Papers
10
Total Citations
115
H-Index
5
About
Ben Lu is a robotics researcher whose work centers on bio-inspired underwater robotics, with a particular focus on flexible and soft robotic fish systems. His research addresses one of the field's most persistent challenges: enabling robotic fish to achieve fast, efficient, and maneuverable locomotion by harnessing the passive bending and energy-storage properties of flexible materials. Lu's most cited work, "Toward Swimming Speed Optimization of a Multi-Flexible Robotic Fish with Low Cost of Transport" (2023, 32 citations), exemplifies his approach of combining biomechanical insight with engineering optimization to minimize energy expenditure while maximizing speed. His dynamic modeling contributions — particularly the wire-driven elastic robotic fish framework (2022, 26 citations) and the flexible-tail stiffness optimization study (2021, 26 citations) — have helped establish rigorous analytical foundations for a class of robots that were previously difficult to model and tune. Beyond fish-inspired locomotion, Lu has explored hydraulically driven soft robots inspired by octopus jet propulsion, broadening his impact across underwater robotics. His work on multi-objective optimization using improved NSGA-II algorithms and adaptive sensor fusion further reflects a comprehensive research vision. Collectively accumulating over 115 citations, Lu's contributions are shaping the next generation of agile, efficient underwater robotic platforms.
Research Focus
Key Achievements
Top Papers
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- 3Development and Stiffness Optimization for a Flexible-Tail Robotic Fish26 citations · 2021
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- 7Design of a Robotic Fish Based on a Passive Flexible Mechanism3 citations · 2019
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- 10Toward Turning Performance Optimization of a Multi-Flexible Robotic Fish1 citations · 2025