Biomimetic Intelligence and Robotics最新文献_第3页

Exoskeletons in the context of soldiers: Current status and future research trends 士兵外骨骼：现状与未来研究趋势

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-08-13 DOI: 10.1016/j.birob.2025.100254

Xinmeng Ma , Lingfeng Lv , Weipeng Liu , Feng Niu , Haihang Wang , Haoyu Wang , Libin Zhao , Zihao Wang , Zhipu Wang

Modern military drills and conventional training, performed under all-weather conditions, impose exacting challenges on soldiers. This has motivated the development of exoskeleton robot systems, leveraging advanced technology and material innovation. These systems have demonstrated their effectiveness at assisting movement, enhancing protection, promoting rehabilitation, and providing comprehensive support to soldiers. This groundbreaking technology not only reduces a soldier’s physical exertion significantly but also effectively diminishes the risk of injury during training, infusing new vitality into the enhancement of military capabilities. Different types of exoskeleton robots differ in their focus. Lower-limb exoskeleton robots are designed to increase the soldier’s endurance. Upper-limb exoskeleton robots enhance strength. This paper provides a detailed explanation of the key technologies of various types of exoskeleton robots, covering their mechanical design, electromechanical transmission structures, sensors, and actuation methods. It also explores the diverse application scenarios of exoskeleton robots in the military field, systematically introducing their development trajectory, milestone achievements, and the cutting-edge technologies currently employed, as well as the challenges faced. The conclusion offers a prospective discussion of future development pathways, anticipating the broad prospects for exoskeleton robots in the military domain.

在全天候条件下进行的现代军事演习和常规训练对士兵提出了严格的挑战。这推动了外骨骼机器人系统的发展，利用先进的技术和材料创新。这些系统已经证明了它们在协助行动、加强保护、促进康复和为士兵提供全面支持方面的有效性。这项突破性的技术不仅大大减少了士兵的体力消耗，而且有效地降低了训练中受伤的风险，为提高军事能力注入了新的活力。不同类型的外骨骼机器人的关注点不同。设计下肢外骨骼机器人是为了提高士兵的耐力。上肢外骨骼机器人增强力量。本文详细介绍了各类外骨骼机器人的关键技术，包括其机械设计、机电传动结构、传感器和驱动方法。探讨了外骨骼机器人在军事领域的多种应用场景，系统介绍了外骨骼机器人的发展轨迹、里程碑式成果、目前采用的前沿技术以及面临的挑战。结论部分对未来的发展路径进行了前瞻性的讨论，展望了外骨骼机器人在军事领域的广阔前景。

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引用次数: 0

Wheeled-legged robots for multi-terrain locomotion in plateau environments 高原环境中多地形运动的轮腿机器人

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-08-13 DOI: 10.1016/j.birob.2025.100256

Kang Wang , Jinmian Hou , Shichao Zhou , Dachuang Wei , Wei Xu , Yulin Wang , Hui Chai , Lingkun Chen , Qiuguo Zhu , Liang Gao , Min Guo , Guoteng Zhang , Zhongqu Xie , Tuo Liu , Mingyue Zhu , Yueming Wang , Tong Yan , Jingsong Gao , Meng Hong , Weikai Ding

Wheeled-legged robots integrate the mobility efficiency of wheeled platforms with the terrain adaptability of legged robots, making them ideal for complex, unstructured environments. However, balancing high payload capacity with agile multimodal locomotion remains a major challenge. This paper presents a field study conducted in the high-altitude region of Golmud, Qinghai, with elevations ranging from 2800 m to 4000 m. We evaluate three wheeled-legged robot platforms of different scales on diverse terrains including Gobi, desert, grassland, and wetlands. Our experiments demonstrate the robot’s robust locomotion performance across multimodal tasks such as obstacle crossing, slope climbing, and terrain classification. Moreover, we validate the performance of autonomous perception systems, including real-time localization and 3D mapping, under harsh plateau conditions. The results provide valuable insights into the deployment of wheeled-legged robots in extreme natural environments and lay a solid foundation for future applications in inspection, rescue, and transport missions in high-altitude regions.

