A Novel Framework for Collaborative Knowledge Sharing and Learning for Disaster Response Robots

M. I. R. Shuvo, Nafiul Alam, Awal Ahmed Fime, Ahmed Aziz Jaidane, Saifuddin Mahmud, Jong-Hoon Kim

Abstract: Collaborative robotics research shows multi-robot systems’ utility in various applications. This study proposes a novel approach to knowledge sharing and learning across heterogeneous robots. The suggested architecture allows robotic agents to easily share vision, control, and decision-making algorithms. We solved object detection, global position estimation, and reconstruction using a stage-wise modular method, demonstrating good performance even in changing situations. The system’s initial findings show that RealSense technology’s rigorous calibration allows for minimum global position estimate errors. The PCA-based orientation module was highly accurate. The paper examines the bounding box estimation module’s complexity and Z-axis orientation flexibility. The reconstruction module needs improvement due to intrinsic conversion problems between pixel data and 3D points, however the system estimates height and width accurately. This research improves adaptability and interoperability in complex multi-agent circumstances by fostering collaborative intelligence in different robot teams.

External IDs:dblp:conf/ihci/ShuvoAFJMK24