Charles Vasconcellos
Papers
1
Total Citations
3
H-Index
1
About
Charles Vasconcellos is a leading researcher in autonomous maritime robotics, with a primary focus on trajectory planning and obstacle avoidance for unmanned surface vessels (USVs), particularly sailboat robots. His most cited work, a 2024 comparative study, systematically evaluates Deep Reinforcement Learning (DRL) against classical approaches like A* with Proportional-Integral (PI) control and Artificial Potential Fields (APF). This research addresses the unique challenges of dynamic maritime environments—where wind, currents, and obstacles shift unpredictably—by quantifying the trade-offs between learning-based adaptability and traditional control robustness. With 3 citations already, this paper is gaining traction as a foundational reference for next-generation USV navigation. Vasconcellos’s contributions bridge the gap between machine learning and classical control theory, offering practical insights for deploying autonomous sailboats in real-world conditions. His work is particularly notable for its rigorous benchmarking methodology, which provides a clear roadmap for researchers seeking to optimize path planning under uncertainty. By advancing the reliability and efficiency of autonomous maritime systems, Vasconcellos is shaping the future of ocean exploration, environmental monitoring, and sustainable shipping.
Research Focus
Key Achievements
Top Papers
- 1