Design and Develop a Smart Mobile Medical-Waste Collection and Segregation System
“Hospitals generate massive amounts of dangerous medical waste that is currently sorted by hand, putting workers at high risk of injury and infection. This problem asks you to design a smart, battery-powered autonomous robot that uses AI vision to collect, identify, and sort medical waste into correct compartments safely and digitally track it.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
An autonomous mobile robotic system integrated with AI-powered waste classification, automated mechanical segregation compartments, and a cloud-based dashboard for end-to-end biomedical waste tracking, designed completely in Autodesk Fusion.
WHO IS IT FOR?
Primary beneficiaries include hospital sanitation workers, nursing staff, and hospital administrators. The regulatory bodies (like State Pollution Control Boards) and patients also benefit from safer disposal and compliance.
WHY DOES IT EXIST?
Manual biomedical waste handling is prone to human error, needle-stick injuries, and hazardous exposure. Lack of real-time tracking leads to illegal dumping, non-compliance with Bio-Medical Waste Management rules, and severe environmental/health hazards.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Live camera feed from the mobile robot, ultrasonic/LiDAR proximity data, weight sensor readings, hospital zone routing maps, and operator commands via web interface.
Computer vision (YOLO-based object detection) classifies waste items into categories (e.g., infectious, sharps, plastic, general). Path planning algorithms navigate the bot autonomously. Digital twin state machine logs weight and category into a central database.
Automated mechanical sorting into dedicated bins, real-time tracking dashboard showing fill levels and compliance logs, automated audit reports, and instant hazard alerts for overflow or improper disposal.
CONSTRAINTS
- Must be designed within Autodesk Fusion for the grand finale following strict DFM (Design for Manufacturing) rules
- Low latency AI inference running on edge hardware (e.g., Raspberry Pi or Jetson Nano)
- Battery-electric power management system requiring optimal weight distribution and energy efficiency
- Strict adherence to biomedical waste segregation color-coding standards (Yellow, Red, White, Blue)
JURY METRICS
- Waste classification accuracy and precision (>95%)
- Autodesk Fusion model quality, manufacturability, and inclusion of Generative Design / Motion Studies
- System response time for sorting actuation (<2 seconds)
- User interface simplicity, dashboard responsiveness, and completeness of traceability logs
CORE BOTTLENECK
Integrating the mechanical flap/compartment sorting mechanism tightly with real-time computer vision inference on an edge device without mechanical jamming or slow cycle times.
Why You Should Choose This PS (Pros)
- Sponsored by Autodesk, making alignment with Autodesk Fusion criteria essential and highly rewarding if executed well
- High social impact addressing sanitation worker safety and hospital hygiene
- Clear hardware-software hybrid scope combining CAD design and software dashboards
Challenges & Risks to Watch (Cons)
- Requires bridging physical mechanical design (Fusion) with software logic (AI and web dashboards) under tight deadlines
- Strict restriction against pre-designed CAD files means everything must be built live during the finale
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
Design and develop an AI-powered, battery-electric autonomous mobile system that automates the collection, identification, segregation, and digital tracking of biomedical waste across hospitals. The solution should leverage AI-enabled vision systems to classify waste, intelligently segregate it into designated compartments, and provide end-to-end traceability while minimizing human exposure to hazardous materials and improving safety, operational efficiency, and regulatory compliance.
Using Autodesk Fusion, students must demonstrate a complete product development lifecycle—from concept ideation to manufacturing-ready product, delivering an innovative, scalable, and solution for next-generation healthcare waste management.
For Grand Finale:
Students must use Autodesk Fusion within the given time period and present:
Note: Teams coming with pre-designed files will be disqualified.