AQUASWEEP
Be the wave that clean the Sea.
The problem AQUASWEEP solves
🌍 The Problem It Solves
Monitoring water bodies for pollution, waste, and environmental hazards is traditionally manual, time-consuming, and risky. It often requires human-operated boats, physical sampling, and delayed lab analysis, which limits how frequently and safely data can be collected—especially in polluted or hazardous areas.
SamudraManthan addresses these challenges by providing a real-time, autonomous, and remote monitoring solution for aquatic environments.
🚤 What People Can Use It For
Oil Spill Detection
Early detection of oil contamination using turbidity and IR sensors helps authorities respond faster and prevent large-scale environmental damage.
Floating Waste Monitoring
Ultrasonic-based waste detection enables identification of debris and polluted zones without manual inspection.
Water Quality Observation
Continuous monitoring of temperature, humidity, and turbidity provides valuable insights into water health over time.
Remote Surveillance
GPS tracking and live dashboards allow users to monitor large water bodies remotely without deploying personnel on-site.
🛡️ How It Makes Tasks Easier and Safer
Reduces Human Exposure
Eliminates the need for people to operate boats in contaminated or dangerous waters.
Improves Response Time
Real-time data streaming enables immediate alerts instead of delayed reporting.
Prevents Operational Failures
Built-in failsafe mechanisms ensure motors shut down safely during communication loss.
Supports Autonomous Missions
Automated waypoint navigation allows systematic coverage of water bodies with minimal human intervention.
🌱 Real-World Impact
SamudraManthan can be used by:
Environmental agencies
Municipal waste management departments
Research institutions
Disaster response teams
By automating water monitoring, the system lowers operational cost, improves data accuracy, and enhances environmental safety, making large-scale marine conservation efforts more practical and scalable.
Challenges we ran into
🚧 Challenges I Ran Into
- ESP32–Server Communication Reliability
One of the first challenges was ensuring stable communication between the ESP32 and the Flask server. Occasionally, the ESP32 would miss commands or the server would not receive sensor updates, causing motors to behave unpredictably.
How I solved it:
I implemented a command–acknowledgment mechanism and a failsafe timeout on the server side. If the ESP32 failed to acknowledge a command within a fixed time window, the server automatically stopped the thrusters. This made the system much safer and more reliable.
- Manual vs Automatic Control Conflict
Another issue was thruster values getting overwritten when switching between MANUAL and AUTO modes. The ESP32 was updating motor values even when manual control was active from the dashboard.
How I solved it:
I clearly separated responsibilities:
In MANUAL mode, only the web dashboard can control thrusters.
In AUTO mode, the ESP32 is allowed to update thrusters.
This logic was enforced on the server, preventing accidental overrides.
- Servo Motor Not Responding
While adding the servo mechanism for waste/oil handling, the servo initially did not move at all, even though the code compiled successfully.
How I solved it:
I discovered that:
The servo required an external 5V power supply
ESP32 PWM timing needed precise duty-cycle mapping
I switched to ESP32 native PWM (ledc) and fine-tuned the pulse width values, which fixed the issue completely.
- Sensor Noise and False Detections
Turbidity and ultrasonic sensors sometimes produced false positives, especially in unstable water conditions.
How I solved it:
I added threshold-based logic and combined multiple sensor conditions (IR + turbidity for oil detection). This reduced noise and improved detection accuracy without adding heavy filtering.
- Real-Time UI Synchronization
Keeping the web dashboard synchronized with real-world sensor and motor states was tricky, especially when updates happened every second.
How I solved it:
I designed the dashboard to always pull live state from the server instead of relying on local UI assumptions. This ensured that the displayed values always reflected the actual boat state.
✨ Key Takeaway
Most challenges came from system integration rather than individual components. Solving them required clear separation of control logic, reliable communication, and careful handling of hardware constraints—lessons that significantly improved the robustness of the project.
Technologies used