Author name cluster

Qiao Hu

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4 papers

2 author rows

IROS Conference 2025 Conference Paper

Design and Dynamic Modeling Analysis of Undulatory Propulsion Underwater Robot with Rotational Passive Degrees of Freedom in Fin Rays

Tangjia Zhang
Qiao Hu
Shijie Li
Yangbin Zeng
Siyu Zu
Liangjie Sun

Current research on undulatory propulsion robots has predominantly centered on hydrodynamic performance simulations. However, challenges such as limited mobility and difficulties in parameter identification during underwater bio-mimetic motion remain unresolved. To address these issues, this study proposes a novel undulating fin robot featuring passive rotational joints, aiming to enhance motion capabilities and facilitate more accurate modeling. These joints enhance both the agility and stability of the robot's movements. Initially, the research develops models for the undulatory motion of the undulating fin and the rotational passive degrees of freedom in the fin rays. Based on fluid drag theory, a hydrodynamic model for undulating fin propulsion is constructed to analyze the thrust, lateral force, and lift generated at varying frequencies. Furthermore, a comprehensive dynamics model for the underwater motion of the biomimetic undulating fin robot is developed. Numerical simulations of the robot's non-steady-state motion are conducted to identify the hydrodynamic parameters of the model, thereby enabling the solution of the dynamic model. The experimental results demonstrate that the robot achieves an underwater straight-line motion speed exceeding 0. 5m/s, a turning speed of approximately 45°/s, and an inclined upward motion speed of 0. 21 m/s. This study provides a novel approach for the design of underwater undulating fin robots and the resolution of kinematic models for underwater robots. It is hoped that this research can contribute to the further development of undulatory propulsion robot technology.

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NeurIPS Conference 2025 Conference Paper

Traversal Verification for Speculative Tree Decoding

Yepeng Weng
Qiao Hu
Xujie Chen
Li Liu
Dianwen Mei
Huishi Qiu
Jiang Tian
Zhongchao Shi

Speculative decoding is a promising approach for accelerating large language models. The primary idea is to use a lightweight draft model to speculate the output of the target model for multiple subsequent timesteps, and then verify them in parallel to determine whether the drafted tokens should be accepted or rejected. To enhance acceptance rates, existing frameworks typically construct token trees containing multiple candidates in each timestep. However, their reliance on token-level verification mechanisms introduces two critical limitations: First, the probability distribution of a sequence differs from that of individual tokens, leading to suboptimal acceptance length. Second, current verification schemes begin from the root node and proceed layer by layer in a top-down manner. Once a parent node is rejected, all its child nodes should be discarded, resulting in inefficient utilization of speculative candidates. This paper introduces Traversal Verification, a novel speculative decoding algorithm that fundamentally rethinks the verification paradigm through leaf-to-root traversal. Our approach considers the acceptance of the entire token sequence from the current node to the root, and preserves potentially valid subsequences that would be prematurely discarded by existing methods. We theoretically prove that the probability distribution obtained through Traversal Verification is identical to that of the target model, guaranteeing lossless inference while achieving substantial acceleration gains. Experimental results on various models and multiple tasks demonstrate that our method consistently improves acceptance length and throughput over token-level verification.

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EAAI Journal 2024 Journal Article

Smooth path planning under maximum curvature constraints for autonomous underwater vehicles based on rapidly-exploring random tree star with B-spline curves

Haobo Feng
Qiao Hu
Zhenyi Zhao
Xinglong Feng

In recent decades, Rapidly-exploring Random Tree star (RRT*) has garnered significant attention in the field of path planning due to its asymptotical optimality feature. However, the paths obtained by RRT* are comprised of polylines and too tortuous to be followed by underwater robots. To solve the drawback, this paper proposes a novel autonomous underwater vehicle (AUV) path planning method based on B-spline RRT* (BSRRT*). It focuses on planning optimal paths under maximum curvature constraints, which considerably improves the path smoothness. Different from conventional RRT*-based methods, the tree generated by BSRRT* is composed of piecewise B-spline curves that meet the curvature constraint. The analytical formulas of curve curvature and curve length enable BSRRT* to extend the tree with a low computational cost. Furthermore, start and end orientations constraints are imposed via the introduction of start node pairs and end node pairs. BSRRT* also combines with the expanded candidate strategy and the goal-biased strategy for a faster convergence rate. Simulation results demonstrate that compared to existing approaches, BSRRT* can provide shorter smooth paths with lower time costs.

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IROS Conference 2021 Conference Paper

Water Surface Stability Prediction of Amphibious Bio-Inspired Undulatory Fin Robot

Zhenhan Chen
Qiao Hu
Yingliang Chen
Chang Wei
Shenglin Yin

To solve the interference problems of wind and wave action and load movement when switching under water surface conditions in the marine environment, a study on the water surface stability prediction of the bio-inspired undulatory fin robot is carried out. Based on the fin motion equation and fluid drag theory, a water surface stability calculation model of the robot is established. The study compares the effects of different loads and heel angles on the stability of the robot's water surface under different calculation methods and verifies the validity of the model through computational fluid dynamics methods. The simulation results show that the water surface stability of the robot exhibits sinusoidal-like changes over time, which is equal to the undulatory fin period. The stability decreases with the increase of the drainage volume. When the drainage volume is constant, the stability first increases and then decreases with the increase in heel angle. The theoretical calculation results are consistent with the numerical results, which verify the effectiveness of the water surface stability prediction model proposed in this paper. It can provide a theoretical basis for the optimization design of water surface stability of the undulatory fin robot.

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