To bypass the high computational overhead and environment-specific mapping dependencies of traditional indoor localization, this work introduces a cost-effective navigation framework that enables standard autopilot controllers to operate indoors via dynamic GPS retransmission. By integrating OAK-D AI cameras for wide-area target detection with Software-Defined Radio (SDR) technology, the system generates real-time, localized GPS signals to provide seamless position inputs to commercial off-the-shelf autopilots. Experimental results demonstrate target detection with a 69% confidence floor at an operational distance of 8.5 m. Under static conditions, Kalman filtering refined the retransmitted GPS tracking accuracy from 21 cm to 13 cm within 0.6 s. Dynamic tracking trials along a complex figure-eight trajectory demonstrated that a 100 Hz non-linear EKF—fusing 5 Hz retransmitted GPS with raw IMU variable speed—effectively neutralized indoor multipath interference. This framework achieved an exceptional 2D position RMSE of just 4.28 cm, compared to a substantial 43.20 cm error yielded by a constant-speed baseline tracking architecture. This study demonstrates that dynamic GPS retransmission provides a robust, infrastructure-light alternative to complex spatial mapping, allowing standard autonomous vehicles to navigate seamlessly within GNSS-denied environments.
The transition from centralized fossil fuel-based power systems toward decentralized smart grids with a high penetration of renewable energy sources (RES) introduces substantial challenges in monitoring, control, coordination, and management. These challenges are particularly evident at the active power grid periphery, defined in this work as the decentralized edge layer of modern power systems comprising low-voltage distribution networks, distributed energy resources (DERs), prosumers, energy storage systems, electric vehicles (EVs), and localized intelligent control entities operating near the consumer side of the grid. This review systematically examines the role of multi-agent systems (MASs) in addressing these emerging challenges. A total of 160 articles, drawn predominantly from top-tier Q1 journals and published up to March 2026, were systematically analyzed to evaluate recent methodological advances, identify persistent research gaps, and compare existing problem formulations and mathematical techniques. The review covers MAS-based applications including distributed energy management, voltage and frequency regulation, demand-side management, microgrid coordination, EV charging coordination, resilience enhancement, and cyber-physical supervisory control. The findings indicate that although MASs offer enhanced scalability, flexibility, resilience, and decentralized decision-making capabilities, existing approaches continue to face significant limitations associated with communication latency, cybersecurity vulnerabilities, interoperability constraints, heterogeneous agent dynamics, and limited real-time experimental validation. Furthermore, this review proposes six emerging research hypotheses targeting underexplored domains, presents a methodological decision flowchart for MAS implementation and selection, and discusses future research directions involving the integration of digital twins, blockchain technologies, edge intelligence, and advanced communication architectures with MAS frameworks.
Sultan Mamun, Stelios Ioannou, N. Christofides et al.· Applied Sciences· 0 citations
