Papers
4
Total Citations
17
H-Index
3
About
A. Saraiva’s research focuses on the intersection of robotics, computer vision, and human-robot interaction, with a particular emphasis on gesture-based control systems. Their major contributions include developing novel methods for robotic arm manipulation and multi-robot navigation using gesture recognition, leveraging techniques such as restricted Boltzmann machines and deep learning. Notably, Saraiva explored the application of fractional calculus for gesture segmentation in bio-inspired robots, demonstrating improved recognition accuracy. Their work on scalable task allocation for multi-agent systems, employing A* pathfinding algorithms, showcases efficient cooperative cleaning behaviors without reliance on vision sensors. With key papers accumulating citations in the range of 3 to 5, Saraiva’s research has laid foundational groundwork for intuitive, non-verbal control interfaces in robotics. By integrating pattern recognition, edge detection, and deep learning, their systems enable seamless interaction between humans and machines, advancing the field of autonomous robotic control. This body of work holds promise for applications in assistive technology, industrial automation, and collaborative robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Computer Vision System with Deep Learning for Robotic Arm Control5 citations · 2018
- 3
- 4Scalable task clean-up assignment for multi-agents3 citations · 2018