Daniel Porto Queiroz Carneiro
Papers
1
Total Citations
9
H-Index
1
About
Daniel Porto Queiroz Carneiro is a researcher advancing the frontier of autonomous mobile robotics, with a primary focus on intelligent navigation, sensor fusion, and human-aware collision avoidance. His most cited work, "Autonomous Robotic Navigation Approach Using Deep Q-Network Late Fusion and People Detection-Based Collision Avoidance" (2023, 9 citations), introduces a novel framework that integrates Double Deep Q-Networks with late sensor fusion and real-time people detection via computer vision. This approach enables robots to navigate dynamic environments more safely by anticipating and avoiding moving pedestrians—a critical capability for real-world deployment in crowded spaces. Carneiro’s contributions lie at the intersection of deep reinforcement learning and perception, demonstrating how late fusion of heterogeneous sensor data can improve decision-making in autonomous systems. By combining state-of-the-art AI techniques with practical collision avoidance strategies, his work addresses key challenges in human-robot interaction and safe navigation. With growing recognition in the robotics community, Carneiro’s research is paving the way for more adaptive, socially-aware autonomous agents that can operate reliably alongside humans.
Research Focus
Key Achievements
Top Papers
- 1