Hidayat Hidayat
Papers
2
Total Citations
7
H-Index
2
About
Hidayat Hidayat is a robotics researcher whose work focuses on advancing autonomous navigation, particularly for agricultural mobile robots. His primary research areas include reinforcement learning, path planning algorithms, and multi-robot systems. Hidayat’s major contributions lie in enhancing the Q-Learning algorithm—a cornerstone of reinforcement learning—to improve real-time path planning for mobile robots. In his 2023 paper, "Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States," he addressed the computational inefficiency of conventional Q-Learning by reducing the state space, enabling faster and more practical navigation for agricultural robots. This work has garnered 4 citations, reflecting its relevance to the field. His follow-up study, "Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model," extended this approach to multi-robot systems, tackling collision avoidance and shortest-path challenges in dynamic environments, with 3 citations. Hidayat’s innovations are notable for bridging theoretical reinforcement learning with real-world agricultural applications, where efficient robot navigation can enhance crop monitoring and harvesting. His work represents a significant step toward scalable, intelligent automation in farming, making him a key contributor to the intersection of robotics and agriculture.
Research Focus
Key Achievements
Top Papers
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