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Modeling and control of a sperm-inspired robot with helical propulsion

Bioinspiration and Biomimetics 2025年第2期20卷 026007-026007页

作者： Deng, Liangwei Zhou, Chao Zhang, Zhuoliang Liao, Xiaocun Fan, Junfeng Wang, Xiaofei Zhang, Jiaming The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Efficient propulsion has been a central focus of research in the field of biomimetic underwater vehicles. Compared to the prevalent fish-like reciprocating flapping propulsion mode, the sperm-like helical propulsion mode features higher efficiency and superior performance in high-viscosity environments. Based on the previously developed sperm-inspired robot, this paper focuses on its dynamic modeling and depth control research. The helical propulsion performance of the sperm-inspired robot is analyzed by resistance-theory-based force analysis, followed by the application of Kirchhoff rod theory to determine the helical waveform parameters. The dynamic model of the sperm-inspired robot is established using the Kirchhoff equation, and its validity is verified through experiments. To enhance the practical application capability of the sperm-inspired robot, this study develops an active disturbance rejection control depth controller for roll-spin coupling motion based on the constructed dynamics model. The effectiveness of the controller is thoroughly validated through a combination of simulation and experiment. Experimental results demonstrate the excellent depth control ability of the robot, with an average depth error controlled within 0.19 cm. This superior performance lays a foundation for the future application of our robot in underwater operations. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： Disturbance rejection

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A Chain-Driven, Sandwich-Legged Quadruped Robot: Design and Experimental Analysis

arXiv

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arXiv 2025年

作者： Singh, Aman Goswami, Bhavya Giri Nehete, Ketan Kolathaya, Shishir N.Y. Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bengaluru India University of Waterloo WaterlooON Canada UC Berkeley BerkeleyCA United States Robert Bosch Center for Cyber Physical Systems Department of Computer Science & Automation Indian Institute of Science Bengaluru India

This paper introduces a chain-driven, sandwich-legged, mid-size quadruped robot designed as an accessible research platform. The design prioritizes enhanced locomotion capabilities, improved reliability and safety of the actuation system, and simplified, cost-effective manufacturing processes. Locomotion performance is optimized through a sandwiched leg design and a dual-motor configuration, reducing leg inertia for agile movements. Reliability and safety are achieved by integrating robust cable strain reliefs, efficient heat sinks for motor thermal management, and mechanical limits to restrict leg motion. Simplified design considerations include a quasi-direct drive (QDD) actuator and the adoption of low-cost fabrication techniques, such as laser cutting and 3D printing, to minimize cost and ensure rapid prototyping. The robot weighs approximately 25 kg and is developed at a cost under $8000, making it a scalable and affordable solution for robotics research. Experimental validations demonstrate the platform’s capability to execute trot and crawl gaits on flat terrain and slopes, highlighting its potential as a versatile and reliable quadruped research platform. © 2025, CC BY.

关键词： Machine design

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Sanam Narejo Muhammad Taimoor Khan Muhammad Zakir Shaikh Lubna Luxmi Dhirani Bhawani Shankar Chowdhery Department of Computer Systems Mehran Univerity of Engineering and Technology Jamshoro Pakistan National Centre for Robotics Automation and Artificial Intelligence Mehran Univerity of Engineering and Technology Jamshoro Pakistan Mechanical Engineering and energy efficiency School of Industrial Engineering University of Malaga Malaga Spain Department of Electronic and Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car’s navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car’s driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car’s evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations.

关键词： Navigation Tracking Neural networks Decision making Reinforcement learning Radar tracking Topology Sensors Automobiles Vehicle dynamics

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Leveraging Privileged Information for Partially Observable Reinforcement Learning

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IEEE Transactions on Games 2025年

作者： Li, Jinqiu Zhao, Enmin Wei, Tong Xing, Junliang Xiang, Shiming Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

