Sustainable Waste Management Using AI and Robotics

Year : 2025 | Volume : 03 | Issue : 02 | Page : 42 48
    By

    Harshvardhan,

  • Manpreet Kaur,

  • Manpreet Kaur,

  1. Student, Department of Computing, Guru Kashi University, Bathinda, Punjab, India
  2. Assistant Professor, Department of Computing, Guru Kashi University, Bathinda, Punjab, India
  3. Assistant Professor, Department of Computing, Guru Kashi University, Bathinda, Punjab, India

Abstract

The fast increment in squander era around the world has driven to noteworthy natural and open wellbeing concerns. Conventional squander administration strategies, which depend intensely on manual labor and obsolete forms, are battling to keep up with the developing volume, driving to wasteful aspects and environmental harm. AI and Mechanical autonomy give inventive arrangements by improving effectiveness in squander collection, sorting, reusing, and transfer. AI-driven squander sorting frameworks optimize exactness, decrease human mediation, and progress maintainability. Mechanical autonomy mechanizes monotonous squander preparing errands, decreasing labor costs and moving forward security. This inquiries about expounds on the integration of AI and Mechanical technology in squander administration, counting current applications, challenges, open recognitions, and future advancements. An organized study was conducted to survey open mindfulness and acknowledgment of these advances, and this comes about highlight the benefits and concerns related to AI-driven squander administration. The paper advance gives arrangement suggestions and industry bits of knowledge to bolster the execution of AI and mechanical autonomy in feasible squander administration frameworks. This consideration contributes to the broader talk on utilizing savvy advances to address natural issues and construct a circular economy.

Keywords: Sustainable waste management, artificial intelligence (AI), machine learning (ML), robotics in waste management, smart waste management

[This article belongs to International Journal of Advanced Robotics and Automation Technology ]

How to cite this article:
Harshvardhan, Manpreet Kaur, Manpreet Kaur. Sustainable Waste Management Using AI and Robotics. International Journal of Advanced Robotics and Automation Technology. 2025; 03(02):42-48.
How to cite this URL:
Harshvardhan, Manpreet Kaur, Manpreet Kaur. Sustainable Waste Management Using AI and Robotics. International Journal of Advanced Robotics and Automation Technology. 2025; 03(02):42-48. Available from: https://journals.stmjournals.com/ijarat/article=2025/view=232961


References

  1. Gupta R, Sharma N, Verma L. Challenges in Traditional Waste Management and the Need for AI-based Solutions. Waste Manag Res. 2021; 39(1): 45–61.
  2. Hannan M, Karthik S, Zhou L. Machine Learning in Waste Classification: Applications and Future Trends. AI and Environmental Sustainability. 2022; 10(3): 203–220.
  3. Kim H, Lee Y, Park J. AI-driven Navigation in Waste Collection Vehicles: Efficiency and Cost Reduction. Journal of Urban Logistics. 2019; 17(4): 312–329.
  4. Kumar V, Mehta P, Singh R. Public Attitudes Towards AI-driven Waste Solutions: A Survey Analysis. Waste Policy Studies. 2023; 15(1): 78–94.
  5. Ahmed S, Khan R, Gupta P. Automation in Waste Sorting: The Role of Robotics and AI. Journal of Environmental Technology. 2023; 45(3): 215–232.
  6. Brown D, Williams J, Patel M. Government Policies and AI Adoption in Waste Management. Environmental Policy Review. 2021; 12(4): 145–167.
  7. Ghosh A, Patel S. Smart Waste Management: The Role of IoT and AI Integration. Sustainable Technologies J. 2022; 8(2): 98–115.
  8. Satav AG, Kubade S, Amrutkar C, Arya G, Pawar A. A state-of-the-art review on robotics in waste sorting: scope and challenges. Int J Interact Des Manuf. 2023 Dec; 17(6): 2789–806.
  9. Bobulski J, Kubanek M. Deep learning for plastic waste classification system. Appl Comput Intell Soft Comput. 2021; 2021(1): 6626948.
  10. Gupta T, Joshi R, Mukhopadhyay D, Sachdeva K, Jain N, Virmani D, Garcia-Hernandez L. A deep learning approach based hardware solution to categorise garbage in environment. Complex Intell Syst. 2022 Apr; 8(2): 1129–52.
Regular Issue Subscription Original Research
Volume 03
Issue 02
Received 31/05/2025
Accepted 17/10/2025
Published 25/10/2025
Publication Time 147 Days
101

Login


My IP

PlumX Metrics