Linda Dotto de Moraes
Papers
2
Total Citations
6
H-Index
2
About
Linda Dotto de Moraes is an emerging researcher whose work sits at the intersection of artificial intelligence, robotics, and autonomous systems. Her primary research focus centers on **deep reinforcement learning (Deep-RL)** applied to mobile robot navigation, particularly in environments where traditional mapping approaches are unavailable or impractical. Her most recognized contributions explore how advanced neural network architectures can enable robots to navigate complex, real-world environments using only low-dimensional sensory inputs — a significant challenge in autonomous robotics. Her notable work comparing Deep Q-Network (DQN) and Double Deep Q-Network (DDQN) algorithms for mapless terrestrial robot navigation represents a meaningful step forward in making autonomous navigation more computationally efficient and accessible. By demonstrating that these techniques can guide mobile robots without pre-built maps, her research contributes to more adaptable and scalable robotic systems with practical applications in logistics, search-and-rescue, and industrial automation. Although still in the early stages of her academic career, her 2023 publications have already begun attracting scholarly attention, accumulating citations that signal growing interest from the robotics and AI communities. Researchers interested in reinforcement learning for autonomous systems will find her work a valuable and forward-looking contribution to the field.
Research Focus
Key Achievements
Top Papers
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