Suraj Raval

University of Maryland, College Park

Papers

7

Total Citations

47

H-Index

5

About

Suraj Raval is an emerging researcher specializing in magnetic surgical robotics, untethered medical device control, and minimally invasive surgical automation. His work addresses one of the central challenges in next-generation robotic surgery: enabling precise, wireless manipulation of miniaturized surgical tools without physical tethers, potentially reducing patient trauma and expanding access to hard-to-reach anatomical regions. Raval's most impactful contributions include developing closed-loop control systems for magnetic agents using low-error numerical magnetic model estimation, achieving enhanced accuracy in force and torque exertion critical for safe surgical operation (11 citations). He has also pioneered the integration of reinforcement learning with model-based simulators for autonomous magnetic robot control (10 citations), moving the field beyond rigid classical methods. His MagnetoSuture™ system, demonstrating untethered needle penetration of human tissue ex vivo, represents a particularly compelling translational achievement (5 citations). Additional contributions include real-time needle localization in cluttered surgical environments with blood and tissue, Zernike polynomial-based magnetic model calibration, and novel nested coil architectures that mitigate actuation singularities in capsule endoscopy. Collectively accumulating over 47 citations, Raval's research positions him as a promising contributor to the future of autonomous, tetherless surgical robotics.

Research Focus

Key Achievements

5
H-Index
7
Papers
47
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Enhanced Accuracy in Magnetic Actuation: Closed-Loop Control of a Magnetic Agent With Low-Error Numerical Magnetic Model Estimation
11 citations · 2022
📈 Most Prolific Year: 2021 (3 Papers)
🤝 Key Collaborators: 21
🏛 Institutions: University of Maryland, College Park

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago