Lakshay Malik,
- Student, Department of Automation and Robotics, Guru Gobind Singh Indraprastha University, Delhi, India
Abstract
Disasters, whether natural or man-made, present significant challenges to societies worldwide. Efficient response, recovery, and mitigation strategies are crucial to minimizing human suffering, loss of life, and economic damage. Traditional disaster management strategies, while effective to some degree, often face limitations related to human resources, response time, accessibility, and safety. The integration of artificial intelligence (AI) and robotics into disaster management offers transformative potential for overcoming these challenges. This paper explores the use of AI-driven robotics in disaster management, focusing on sustainable solutions that enhance the efficiency and effectiveness of disaster response and recovery operations. AI-powered robots can be deployed for a wide range of tasks, including search and rescue operations, damage assessment, and environmental monitoring. These robots leverage AI algorithms to navigate hazardous environments autonomously, analyze vast amounts of data in real-time, and assist emergency responders without putting human lives at risk. Furthermore, the ability to process and interpret data from multiple sensors allows these robots to provide valuable insights for decision-making in critical situations. This article delves into various AI-driven robotic systems currently in use or under development, such as autonomous drones, ground robots, and swarm robotics, examining their roles in disaster response. Case studies from real-world disaster scenarios illustrate the impact of these technologies in saving lives, optimizing resource allocation, and reducing response times. Additionally, the paper addresses the potential for scalability and sustainability of AI robotics in disaster management, highlighting the need for collaboration between governments, industries, and research institutions to ensure these technologies are accessible and affordable. Ultimately, this paper argues that AI-driven robotics have a pivotal role in advancing sustainable disaster management practices, fostering resilience in vulnerable communities, and reducing the long-term effects of catastrophic events.
Keywords: Artificial intelligence, human–robot interaction, disaster management, machine learning, deep learning
[This article belongs to International Journal of Advanced Robotics and Automation Technology ]
Lakshay Malik. AI-Driven Robotics for Sustainable Solutions in Disaster Management. International Journal of Advanced Robotics and Automation Technology. 2025; 03(01):24-30.
Lakshay Malik. AI-Driven Robotics for Sustainable Solutions in Disaster Management. International Journal of Advanced Robotics and Automation Technology. 2025; 03(01):24-30. Available from: https://journals.stmjournals.com/ijarat/article=2025/view=227296
References
- Murphy RR, Tadokoro S, Nardi D, Jacoff A, Fiorini P, Choset H, et al. Search and rescue robotics. In: Siciliano B, Khatib O, editors. Springer Handbook of Robotics. Berlin: Springer; 2008. p. 1151–73. doi:10.1007/978-3-540-30301-5_51.
- Siciliano B, Sciavicco L, Villani L, Oriolo G. Robotics: Modelling, Planning and Control. Berlin: Springer-Verlag; 2009. p. 108–15.
- Stachura M, Frew E. Communication-aware information-gathering experiments with an unmanned aircraft system. J Field Robot. 2017;34:736–56. doi:10.1002/rob.21666.
- Griffin B, Grizzle J. Nonholonomic virtual constraints and gait optimization for robust walking control. Int J Robot Res. 2017;36:895–922. doi:10.1177/0278364917708249.
- Sentis L, Park J, Khatib O. Compliant control of multicontact and center-of-mass behaviors in humanoid robots. IEEE Trans Robot. 2010;26:483–501. doi:10.1109/TRO.2010.2043757.
- Mohammadi M, Shahri AM. Adaptive nonlinear stabilization control for a quadrotor UAV: Theory, simulation and experimentation. J Intell Robot Syst. 2013;72:105–22. doi:10.1007/s10846-013-9813-y.
- Werger BB, Mataric MJ. Broadcast of local eligibility: behavior-based control for strongly cooperative robot teams. In: Proceedings of the Fourth International Conference on Autonomous Agents (AGENTS ’00); 2000 Jun 3–7; Barcelona, Spain. New York (NY): Association for Computing Machinery; 2000. p. 21–2. doi:10.1145/336595.336621.
- Otte M, Kuhlman MJ, Sofge D. Auctions for multi-robot task allocation in communication limited environments. Auton Robots. 2020;44:547–84. doi:10.1007/s10514-019-09828-5.
- Lee MFR, Chien TW. Artificial intelligence and internet of things for robotic disaster response. 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan. 2020. p. 1–6. doi:10.1109/ARIS50834.2020.9205794.
- Nikolaidis S, Hsu D, Srinivasa S. Human–robot mutual adaptation in collaborative tasks: Models and experiments. Int J Robot Res. 2017;36:618–34. doi:10.1177/0278364917690593. PMID: 32855581.
- Dsouza F. Human–robot collaboration in disaster response: The role of rescue robots. Adv Robot Autom. 2023;12:271. doi:10.37421/2168-9695.2023.12.271.
- Rajesh M, George A, Sudarshan TS. Energy efficient deployment of wireless sensor network by multiple mobile robots. 2015 International Conference on Computing and Network Communications (CoCoNet), Trivandrum, India. 2015. p. 72–8. doi:10.1109/CoCoNet.2015.7411169.
- Gilpin K, Rus D. Modular robot systems. IEEE Robot Autom Mag. 2010;17:38–55. doi:10.1109/MRA.2010.937859.
- Battistuzzi L, Recchiuto CT, Sgorbissa A. Ethical concerns in rescue robotics: A scoping review. Ethics Inf Technol. 2021;23:863–75. doi:10.1007/s10676-021-09603-0.
| Volume | 03 |
| Issue | 01 |
| Received | 24/02/2025 |
| Accepted | 27/02/2025 |
| Published | 10/03/2025 |
| Publication Time | 14 Days |
Login
PlumX Metrics

