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AI

LSTM–DDPG controller improves robotic-fish navigation in turbulent flow experiments

2026-07-26

LSTM–DDPG controller improves robotic-fish navigation in turbulent flow experiments
Photo: Inria / Poppy-project.org / Photo H. Raguet. (CC BY-SA 4.0)

The hybrid controller links local flow sensing, temporal prediction and continuous control without requiring a pre-built flow model. In simulations and physical-prototype experiments, the authors report an 88.3% task-completion rate and energy use of 5.2 J/m—45.3% below conventional PID control.

Why it matters: The work is relevant to inspection and environmental underwater robots. Results are constrained to specified flow conditions and need wider field trials.

Paper: Scientific Reports

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