The practical application of aerial-aquatic robots is hindered by severe impact loads during water entry. Existing load reduction methods are inefficient for robots requiring rapid, high-frequency aerial-aquatic transitions. Therefore, this study proposes an active learning based joint optimization framework for nose profiles and water-entry strategies, which is fully automated. Specifically, an 8-dimensional parameter space is defined for generating dataset inputs through Latin Hypercube Sampling (LHS). Furthermore, a Deep Kernel Learning (DKL) surrogate model is trained under different water-entry strategies, in order to predict peak impact loads and quantify prediction uncertainty for varying nose profiles. Within each active learning loop, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) guides the selective labelling of samples to expand the dataset, and the DKL model is iteratively retrained until convergence. Compared against state-of-the-art methods, the proposed approach reduces the number of required high-fidelity Computational Fluid Dynamics (CFD) simulations to 65% of that of the comparison methods, while achieving maximum reductions of 66.9% in axial and 72.8% in normal impact loads across the parameter space, respectively. Notably, under the water-entry strategies employed by the hunting behavior of gannets, the approach yields profiles closely matching the skull morphology of the northern gannet. In this case, this work not only delivers an efficient and reliable impact loads reduction solution for aerial-aquatic robots, but also reveals the role of natural selection in minimizing such loads. Note to Practitioners—The purpose of this work is to reduce the impact loads on aerial-aquatic transmedia robots during water entry. Existing approaches generally train a single surrogate model for impact load prediction and utilize optimization algorithms to reduce such loads; however, dataset construction requires tedious manual 3D modeling and time-consuming Computational Fluid Dynamics (CFD) simulations. This study proposes an active learning-based optimization framework that can automatically complete the entire process, from nose profile modeling to surrogate model training, without manual intervention. The framework enhances the prediction accuracy of the surrogate model for inputs conducive to load reduction by selectively adding samples before retraining, while also reducing the number of high-fidelity CFD simulations, thereby saving time and computational costs. The optimized design obtained via this framework effectively reduces the impact loads on the robot, achieving values lower than the minimum identified through extensive manual CFD simulations with uninformed search. Furthermore, the optimized nose profile shows superior load reduction performance compared to the traditional conical nose. Although the optimization was conducted specifically on the Lingyuan aerial-aquatic robot, the proposed automated framework is broadly applicable to the design of any water-entry vehicle with complex geometries for impact load reduction.
Active Learning-Based Joint Optimization of Bionic Nose Profile and Water-Entry Strategy for Aerial-Aquatic Robots
Meng Zhao,Kaihong Huang,Hao Lin,Shiyou Zhao,Hui Peng,Huimin Lu,Junhao Xiao
Published 2026 in IEEE Transactions on Automation Science and Engineering
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2026
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IEEE Transactions on Automation Science and Engineering
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Computer Science, Engineering, Environmental Science
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