Use of AI in Improvement of Robots for Rescue

Case Study – Use of AI in Improvement of Robots for Rescue
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April 8, 2023
Problem Formulation
The project aims to enhance rescue robots' situational awareness and decision-making abilities for search and rescue missions in hazardous environments. The problem to be solved involves developing algorithms and models that enable robots to navigate complex terrains, deal with task uncertainty, and make automated decisions. The objective is to reduce the human workload, improve the proportion of man-machine in robot-assisted search and rescue, and enhance rescue efficiency.
Approach Overview
The approach involves developing models and algorithms to enhance the robots' ability to navigate complex terrains and deal with task uncertainty. The main steps involved in the approach are:
1 Developing a situational awareness model enables the robot to understand the rescue site's environment and obstacles.
2 Developing a decision-making algorithm enables the robot to make automated decisions based on understanding the rescue site's environment and the mission's objectives.
3 Developing a navigation model that enables the robot to navigate the rescue site autonomously.
4 Testing the developed models and algorithms in real-world scenarios.
Case Study
To showcase the project, we will consider the following case studies:
1 Search and Rescue in Urban Environments: This case study involves developing robots that can navigate urban environments, such as collapsed buildings and rubble, to search for and rescue victims. The robots should be able to navigate narrow passages, climb over debris, and locate victims using sensors such as cameras and microphones.
2 Search and Rescue in Natural Disasters: This case study involves developing robots to assist search and rescue missions in natural disasters such as earthquakes, floods, and landslides. The robots should be able to navigate through rugged terrain, locate victims, and provide assistance.
References for Approach
Primary Reference
Lygouras, E., Santavas, N., Taitzoglou, A., Tarchanidis, K., Mitropoulos, A., & Gasteratos, A. (2019). Unsupervised human detection with an embedded vision system on a fully autonomous UAV for search and rescue operations. Sensors, 19(16), 3542.
Alternative References
Dilip, G., Guttula, R., Rajeyyagari, S., Hemalatha, S., Pandey, R. R., Bora, A., Kshirsagar, P. R., M, K. M., & Sundramurthy, V. P. (2022). Artificial Intelligence-Based Smart Comrade Robot for Elders Healthcare with Strait Rescue System. Journal of Healthcare Engineering, 2022, pp. 1–12. https://doi.org/10.1155/2022/9904870
Drew, D. S. (2021). Multi-Agent Systems for Search and Rescue Applications. Current Robotics Reports, 2(2), 189–200. https://doi.org/10.1007/s43154-021-00048-3
Ezenyilimba, A., Wong, M. W. N., Hehr, A., Demir, M., Wolff, A. T., Chiou, E. K., & Cooke, N. J. (2022). Impact of Transparency and Explanations on Trust and Situation Awareness in Human–Robot Teams. Journal of Cognitive Engineering and Decision Making, 17(1), 75–93. https://doi.org/10.1177/15553434221136358
Jain, V., Saini, D., Gupta, M., Joshi, N. K., Mishra, A...