David Jacobo
Papers
1
Total Citations
10
H-Index
1
About
David Jacobo is a researcher in robotics and control systems, with a primary focus on pursuit–evasion dynamics and autonomous motion planning. His most cited work, "A visual feedback-based time-optimal motion policy for capturing an unpredictable evader" (2014, 10 citations), addresses the classic problem of capturing an omnidirectional evader using a differential drive robot in an obstacle-free environment. Jacobo’s key contributions include a state feedback-based time-optimal motion policy that enables a robot to reactively and efficiently intercept an unpredictable target using only visual input. This work bridges theoretical optimal control with practical sensor-driven robotics, offering a robust framework for real-time autonomous pursuit. While his citation count is modest, the paper’s focus on time-optimality and unpredictability highlights its relevance to applications in surveillance, robotic games, and autonomous interception. Jacobo’s research sits at the intersection of control theory, computer vision, and robotics, and his approach to integrating feedback with motion policies provides a foundation for further studies in dynamic, adversarial environments. His work is a valuable resource for students and researchers exploring reactive planning under uncertainty.
Research Focus
Key Achievements
Top Papers
- 1