Miguel Figueiredo Nascimento
Papers
1
Total Citations
2
H-Index
1
About
Miguel Figueiredo Nascimento is a robotics researcher whose work bridges visual servoing and motion planning to enable safer, more adaptive robot operation in human environments. His key research areas include visual-based robot control, collision avoidance, and sensor-driven path planning. Nascimento’s major contribution lies in integrating 2D visual servoing with Rapidly-exploring Random Trees (RRT) to simultaneously correct kinematic errors and generate collision-free trajectories. By leveraging 3D information, his approach addresses persistent challenges in visual control—such as real-time obstacle avoidance and precision in unstructured settings—advancing the practical deployment of robots in homes, hospitals, and workplaces. While his most cited paper (2020, 2 citations) represents early-stage work, its conceptual fusion of classic servoing with sampling-based planning has laid groundwork for more robust human-robot interaction systems. Nascimento’s research is particularly notable for its focus on real-world applicability, tackling the gap between theoretical visual servoing and the demands of dynamic, cluttered environments. His ongoing efforts continue to push toward robots that can see, plan, and move with greater autonomy and safety.
Research Focus
Key Achievements
Top Papers
- 1