Smart Waste Management with Intelligent Autonomous Robot for Monitoring Dustbins and Obstacle Avoidance (SMARTBIN ROVER)

Abstract

The present work is the Design and Development of an intelligent rover for autonomous dustbin identification, dustbin fill level monitoring, and real-time notification to concerned authorities. The intelligent rover will be operated for a predefined route with GPS. It facilitates obstacle avoidance, dustbin detection with ultrasonic sensors and dustbin fill level measurement. A central server is equipped to receive the data wirelessly. The dustbin\'s reach capacity will be sent by processing the information. For achieving over 90% in dustbin detection accuracy, the entire system integrates IoT, machine learning and robotics, and dustbin level measurement with 5% error. Low-light detection issues and limited battery life were the challenges during the development of the intelligent rover. Keeping concerned with cleaner urban environments and smart waste management, the present proposed work ensures a scalable, efficient, and sustainable approach. The challenges faced during the development of intelligent rovers will be the future work to enhance the accuracy for broader deployment

Introduction

The paper addresses the increasing challenges of municipal waste management in urban areas due to rapid urbanization and population growth. Traditional manual collection systems are inefficient, leading to overflowing bins and unsanitary conditions. To tackle this, the proposed solution involves a smart bin rover system that uses IoT and robotic technologies for real-time waste monitoring, autonomous navigation, and automated collection.

The intelligent rover:

• Navigates using GPS on predefined routes.

• Uses ultrasonic and IR sensors for obstacle avoidance and bin detection.

• Monitors bin fill levels with high-precision sensors.

• Sends real-time notifications to authorities via a central server.

The system demonstrates 95% accuracy in detection and aims to reduce human intervention, improve operational efficiency, and promote sustainability in urban waste management.

Literature Survey

Multiple studies have contributed to the field:

• Cheong et al. (2020) developed a ROS-based robot with obstacle avoidance and real-time route planning.

• Qi Zhang et al. (2020) used IoT and predictive analytics to optimize waste collection.

• Sung et al. (2021) proposed an AIoT-based system for real-time waste classification and adaptive management.

• Nithya and Mahesh (2017) created a robotic system for autonomous garbage collection and unloading.

• Noiki et al. (2020) built a GSM- and sensor-enabled smart bin for real-time fill status tracking.

• Lindgren and Kuosmanen (2018) focused on an indoor autonomous robot for office waste collection using SLAM.

Proposed Methodology

• Problem: Inefficient, labor-intensive waste collection methods causing environmental issues.

• Objective: Develop a low-cost, autonomous waste collection rover that reduces human labor and enhances safety through real-time obstacle detection.

• Design: The rover uses IR sensors for line-following and ultrasonic sensors for obstacle detection. Controlled by an Arduino Nano, it operates in:

  • Line Following Mode (PID-controlled)

  • Obstacle Avoidance Mode (reroutes when objects are within 30 cm)

• Hardware includes: IR sensors, ultrasonic sensor, servo motors, Arduino Nano.

• Software includes: Navigation algorithms, sensor data processing.

Results and Discussion

The system demonstrates:

• 92% path-following accuracy

• <0.8s response time

• 10–400 cm obstacle detection range

• 6-hour battery life

Key achievements include automated lid control, scalability, and hands-free operation. Future improvements suggested are voice control, solar power integration, and enhanced IoT capabilities for remote monitoring.

Conclusion

The design and implementation of an intelligent rover system that can recognize dustbins on its own, track their fill levels, and provide real-time alerts to the relevant authorities is successfully demonstrated in this study. The system demonstrates a successful integration of robotics, machine learning, and the Internet of Things, with a detection accuracy of over 90% and a fill-level measurement error margin of less than 5%. The rover follows a predetermined GPS-based path and guarantees dependable obstacle avoidance, bin identification using ultrasonic technology, and wireless data transfer to a central server. The prototype has shown itself to be a scalable, effective, and sustainable urban waste management system, despite issues like low-light detection and short battery life. These results demonstrate its potential for wider use with upcoming improvements that solve present drawbacks.

References

[1] S. Wong Seng Cheong, Syafiq Fauzi Kamarulzaman, Md Arafatur Rahman , Implementation of Robot Operating System in Smart Garbage Bin Robot with Obstacle Avoidance system, 2020, Emerging Technology in Computing, Communication and Electronics (ETCCE). [2] Qi Zhang, Hongyang Li, Xin Wan, Martin Skitmore, Hailin Sun, An Intelligent Waste Removal System for Smarter Communities,2020, Sustainability. [3] Wen-Tsai Sung, Ihzany Vilia Devi, Sung-Jung Hsiao, Fathria Nurul Fadillah, Smart Garbage Bin Based on AIoT, 2021, Intelligent Automation & Soft Computing. [4] Nithya.L, Mahesh.M, A Smart Waste Management and Monitoring System Using Automatic Unloading Robot, 2017, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. [5] Ayodeji Noiki, Sunday A. Afolalu, Abiodun A. Abioye, Christian A. Bolu, Moses E. Emetere, Smart waste bin system, 2020, IOP Conference Series: Earth and Environmental Science. [6] Billy Lindgren, Giancarlo Kuosmanen, An Autonomous Robot For Collection Waste Bins In An Office Environment, 2018, Thesisfor the Degree of Master of Science in Engineering - Robotics.

Copyright

Copyright © 2025 Sunilkumar R, Kaviyapriya K, Logeshwaran A, Pragadeeshwaran V, Prathap R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.