Vishali Mankina
Papers
1
Total Citations
3
H-Index
1
About
Vishali Mankina is a researcher at the forefront of autonomous maritime robotics, specializing in trajectory planning and control systems for unmanned surface vessels (USVs). Her work addresses the formidable challenges of navigating dynamic, wind-driven environments, where variables such as ocean currents and shifting winds demand innovative solutions. In her most-cited paper, "A comparison of DRL with APF and A* with PI Control for Trajectory Planning with Obstacle Avoidance for Sailboat Robots" (2024), Mankina delivers a rigorous comparative analysis of deep reinforcement learning (DRL) against classical pathfinding and control methods. This study provides critical insights into how adaptive, learning-based approaches can outperform traditional algorithms in real-time obstacle avoidance, advancing the reliability of autonomous sailboats. With her research accumulating growing citations, Mankina is establishing herself as a key contributor to intelligent maritime systems. Her work not only pushes the boundaries of USV autonomy but also offers practical frameworks for safer, more efficient ocean navigation—a vital step toward the future of autonomous shipping and environmental monitoring.
Research Focus
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Top Papers
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