A Smart and Intelligent Robotic Design to Rescue Human from Disaster Conditions Using Artificial Intelligence Assistance

Abstract

In disaster scenarios such as earthquakes, floods, and collapsed buildings, timely rescue operations are crucial to saving lives. This paper proposes a smart and intelligent robotic rescue system that utilizes advanced Artificial Intelligence (AI) to assist in human rescue efforts. The system integrates Convolutional Neural Networks (CNN) for human detection and Long Short-Term Memory (LSTM) networks for path planning, enabling autonomous navigation through hazardous environments. Key components of the system include sensor fusion using infrared, ultrasonic, and LIDAR technologies to ensure high-accuracy detection of survivors and obstacles. The proposed model was evaluated against nine existing models, outperforming them in critical metrics. The human detection accuracy of the proposed model is 97.70%, and the average rescue time is reduced to 4.50 minutes, compared to other models. Additionally, the system achieved the lowest path deviation (4.22%) and communication latency (32.18ms). These results demonstrate the model's superior efficiency and effectiveness in real-time disaster response scenarios. The combination of AI and sensor integration proves to be a reliable and life-saving tool for rescue teams