An Embedded Approach to the Implementation of a Multi-Agent Energy Management System for Unmanned Systems

Le résumé fourni par la source

The paper proposes a multi-agent energy management system for autonomous unmanned platforms, specifically unmanned aerial vehicles (UAVs) and unmanned ground robotic complexes (UGRCs). For the first time, an embedding-based approach is employed to represent the technical state of the battery pack: a multidimensional feature vector is mapped into an embedding space for subsequent analysis, classification, and prediction. The effectiveness of clustering and short-term degradation forecasting of battery cells-implemented via the embedding method using MLP models-is demonstrated. Simulation results confirm the feasibility of early detection of critical battery operating modes and adaptive energy management. The integration of the embedding approach within a multi-agent architecture is examined, assigning distinct roles to monitoring, classification, prediction, and decision-making agents. Attention is also given to the potential integration with digital twin models of batteries. The proposed method is suitable for deployment on edge devices and is promising for application in high-reliability power systems operating under resource-constrained and highly dynamic conditions.

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