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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Development and simulation of an innovative autonomous knowledge-based smart waste collection system.

Mohamed Abdallah1,2,3, Mariam Hosny4,5

  • 1Department of Construction Engineering, The American University in Cairo, New Cairo, Egypt. Mohamed-Abdallah@aucegypt.edu.

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|April 26, 2026
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Summary

A new cloud-based waste collection system (KWC) uses machine learning to predict waste levels, outperforming hardware-intensive smart waste collection (SWC) systems. KWC significantly cuts costs and environmental impact by optimizing routes and reducing collections.

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Area of Science:

  • Environmental Science and Engineering
  • Computer Science and Artificial Intelligence
  • Operations Research

Background:

  • Modernizing waste collection is crucial for decarbonization efforts.
  • Smart sensor-based waste collection (SWC) systems aim to optimize routes but face practical and commercialization challenges due to hardware intensity.
  • Existing systems often rely on fixed schedules rather than actual waste levels.

Purpose of the Study:

  • To introduce and evaluate an innovative cloud-based Knowledge-based Waste Collection (KWC) system.
  • To replace hardware-intensive bin sensors with historical data and machine learning for waste forecasting.
  • To compare the operational and economic efficiency of KWC against conventional and SWC systems.

Main Methods:

  • Developed a KWC system integrating machine learning (XGBoost, deep neural networks, etc.) for waste prediction, expert bin selection, and route optimization.
  • Simulated KWC, SWC, and conventional collection scenarios in a residential district using historical data.
  • Employed heuristic algorithms for bin selection based on actual (SWC) and predicted (KWC) waste quantities.
  • Utilized connected and autonomous vehicles in simulations.

Main Results:

  • XGBoost demonstrated high prediction accuracy (4.2% relative error) for daily waste generation.
  • KWC significantly reduced travel distance (60.9%), collected bins (89%), and collection days (10%) compared to conventional methods.
  • KWC achieved a 63% cost reduction through its cloud-based approach, contrasting with SWC's insufficient 5% travel expense reduction.
  • Connected and autonomous vehicles further reduced total delay by 90%.

Conclusions:

  • The cloud-based KWC system offers substantial economic and operational advantages over hardware-intensive SWC systems.
  • KWC effectively optimizes waste collection by leveraging machine learning for accurate waste forecasting and intelligent route planning.
  • This approach presents a more practical and cost-effective solution for modernizing and decarbonizing the waste collection industry.