Enhanced Object Matching and Identification for Multi-Robot Systems in Complex Environments

Our research focuses on developing a novel loss function that significantly improves object matching accuracy in multi-robot systems, a critical capability for Safety, Security, and Rescue Robotics (SSRR) applications. By enhancing the consistency and reliability of object identification across multiple viewpoints, our approach ensures a comprehensive understanding of environments with complex layouts and interlinked infrastructure components. We utilize ZED 2i cameras to capture diverse scenarios, demonstrating that our proposed loss function, inspired by the DETR framework, outperforms traditional methods in both accuracy and efficiency. The function’s ability to adapt to dynamic and high-risk environments, such as disaster response and critical infrastructure inspection, is further validated through extensive experiments, showing superior performance in real-time decision-making and operational effectiveness. This work not only advances the state of the art in SSRR but also addresses the practical needs of end-users, providing a more robust tool for mission-critical operations.