G.C. Cardarilli
Papers
2
Total Citations
92
H-Index
2
About
G.C. Cardarilli is a leading researcher in embedded systems, indoor localization, and multi-agent reinforcement learning. His work bridges theoretical algorithms with practical hardware implementations, notably through the development of a Bluetooth Low Energy (BLE)-based indoor localization system for museum applications (2020, 67 citations), which enhances visitor experiences by enabling precise, low-cost position tracking without GPS. Cardarilli also made significant contributions to swarm robotics with his Q-RTS (Q-Learning for Real-Time Swarm) algorithm (2024, 25 citations), a multi-agent reinforcement learning approach that reduces convergence time for robotic swarms, successfully deployed on the Robotarium platform. His research emphasizes real-time embedded system constraints, ensuring that complex AI models can operate efficiently on resource-limited devices. With a career focused on intelligent systems, Cardarilli’s work has practical impacts on smart environments, autonomous robotics, and IoT applications, making him a key figure in advancing deployable AI solutions.
Research Focus
Key Achievements
Top Papers
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