轮腿机器人将轮式平台的移动效率与腿式机器人的地形适应性相结合，使其成为复杂、非结构化环境的理想选择。然而，平衡高负载能力和灵活的多模式运动仍然是一个主要挑战。本文在海拔2800 ~ 4000 m的青海格尔木高海拔地区进行了野外研究。我们在戈壁、沙漠、草原和湿地等不同地形上对不同尺度的三轮腿机器人平台进行了评估。我们的实验证明了机器人在跨障碍、爬坡和地形分类等多模式任务中的鲁棒运动性能。此外，我们验证了自主感知系统的性能，包括实时定位和3D映射，在恶劣的高原条件下。研究结果为轮式腿机器人在极端自然环境中的部署提供了有价值的见解，并为未来在高海拔地区的检查、救援和运输任务中的应用奠定了坚实的基础。

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引用次数: 0

Communication-aided multi-UAV collision detection and avoidance based on two-stage curriculum reinforcement learning 基于两阶段课程强化学习的通信辅助多无人机碰撞检测与避碰

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-07-25 DOI: 10.1016/j.birob.2025.100253

Guanzheng Wang , Xiangke Wang , Zhiqiang Miao , Zhihong Liu , Xinyu Hu

Currently, multi-UAV collision detection and avoidance is facing many challenges, such as navigating in cluttered environments with dynamic obstacles while equipped with low-cost perception devices having a limited field of view (FOV). To this end, we propose a communication-aided collision detection and avoidance method based on curriculum reinforcement learning (CRL). This method integrates perception and communication data to improve environmental understanding, allowing UAVs to handle potential collisions that may go unnoticed. Furthermore, given the challenges in policy learning caused by the substantial differences in scale between perception and communication data, we employ a two-stage training approach, which performs training with the network expanded from part to whole. In the first stage, we train a partial policy network in an obstacle-free environment for inter-UAV collision avoidance. In the second stage, the full network is trained in a complex environment with obstacles, enabling both inter-UAV collision avoidance and obstacle avoidance. Experiments with PX4 software-in-the-loop (SITL) simulations and real flights demonstrate that our method outperforms state-of-the-art baselines in terms of reliability of collision avoidance, including the DRL-based method and NH-ORCA (Non-Holonomic Optimal Reciprocal Collision Avoidance). Besides, the proposed method achieves zero-shot transfer from simulation to real-world environments that were never experienced during training.

当前，多无人机碰撞检测与避撞面临着许多挑战，例如在具有动态障碍物的混乱环境中导航，同时配备的低成本感知设备具有有限的视场（FOV）。为此，我们提出了一种基于课程强化学习（CRL）的通信辅助碰撞检测和避免方法。这种方法集成了感知和通信数据，以提高对环境的理解，使无人机能够处理可能未被注意到的潜在碰撞。此外，考虑到感知数据和通信数据之间规模的巨大差异导致的政策学习挑战，我们采用了两阶段训练方法，该方法将网络从部分扩展到整体进行训练。在第一阶段，我们在无障碍环境中训练了一个局部策略网络，用于无人机间的避碰。在第二阶段，整个网络在具有障碍物的复杂环境中进行训练，使无人机之间的避碰和避障成为可能。PX4软件在环（SITL）模拟和真实飞行的实验表明，我们的方法在避碰可靠性方面优于最先进的基线，包括基于drl的方法和NH-ORCA（非完整最优互反避碰）。此外，该方法还实现了从模拟到真实环境的零射击转移，这是训练中从未经历过的。

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引用次数: 0

Tracking control for Pneumatic muscle actuators with unknown dynamics and output constraints 具有未知动力学和输出约束的气动肌肉执行器跟踪控制

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-07-22 DOI: 10.1016/j.birob.2025.100252

Xingchen Li , Xifeng Gao

Among the various soft actuators explored for robotic applications, the pneumatic muscle actuators (PMAs) stand out because of many advantages, such as compliant structures, high power-to-weight/volume ratios, and lightweight materials. Despite these advantages, their inherent nonlinearities and time-varying dynamics pose significant challenges for tracking control. To tackle this challenge, we present a robust control method that is structurally simple and computationally inexpensive. Such a method is comprised of an error transformation scheme, which is deeply explored to withstand model uncertainties to accomplish the output tracking with assigned accuracy, and a tuning function for relaxing requirements on the initial conditions. Experimental results of the PMA are presented to validate the concepts.

