Indra Hidayatulloh
Papers
2
Total Citations
3
H-Index
1
About
Indra Hidayatulloh is a researcher focused on control systems and autonomous robotics, with key contributions in stabilizing two-wheeled robots and advancing self-driving mobile robot technologies. His work addresses fundamental challenges in robotic stability and intelligent navigation, bridging classical control theory with modern machine learning approaches. In his 2023 paper, "Comparing the Efficiency of State-Feedback Controllers in Stabilizing Two-Wheeled Robot" (2 citations), Hidayatulloh systematically evaluates various state-feedback controllers to achieve dynamic balance in inherently unstable two-wheeled platforms, providing practical insights for hobbyists and engineers. His 2024 study, "Enhance Deep Reinforcement Learning with Denoising Autoencoder for Self-Driving Mobile Robot" (1 citation), introduces a novel integration of denoising autoencoders with deep reinforcement learning to improve perception and decision-making in autonomous navigation, aiming to reduce human error in transportation. Though early in his citation impact, Hidayatulloh’s work demonstrates a clear trajectory toward robust, real-world robotic applications, combining theoretical rigor with applied engineering. His research is particularly relevant for students and researchers exploring control theory, reinforcement learning, and autonomous systems, offering foundational contributions that address stability and safety in mobile robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2