AI-Driven Autonomous Robots for Search and Rescue Operations in Disaster Zones

Search and rescue (SAR) operations designed to save lives in disaster zones face difficulties executing effectively because of dangerous surroundings and challenging terrain features. An AI-controlled robotic system exists to deal with existing challenges. The system combines a reinforcement learning-based navigation system that integrates multiple sensors including LiDAR with thermal cameras and RGB sensors for detecting victims to work with decentralized task distribution frameworks. Real-time simulations tested disaster navigation datasets showing an overall detection accuracy rate of 95.6%, navigation accuracy at 92.8%, while obstacle avoidance performed at 97.2%. This deployed system achieved 3.2-minute average response times and processed requests with 85.4% resource efficiency. The system proves superior performance over existing SAR methods while demonstrating speed enhancement, accuracy improvement, and autonomous operational value in SAR operations. Future disaster response will change thanks to AI-powered robotics because this technology enables scalable efficient reliable solutions for future deployments.