{"product_id":"kit-pi-5-multi-objective-robot-optimisation","title":"Pi 5 Multi Objective Robot Optimisation","description":"\u003ch1\u003eRaspberry Pi 5 Multi-Objective Robot That Learns Speed, Safety, and Energy Trade-Offs\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 10-12 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Multi-Objective Reinforcement Learning on Edge Silicon\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eThis 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eImplement NSGA-III multi-objective optimisation in Python and deploy on Raspberry Pi 5\u003c\/li\u003e\n  \u003cli\u003eDesign a reinforcement learning reward function that encodes speed, safety, and energy as competing objectives\u003c\/li\u003e\n  \u003cli\u003eFuse MPU9250 IMU and encoder odometry for real‑time state estimation and safety‑aware control\u003c\/li\u003e\n  \u003cli\u003eBenchmark edge‑AI training loops using NVMe SSD storage to eliminate I\/O bottlenecks\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 8GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 512GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCytron Motor Driver\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDC Motor with Encoder\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRobot Chassis\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMPU9250 IMU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e20\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eFinal‑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.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery 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.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eOpen the AI companion for contextual debugging; if it can’t resolve the issue, reach our engineers directly via WhatsApp. We typically respond within an hour during Indian working hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior experience with NSGA-III or multi‑objective RL?\u003c\/summary\u003e\u003cp\u003eFamiliarity with single‑objective RL and Python is expected. The AI companion walks you through the NSGA‑III implementation step‑by‑step, from crossover operators to crowding distance, so advanced concepts become manageable.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan this kit be used for my B.Tech final‑year project?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The hardware and software stack produce a full research‑grade result — Pareto fronts, learned policies, and a physical demonstrator. Many universities accept projects built around this kit’s core challenge: real‑world multi‑objective optimisation on edge hardware.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow do I switch between different trade‑off policies after training?\u003c\/summary\u003e\u003cp\u003eThe robot’s onboard web interface lets you drag a slider along the Pareto front. Underneath, the Pi 5 loads the corresponding policy weights from SSD and instantly adjusts motor behaviour to match your chosen speed‑safety‑energy balance.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eNSGA-III multi-objective RL on Pi 5 finds Pareto front between speed, safety and energy — operator selects preferred trade-off.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-8gb-high-performance-single-board-computer\"\u003eRaspberry Pi 5 8GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 512GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eCytron Motor Driver\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/l293d-motor-driver-shield-for-arduino-drive-dc-stepper-motors\"\u003eDC Motor with Encoder\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/2-wheel-smart-car-robot-chassis-kit-diy-for-arduino-raspberry-pi\"\u003eRobot Chassis\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/mpu9250-9-axis-sensor-module-gyro-accel-magnetometer\"\u003eMPU9250 IMU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x20\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Multi Objective Robot Optimisation?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Multi Objective Robot Optimisation includes all components needed: Raspberry Pi 5 8GB, NVMe SSD 512GB, Pi 5 M.2 HAT+, Cytron Motor Driver, DC Motor with Encoder and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Multi Objective Robot Optimisation?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. NSGA-III multi-objective RL on Pi 5 finds Pareto front between speed, safety and energy — operator selects preferred trade-off. Estimated build time is 10-12 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Multi Objective Robot Optimisation online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Multi Objective Robot Optimisation is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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