Reinforcement learning has achieved remarkable success across diverse scenarios. However, learning optimal policies within partially observable games remains a formidable challenge. Crucial privileged information in states is often shrouded during gameplay, yet ideally, it should be accessible and exploitable during training. Previous studies have concentrated on formulating policies based wholly on partial observations or oracle states. Nevertheless, these approaches often face hindrances in attaining effective generalization. To surmount this challenge, we propose the Actor-Cross-Critic (ACC) learning framework, integrating both partial observations and oracle states. ACC achieves this by coordinating two critics and invoking a maximization operation mechanism to switch between them dynamically. This approach encourages the selection of the higher values when computing advantages within the actor-critic framework, thereby accelerating learning and mitigating bias under partial observability. Some theoretical analyses show that ACC exhibits better learning ability towards optimal policies than actor-critic learning using the oracle states. We highlight its superior performance through comprehensive evaluations in decision-making tasks such as QuestBall, Minigrid, Atari, and the challenging card game DouDizhu. The code is available at https://***/lijinqiu2021/acc. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Reinforcement learning

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Human-Robot Pose Tracking Based on CNN with Color and Geometry Aggregation 16th

Human-Robot Pose Tracking Based on CNN with Color and Geo...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Xu, Yue Zhang, Yinlong Liu, Shuai Liu, Yuanhao Liang, Wei He, Hongsheng Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9789819611508

Accurately tracking the robotic arm and human joints is crucial to ensure safety during human-robot interaction. However, traditional pose tracking methods often exhibit insufficient performance and robustness in complex environments. The variations in the robotic arm’s environment and posture make it challenging for traditional methods to accurately capture the positions and posture of its joints. Specifically, when addressing challenges such as high similarity, occlusion, background complexity, and joint recognition failures, they often struggle to provide reliable and accurate results. To address these challenges, this paper proposes a human-robot pose tracking algorithm based on a new convolutional neural network model. To enhance detection accuracy, an improved color detection module is introduced to resolve joint misclassification. A geometric perception module is designed to accurately locate joints even under occlusion. Additionally, innovative iEMA and DBB modules are incorporated. The iEMA module employs edge detection technology to dynamically adjust thresholds for correct matching by improving the edge matching process. The DBB module refines the boundary box parameters for precise localization by introducing adaptive bounding box updates in real-time. This algorithm also integrates human pose recognition, enabling real-time pose recognition, thereby facilitating more intelligent and natural human-robot interaction. The algorithm has been rigorously evaluated on a custom-designed robotic arm platform. Experimental results validate the algorithm’s effectiveness and feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction Data mining Facial features

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A Hybrid Data-Driven Granular Model for Battery Remaining Useful Life Prediction

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jing, TaiLong Du, Sheng Wang, Cong Wu, Chao Pedrycz, Witold Sharaf, Mohamed Li, ZhiWu Luoyang Institute of Science and Technology School of Electrical Engineering and Automation Luoyang471023 China China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi’an710072 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland King Saud University College of Engineering Department of Industrial Engineering Riyadh11421 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau China

Accurate and meaningful prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) provides a significant guide for the maintenance and management of these LIBs. A singular numerical predictive result by numerous conventional models is insufficient to furnish decision-makers with comprehensive or nuanced insights. To address this issue, predictive RUL results in the form of information granules are essential. In this study, a hybrid data-driven granular model is established for battery RUL prediction with granular (interval-based) quantification for LIBs. This granular model is designed and formulated in a way that it produces comprehensive prediction outcomes realized as information granules, which are expressed in a more descriptive and comprehensive manner. Thus, the resulting information granules can effectively reflect and communicate the confidence associated with the predictions. The granular model is realized in two steps: 1) probabilistic prediction results are produced by a hybrid numeric predictive model and 2) a comprehensive granulation of the probabilistic results is achieved under the principle of information granularity. The granular predictive results are holistically evaluated and optimized by the two associated criteria of granularity (coverage and specificity). This transformation of information granules provides an intuitive and interpretable display for battery maintenance and safe work. The proposed granular model is verified by using an open-access LIBs dataset, and the experimental results demonstrate that the proposed granular model outperforms other comparative methods. © 2025 IEEE. All rights reserved.

关键词： Prediction models

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Robust Co-Optimization of Distribution Network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

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Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitudes. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic programming

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