Raspberry Pi 5 Multi-Objective Robot That Learns Speed, Safety, and Energy Trade-Offs

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 kit puts you in the operator’s seat of an autonomous robot that doesn’t just navigate — it optimises. Using NSGA-III, a state-of-the-art evolutionary algorithm, the Pi 5 simultaneously learns a family of policies that span the trade-off curve between velocity, collision avoidance, and battery consumption. Once the Pareto front is computed, you select the exact balance your application demands, from aggressive speed to ultra‑safe low‑energy patrol. Every line of training and inference runs directly on the onboard 8GB Pi 5, accelerated by NVMe storage.

What You'll Build

You assemble a differential-drive mobile platform with encoder feedback and an IMU, then deploy a custom multi-objective RL stack that runs NSGA-III on the Cortex‑A76 cores. The robot learns to maximise forward speed while minimising energy per meter and collision risk, presenting you with a visual Pareto front on any device connected to its Wi‑Fi hotspot. You’ll then command the robot to instantly switch between policies — sprint across a room, crawl with minimal power, or maintain a safe distance from obstacles — all while the Pi 5 tracks and logs each decision.

What You'll Learn

• Implement NSGA-III multi-objective optimisation in Python and deploy on Raspberry Pi 5
• Design a reinforcement learning reward function that encodes speed, safety, and energy as competing objectives
• Fuse MPU9250 IMU and encoder odometry for real‑time state estimation and safety‑aware control
• Benchmark edge‑AI training loops using NVMe SSD storage to eliminate I/O bottlenecks

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Final‑year B.Tech students in ECE, EEE, or CSE tackling multi‑objective robotics for their capstone project. Research interns at IITs, NITs, VIT, or BITS Pilani running edge‑AI experiments on a real mobile platform. Smart India Hackathon hardware‑track teams that need a reproducible RL benchmark out of the box. The kit assumes comfort with Python, Linux, and basic reinforcement learning concepts — it rewards deep exploration, not first‑time tinkering.

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.