Tiered Reactive Exploration Algorithm (TREA) in Independent versus Cooperative Role-Based Agent Robot Rescue Systems

Despite advances in autonomous robotics, the existing pathfinding algorithms used for search and rescue (SAR) still struggle to adapt in dynamic environments, resulting in delayed response and inefficient navigation. This study focuses on the development and implementation of a novel adaptive pathfinding algorithm in a simulated environment with improved real-time decision-making, route optimization, and adaptability to obstacles and dynamic terrain, that aims to enhance the performance of robotic agents in search and rescue scenarios. The newly developed pathfinding algorithm was tested in independent and cooperative role-based agent systems across varying robot counts, victim counts, and grid sizes. The results were then analyzed using a two-way ANOVA, which revealed that robot count and grid size significantly affected rescue completion time, and cooperative role-based agents outperformed independent ones, particularly in complex environments. The findings of this study demonstrate a significant improvement in navigation speed, obstacle avoidance, and agent coordination that contributes to the development of more reliable autonomous rescue operations and offers a scalable approach for implementing adaptive pathfinding in future SAR robotics, particularly in real-world disaster scenarios.