Arduino Robotics Sensors Environmental
Garbage Collection Boat

Project Overview

The Garbage Collection Boat is an autonomous water vehicle designed to help clean up water bodies by collecting floating garbage. It uses Arduino-based control systems and sensors to navigate and collect debris effectively.

Completed: April 2023
Status: Prototype
Team Size: 2

Problem Statement

Water pollution from floating debris is a significant environmental concern. Manual cleanup is labor-intensive and often hazardous. An autonomous solution can make cleanup operations more efficient and sustainable.

Solution

Our autonomous garbage collection boat provides an innovative solution to address water pollution. The boat uses sensors to detect and navigate toward floating debris, then collects it using a specialized collection mechanism.

Technical Implementation

Code

garbage_boat.ino
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#include <Servo.h>

#include <NewPing.h>

// Motor pins

const int leftMotorPin = 5;

const int rightMotorPin = 6;

// Ultrasonic sensor pins

const int trigPin = 9;

const int echoPin = 10;

// Collection mechanism

Servo collectorServo;

void setup() {

Serial.begin(9600);

collectorServo.attach(3);

pinMode(leftMotorPin, OUTPUT);

pinMode(rightMotorPin, OUTPUT);

}

void loop() {

// Read distance from ultrasonic sensor

long duration = pulseIn(echoPin, HIGH);

int distance = duration * 0.034 / 2;

// If debris detected, collect it

if (distance < 20) {

collectDebris();

} else {

navigateForward();

}

delay(100);

}

void collectDebris() {

collectorServo.write(90);

delay(500);

collectorServo.write(0);

}

void navigateForward() {

analogWrite(leftMotorPin, 150);

analogWrite(rightMotorPin, 150);

}

Key Features

  • Autonomous navigation using ultrasonic sensors
  • Solar-powered operation for extended use
  • Debris collection mechanism with servo control
  • Water-resistant electronics housing
  • Remote monitoring capabilities

Results & Impact

The prototype successfully demonstrated the ability to navigate and collect floating debris in controlled testing environments. The project shows promise for scaling to larger implementations for real-world cleanup operations.

Future Improvements

  • Integration of machine learning for better object recognition
  • Improved battery capacity and solar efficiency
  • Fleet management system for multiple boats
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