Hiago Batista
Papers
1
Total Citations
7
H-Index
1
About
Hiago Batista is a researcher focused on autonomous navigation and reinforcement learning for robotic systems, with a particular emphasis on Unmanned Aerial Vehicles (UAVs). His most cited work, "A 3D Q-Learning Algorithm for Offline UAV Path Planning with Priority Shifting Rewards" (2022, 7 citations), introduces a novel reinforcement learning approach that enables UAVs to plan efficient, collision-free paths in three-dimensional environments. By incorporating priority-shifting reward mechanisms, Batista’s algorithm improves the adaptability and safety of autonomous flight, addressing critical challenges in exploration, transportation, and defense applications. His contributions lie at the intersection of machine learning and robotics, advancing the practical deployment of intelligent aerial systems. Batista’s work demonstrates how offline learning methods can reduce computational demands while maintaining robust performance, making his research valuable for real-world autonomous navigation. As a developing scholar, his citation record reflects growing recognition in the field, and his focus on priority-based reward shaping offers a promising direction for future UAV path planning research.
Research Focus
Key Achievements
Top Papers
- 1