在机器人应用的各种软致动器中，气动肌肉致动器（pma）因其柔韧的结构、高功率/重量/体积比和轻质材料等诸多优势而脱颖而出。尽管有这些优点，但它们固有的非线性和时变动力学给跟踪控制带来了重大挑战。为了应对这一挑战，我们提出了一种结构简单且计算成本低廉的鲁棒控制方法。该方法由误差转换方案和调节函数组成，前者深入研究了误差转换方案以承受模型不确定性，实现给定精度的输出跟踪，后者放宽了对初始条件的要求。给出了PMA的实验结果来验证这些概念。

引用次数: 0

Kirigami analogies for parallelogram-based remote-center-of-motion mechanisms 基于平行四边形的远程运动中心机制的Kirigami类比

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-07-18 DOI: 10.1016/j.birob.2025.100251

Bok Seng Yeow , Alex Wang , Chin-Hsing Kuo , Hongliang Ren

This paper presents a framework for applying origami-kirigami techniques to design kirigami analogies for remote center-of-motion (RCM) mechanisms, specifically targeting minimally invasive keyhole procedures. The proposed kirigami RCM analogs emulate the motions of existing bar-linkage RCMs, offering advantages in deployability, transportability, and simplified fabrication. A workflow is introduced to transition from initial crease patterns to functional kirigami equivalents, demonstrating the potential for customizability and scalability. Furthermore, a proof-of-concept kirigami RCM under magnetic actuation is presented, showcasing its ability to reduce structural profile during transportation and improve device deployment. Three representative parallelogram-based RCM mechanisms: coupled dual parallelogram, back-drivable, and triple parallelogram, are transformed into kirigami analogs, highlighting the versatility of the design approach. The discussion includes computational modeling, fabrication considerations, and potential applications in MIS robots. This work contributes to the development of compact, deployable, and cost-effective RCM mechanisms for robotic keyhole procedures. This approach can also further facilitate the education of RCM mechanisms and the hands-on demonstration of small-scale RCM concepts.

本文提出了一个应用折纸-基里伽米技术设计基里伽米类比的框架，用于远程运动中心（RCM）机制，特别是针对微创锁孔手术。提出的kirigami RCM模拟了现有杆连接RCM的运动，在可部署性、可移植性和简化制造方面具有优势。介绍了从初始折痕模式过渡到功能性kirigami的工作流，展示了可定制性和可扩展性的潜力。此外，提出了磁驱动下的kirigami RCM概念验证，展示了其在运输过程中减少结构轮廓和改善设备部署的能力。三种具有代表性的基于平行四边形的RCM机制：耦合双平行四边形、反向驱动和三重平行四边形，被转化为kirigami类比，突出了设计方法的通用性。讨论内容包括计算建模、制造注意事项以及MIS机器人的潜在应用。这项工作有助于为机器人锁孔程序开发紧凑、可部署和具有成本效益的RCM机制。这种方法还可以进一步促进RCM机制的教育和小规模RCM概念的实际演示。

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引用次数: 0

Literature survey on machine learning techniques for enhancing accuracy of myoelectric hand gesture recognition in real-world prosthetic hand control 提高实际假手控制中肌电手势识别准确性的机器学习技术文献综述

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-07-11 DOI: 10.1016/j.birob.2025.100250

