Reza Firsandaya
Papers
1
Total Citations
8
H-Index
1
About
Reza Firsandaya is a researcher whose work sits at the intersection of robotics, embedded systems, and intelligent navigation. His primary contributions focus on developing robust, low-cost algorithms for autonomous mobile robots, particularly in constrained environments. His most cited work, "Implementation of RAM Based Neural Networks on Maze Mapping Algorithms for Wall Follower Robot" (2019, 8 citations), demonstrates a novel approach to safe navigation by combining a RAM-based neural network with the classic left-hand rule maze-solving technique. This allows a simple wall-follower robot to learn and map its surroundings without damaging obstacles, improving both safety and autonomy. Firsandaya’s research is notable for its practical, hardware-aware design, bridging theoretical machine learning with real-world robotic constraints. His work has been influential in the field of educational and service robotics, where cost and reliability are paramount. By showing how lightweight neural networks can enhance traditional control algorithms, Firsandaya has provided a valuable framework for researchers and students working on embedded AI and autonomous navigation systems.
Research Focus
Key Achievements
Top Papers
- 1