line.

What You'll Build

You'll build a fully autonomous robot that navigates dynamic factory floors, using a deep learning model that not only classifies obstacles but also estimates the probability of being wrong. The robot plans safe detours around uncertain detections, mimicking the caution of a human inspector.

What You'll Learn

• Train and deploy a Bayesian neural network on a Raspberry Pi 5 using PyTorch and MCMC approximation
• Integrate RPLidar A1 data with camera input for sensor fusion
• Implement uncertainty-aware cost maps and DWA path planning
• Set up NVMe SSD for fast model inference and dataset storage

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit is designed for final-year B.Tech students in ECE, EEE, or CSE at IITs, NITs, VIT, BITS, and other engineering colleges tackling computer vision and robotics projects. It's also ideal for participants in Smart India Hackathon or DRDO/ISRO internship prototypes requiring AI-driven quality inspection on mobile platforms.

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.