RoboSustain

Visual Abstract
Solution
Technology Used
Impact
RoboSustain Robot Prototype

Inspiration

Buildings account for 30% of global energy consumption and 27% of carbon emissions worldwide. Hidden infrastructure faults gas leaks, water infiltration, and thermal bridges silently inflate energy waste by 20–50% with no visible symptoms. Water losses alone cost $6.4 billion annually in the U.S., with 6 billion gallons leaked daily. The breaking point for us was a simple question:

Why do we still rely on expensive, destructive, manual inspections when autonomous AI can do it faster, cheaper, and smarter?

Current solutions cost $500+ per inspection and leave building owners with no repair guidance. We were inspired to close the 9-point building decarbonization gap to build something autonomous, affordable, and truly sustainable RoboSustain.

What it does

RoboSustain is a fully integrated, AI-powered robotic system for sustainable building diagnostics. Here's what it delivers:

🤖 Autonomous Robot — Navigates buildings independently or via Control App, scanning for hidden infrastructure faults in real time with less than 4 second latency
📊 Live PWA Dashboard — Displays live tracking, temperature, humidity, gas readings, and solar battery status in real time
🔥 AI Model 1 — Vision & Diagnostics — Converts standard RGB camera images into thermal heatmaps, performs object detection, estimates distance, and localizes defects with bounding boxes, achieving 92% testing accuracy
🧠 AI Model 2 — Expert Consultant — Triggered on Emergency Action, analyzes sensor readings and recommends eco-friendly, high-efficiency repair materials with images, properties, and step-by-step installation instructions
⚡ Energy Impact — Targets up to 50% reduction in hidden energy waste, directly contributing to building decarbonization | Metric | Value | | Overall System Accuracy | ~92% | | Gas & Water Detection | 90% | | Temperature Monitoring | 95% | | Response Time | 2–4 seconds | | Hardware Cost | ~$150–200 |

How I built it

🤖 Robot Hardware The robot is built on a dual-controller architecture using Arduino Mega and ESP32. It integrates an MQ-7 gas sensor, DHT11 for temperature and humidity, a Raindrop moisture sensor, and HC-SR04 ultrasonic sensors for navigation. Vision is handled by an ESP32-CAM with servo-actuated positioning, and the entire system runs on a solar-regulated Li-ion battery for sustainable operation.

🧠 AI Models Model 1 uses a pipeline of CNN / YOLOv8 / MobileNet — images are preprocessed at 224×224px, then passed through defect detection, generating a thermal heatmap and a structured diagnosis covering Problem, Cause, Risk, and Fix.

Model 2 is an LLM-based bilingual chatbot supporting Arabic and English, fine-tuned for material recommendation and verified location matching.

💻 PWA Stack

Frontend: HTML/CSS/JS dashboard hosted on Vercel with real-time polling every 8 seconds
Backend: PHP RESTful APIs with JSON flat-file storage and ImgBB for image hosting
AI Layer: Sensor Fusion + Vision AI + Bilingual Chatbot

Challenges I ran into

High cost of thermal imaging Thermal cameras cost $500+ and their use is often destructive. We solved this by training an AI model to convert regular RGB images into accurate thermal heatmaps, eliminating the need for specialized hardware entirely.

Real-time multi-sensor fusion Synchronizing MQ-7, DHT11, Raindrop, and ultrasonic sensors with live camera vision under sub-4-second latency required careful hierarchical bus architecture design on the ESP32.

Model generalization We validated with seen vs. unseen locations and diverse slang queries to ensure the models perform well outside training data, not just on it.

Hardware cost vs. performance Achieving over 90% detection accuracy at a ~$150–200 total system cost compared to $500+ for conventional tools required careful component selection at every step.

Building a bilingual expert system Making Model 2 understand both Arabic and English queries with 90% intent recognition accuracy while providing contextually correct material recommendations was a significant NLP challenge.

Accomplishments that I'm proud of

✅ Achieved 92% overall system accuracy with only 2–4 second response time at a fraction of conventional inspection costs
✅ Built a working RGB-to-Thermal conversion AI that replaces $500+ thermal cameras with a pure software solution
✅ Deployed a fully functional PWA dashboard with live robot tracking, real-time sensor feeds, and AI-generated alerts
✅ Developed a bilingual AI expert consultant in Arabic and English with 93% location matching accuracy and 90% intent recognition
✅ Validated the system with 50 real trial users in a human-centric assessment
✅ Directly aligned with 5 UN SDGs: SDG 7, 9, 11, 12, and 13
✅ Built Green Rescue Academy, an educational game that teaches sustainability and real engineering concepts through gamified missions

What I learned

Real engineering lives in trade-offs — balancing cost, accuracy, latency, and sustainability required constant iteration at every layer of the system
AI is only as good as its integration — connecting models to real hardware in real time taught us the difference between research accuracy and production reliability
Sensor fusion is an art — getting multiple heterogeneous sensors to agree on a single ground truth required deep understanding of each sensor's failure modes
Sustainability is a system problem — solving energy waste requires combining hardware, software, AI, UX, and business thinking together, not just any one of them
Designing for inclusion — making professional-grade diagnostics accessible to DIY users and emerging markets forced us to rethink every cost decision

What's next for RoboSustain

Phase 1 — Enhanced Sensing 🔬 Upgrade to MLX90614 infrared sensors for true thermal mapping and integrate the Raspberry Pi AI Camera for higher-resolution vision.

Phase 2 — Hybrid Mobility 🚁 Add aerial drone capability for external envelope scanning and enable coordinated ground and aerial inspection missions.

Phase 3 — Global Intelligence 🌍 Connect to satellite and climate data for regional risk mapping and build a global network of building diagnostics intelligence.

Phase 4 — RoboSustain Hub 🏪 Launch a Green Marketplace connecting users to verified eco-friendly material vendors with ratings and geolocation. The AI will automatically vet stores meeting our environmental standards, evolving into the RoboSustain Store a one-stop shop for all sustainable repair solutions.

Our mission: make building diagnostics smart, accessible, and fully sustainable — reducing hidden energy waste by up to 50% and turning every repair into an eco-conscious decision.