Duarte Antunes
Papers
5
Total Citations
30
H-Index
3
About
Duarte Antunes is a leading researcher at the intersection of control theory, robotics, and real-time decision-making. His work primarily focuses on event-triggered control, learning-based model predictive control (MPC), and autonomous robotic systems operating under communication and sensing constraints. Antunes has made significant contributions to developing control strategies that are both theoretically rigorous and experimentally validated, bridging the gap between abstract control policies and practical robotic applications. His most cited paper (11 citations) addresses the challenge of vision-induced delays in self-localizing robots, proposing deadline-driven control to maintain performance despite variable processing times. Another key contribution is his embedded learning-based MPC framework using Gaussian Process Regression (8 citations), which enables mobile robots to improve their models from real-time data without requiring precise prior knowledge. Antunes has also experimentally validated event-triggered control policies with performance guarantees (6 citations), demonstrating their effectiveness in remote sensing and control scenarios. His recent work on selective table grape harvesting (2025) showcases his ability to tackle complex agricultural robotics problems, integrating mapping, quality estimation, and decision-making under uncertainty. Through his research, Antunes has advanced the practical deployment of intelligent control systems in robotics and automation.
Research Focus
Key Achievements
Top Papers
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