Heterogeneous Event Mapping of Multi-Robot System Based on Event Structures

Abstract

Heterogeneous event mapping is a critical challenge in multi-robot system, where diverse robots generate event logs with opaque names and inconsistent formats due to subsystem heterogeneity. However, existing techniques are inappropriate because they do not make full use of workflow features in event logs. We observe that several types of event constraints in event logs may serve as more discriminative features in event matching. Therefore, a novel approach is proposed to address the above problem by leveraging event structures as discriminative feature for heterogeneous event mapping. Specifically, the event structure captures behavioral constraints in event logs to provide a robust foundation for event mapping. To enhance matching efficiency, we devise an advanced A* search algorithm with a tight upper bound, which effectively prunes nonoptimal mappings and avoid the space explosion problem. Furthermore, a prototype system is implemented that automatically calculates the optimal mapping score between heterogeneous events. Based on this, extensive experiments on both real and synthetic data sets demonstrate that our approach outperforms state-of-the-art approaches in accuracy, efficiency, and scalability. This work provides a robust solution for seamless coordination and data sharing in MRS, with potential applications in autonomous navigation, collaborative robotics, and human–robot interaction.