Indra Agustian

Papers

Total Citations

H-Index

About

Indra Agustian’s research lies at the intersection of robotics, control systems, and artificial intelligence, with a particular focus on developing intelligent navigation and obstacle avoidance algorithms for wheeled mobile robots. His work demonstrates a practical, hands-on approach to integrating machine learning and advanced control theory into real-world robotic platforms. One of his key contributions is the application of Q-learning, a reinforcement learning algorithm, for obstacle avoidance in a wheeled robot prototype, implemented on an Arduino Mega2560 platform with multiple ultrasonic sensors. This work, published in 2021, showcases how autonomous agents can learn optimal navigation policies through trial and error. Additionally, Agustian has advanced the field of wall-following robots by proposing a hybrid Fuzzy-PID controller for Ackerman steering systems. This approach intelligently switches between fuzzy logic and PID control depending on input variations, offering a robust solution for smooth and responsive robot motion. While his citation counts are currently modest, his research provides foundational insights into low-cost, embedded implementations of intelligent control, making his work valuable for students and engineers seeking to bridge theory with practical robotics design.