DrainX

Inspiration

Urban flooding in informal settlements is often treated as a natural disaster, but in reality it begins with something far more preventable: blocked drains filled with uncollected waste. With over a billion people living in such conditions and less than 5% of waste collected regularly in many areas, the lack of visibility into drainage conditions makes prevention almost impossible. DrainX was inspired by the need to make these invisible risks visible, early, and actionable.

What it does

DrainX is an autonomous ground robot designed to navigate narrow urban lanes and inspect open drainage systems. It visually analyzes drains and nearby waste to identify blockage severity, tags problem locations, and converts this data into clear, priority-based maps. These maps help community workers and local authorities intervene before flooding and disease outbreaks occur.

How we built it

We designed DrainX as a compact differential-drive rover and modeled it using ROS 2 Humble and Gazebo. The robot includes a front-mounted RGB camera and ultrasonic sensor for inspection and obstacle awareness. The software architecture is modular and ROS-native, separating motion control, perception, blockage classification, and mapping. Simulation was used to validate the robot’s design, sensor placement, and system feasibility before real-world deployment.

Challenges we ran into

Designing a robot that is both realistic and feasible within a hackathon timeline was a key challenge. Balancing simulation detail with simplicity required careful scoping. Another challenge was framing the problem without over-automating the solution, ensuring that the robot supports human decision-making rather than replacing community workers.

Accomplishments that we're proud of

We successfully translated a large-scale urban resilience problem into a concrete robotics solution. We built a ROS 2–compatible robot model, validated the system architecture in simulation, and clearly demonstrated how ground-level data can be transformed into actionable sanitation insights. Most importantly, we aligned engineering decisions with real societal impact.

What we learned

We learned that impactful robotics solutions are as much about system design and problem framing as they are about code. Early-stage simulation is a powerful tool for validating ideas, and focusing on data collection and decision support often creates more value than full automation in resource-constrained environments.

What's next for DrainX

Future work includes upgrading the vision system with trained computer vision models, integrating rainfall and historical data for flood-risk prediction, and scaling deployment through multi-robot coordination. We also plan to collaborate with NGOs and municipal bodies to pilot DrainX in real communities and refine the system through human-in-the-loop feedback.

Built With

• gazebo
• python
• ros2