TY - JOUR
T1 - Touchless underwater wall-distance sensing via active proprioception of a robotic flapper
AU - Panta, Kundan
AU - Deng, Hankun
AU - Zhang, Zhiyu
AU - Huang, Daning
AU - Panah, Azar
AU - Cheng, Bo
N1 - Publisher Copyright:
© 2024 The Author(s). Published by IOP Publishing Ltd.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - In this work, we explored a bioinspired method for underwater object sensing based on active proprioception. We investigated whether the fluid flows generated by a robotic flapper, while interacting with an underwater wall, can encode the distance information between the wall and the flapper, and how to decode this information using the proprioception within the flapper. Such touchless wall-distance sensing is enabled by the active motion of a flapping plate, which injects self-generated flow to the fluid environment, thus representing a form of active sensing. Specifically, we trained a long short-term memory (LSTM) neural network to predict the wall distance based on the force and torque measured at the base of the flapping plate. In addition, we varied the Rossby number (Ro, or the aspect ratio of the plate) and the dimensionless flapping amplitude ( A ∗ ) to investigate how the rotational effects and unsteadiness of self-generated flow respectively affect the accuracy of the wall-distance prediction. Our results show that the median prediction error is within 5% of the plate length for all the wall-distances investigated (up to 40 cm or approximately 2-3 plate lengths depending on the Ro); therefore, confirming that the self-generated flow can enable underwater perception. In addition, we show that stronger rotational effects at lower Ro lead to higher prediction accuracy, while flow unsteadiness ( A ∗ ) only has moderate effects. Lastly, analysis based on SHapley Additive exPlanations (SHAP) indicate that temporal features that are most prominent at stroke reversals likely promotes the wall-distance prediction.
AB - In this work, we explored a bioinspired method for underwater object sensing based on active proprioception. We investigated whether the fluid flows generated by a robotic flapper, while interacting with an underwater wall, can encode the distance information between the wall and the flapper, and how to decode this information using the proprioception within the flapper. Such touchless wall-distance sensing is enabled by the active motion of a flapping plate, which injects self-generated flow to the fluid environment, thus representing a form of active sensing. Specifically, we trained a long short-term memory (LSTM) neural network to predict the wall distance based on the force and torque measured at the base of the flapping plate. In addition, we varied the Rossby number (Ro, or the aspect ratio of the plate) and the dimensionless flapping amplitude ( A ∗ ) to investigate how the rotational effects and unsteadiness of self-generated flow respectively affect the accuracy of the wall-distance prediction. Our results show that the median prediction error is within 5% of the plate length for all the wall-distances investigated (up to 40 cm or approximately 2-3 plate lengths depending on the Ro); therefore, confirming that the self-generated flow can enable underwater perception. In addition, we show that stronger rotational effects at lower Ro lead to higher prediction accuracy, while flow unsteadiness ( A ∗ ) only has moderate effects. Lastly, analysis based on SHapley Additive exPlanations (SHAP) indicate that temporal features that are most prominent at stroke reversals likely promotes the wall-distance prediction.
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U2 - 10.1088/1748-3190/ad2114
DO - 10.1088/1748-3190/ad2114
M3 - Article
C2 - 38252966
AN - SCOPUS:85184013804
SN - 1748-3182
VL - 19
JO - Bioinspiration and Biomimetics
JF - Bioinspiration and Biomimetics
IS - 2
M1 - 026009
ER -