Carlos Andrey Maia
Papers
6
Total Citations
241
H-Index
6
About
Carlos Andrey Maia’s research lies at the intersection of robotics, control theory, and motion planning, with a particular focus on guiding robots along complex, time-varying curves. His most influential work, “Vector Fields for Robot Navigation Along Time-Varying Curves in $n$-Dimensions” (2010, 188 citations), introduces a groundbreaking methodology for generating artificial vector fields that enable robots to converge to and circulate around generic curves—a critical capability for applications like unmanned aerial vehicle (UAV) surveillance and border inspection. Maia extends this foundation with practical validations through real robot experiments in both 2D and 3D workspaces, demonstrating the robustness of his approach. He has also made notable contributions to multi-robot coordination, addressing the challenge of synchronizing multiple fixed-wing UAVs along intersecting periodic paths while ensuring collision avoidance. More recently, Maia has explored algebraic frameworks for manipulation tasks using dual quaternion algebra, and tackled motion planning under uncertainty with Partially Observable Markov Decision Processes. His work bridges theoretical elegance and real-world applicability, earning him a reputation as a key figure in vector-field-based robot navigation and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
- 1Vector Fields for Robot Navigation Along Time-Varying Curves in $n$-Dimensions188 citations · 2010
- 2
- 3
- 4
- 5
- 6