Chiotis Dimitrios
Papers
2
Total Citations
5
H-Index
2
About
Dimitrios Chiotis is a researcher at the forefront of integrating robotics and artificial intelligence for renewable energy optimization. His primary research areas include solar energy tracking, high-degree-of-freedom (DoF) robotic systems, and deep reinforcement learning. Chiotis’s major contribution lies in developing novel algorithms that leverage advanced robotics and AI to maximize solar energy harvesting. His most cited work, "Maximum Solar Energy Tracking Leverage High-DoF Robotics System with Deep Reinforcement Learning" (2024), addresses the critical challenge of maintaining accurate solar trajectory monitoring. This research tackles a common failure mode in solar tracking systems—predictive divergence from the solar locus—by employing deep reinforcement learning to enhance tracking precision and robustness. With citations reaching 3 and 2 for his key papers, Chiotis’s work is gaining traction in the field of autonomous energy harvesting and environmental sensing. His achievements demonstrate a promising intersection of robotics, control systems, and sustainable energy, positioning him as an emerging innovator in the quest for more efficient and reliable solar energy technologies.
Research Focus
Key Achievements
Top Papers
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- 2