Hongquan Le , Marc in Het Panhuis , Gursel Alici

The human hand, essential for performing daily tasks and facilitating social interaction, is indispensable to everyday life. Millions worldwide experience varying levels of amputation, profoundly affecting their physical, emotional, and psychological well-being, limiting independence, and reducing quality of life. Myoelectric prosthetics, the most advanced active prosthetic hands, use surface electromyography (sEMG) signals and pattern recognition to translate user intentions into control signals. Despite these advancements, high rejection rates persist due to the non-stationarity of sEMG signals, leading to inconsistent and often frustrating user experiences. As a result, clinical and academic research has increasingly focused on improving myoelectric hand gesture recognition under real-world conditions to reduce rejection rates and enhance user acceptance of myoelectric prostheses. Given the vast and diverse range of methods applied in previous research, this survey aims to systematically highlight key studies and provide an overview of the field’s current achievements. Furthermore, research on machine learning for myoelectric hand gesture recognition has been largely influenced by unrelated fields of computer science, such as computer vision and natural language processing. However, myoelectric hand gesture recognition presents unique challenges, particularly severe and unpredictable covariate shifts in sEMG signals, which require specialized approaches. To address these challenges, we propose a new taxonomy for categorizing machine learning models based on feature extraction methods and decision boundary strategies. Additionally, this paper highlights the need for benchmark datasets that accurately reflect real-world conditions and emphasizes the importance of re-evaluating real-time performance, particularly when using long temporal contextual windows. This study concludes with research challenges and future research directions to enhance the accuracy of myoelectric hand gesture recognition using machine learning techniques.

人的手对于完成日常任务和促进社会互动至关重要，是日常生活中不可或缺的。全世界数百万人经历了不同程度的截肢，这深刻地影响了他们的身体、情感和心理健康，限制了他们的独立性，降低了他们的生活质量。肌电义肢是目前最先进的主动义肢，它利用表面肌电图（sEMG）信号和模式识别将用户意图转化为控制信号。尽管取得了这些进步，但由于表面肌电信号的非平稳性，高拒绝率仍然存在，导致不一致和经常令人沮丧的用户体验。因此，临床和学术研究越来越关注于改善现实条件下的肌电手势识别，以降低排斥率，提高用户对肌电假肢的接受度。鉴于以前的研究中应用了广泛而多样的方法，本调查旨在系统地突出重点研究并提供该领域当前成就的概述。此外，肌电手势识别的机器学习研究在很大程度上受到计算机科学不相关领域的影响，如计算机视觉和自然语言处理。然而，肌电手势识别面临着独特的挑战，特别是表面肌电信号中严重和不可预测的协变量变化，这需要专门的方法。为了解决这些挑战，我们提出了一种新的基于特征提取方法和决策边界策略的机器学习模型分类方法。此外，本文强调了对准确反映现实世界条件的基准数据集的需求，并强调了重新评估实时性能的重要性，特别是在使用长时间上下文窗口时。本研究总结了利用机器学习技术提高肌电手势识别准确性的研究挑战和未来的研究方向。

{"title":"Literature survey on machine learning techniques for enhancing accuracy of myoelectric hand gesture recognition in real-world prosthetic hand control","authors":"Hongquan Le , Marc in Het Panhuis , Gursel Alici","doi":"10.1016/j.birob.2025.100250","DOIUrl":"10.1016/j.birob.2025.100250","url":null,"abstract":"<div><div>The human hand, essential for performing daily tasks and facilitating social interaction, is indispensable to everyday life. Millions worldwide experience varying levels of amputation, profoundly affecting their physical, emotional, and psychological well-being, limiting independence, and reducing quality of life. Myoelectric prosthetics, the most advanced active prosthetic hands, use surface electromyography (sEMG) signals and pattern recognition to translate user intentions into control signals. Despite these advancements, high rejection rates persist due to the non-stationarity of sEMG signals, leading to inconsistent and often frustrating user experiences. As a result, clinical and academic research has increasingly focused on improving myoelectric hand gesture recognition under real-world conditions to reduce rejection rates and enhance user acceptance of myoelectric prostheses. Given the vast and diverse range of methods applied in previous research, this survey aims to systematically highlight key studies and provide an overview of the field’s current achievements. Furthermore, research on machine learning for myoelectric hand gesture recognition has been largely influenced by unrelated fields of computer science, such as computer vision and natural language processing. However, myoelectric hand gesture recognition presents unique challenges, particularly severe and unpredictable covariate shifts in sEMG signals, which require specialized approaches. To address these challenges, we propose a new taxonomy for categorizing machine learning models based on feature extraction methods and decision boundary strategies. Additionally, this paper highlights the need for benchmark datasets that accurately reflect real-world conditions and emphasizes the importance of re-evaluating real-time performance, particularly when using long temporal contextual windows. This study concludes with research challenges and future research directions to enhance the accuracy of myoelectric hand gesture recognition using machine learning techniques.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 3","pages":"Article 100250"},"PeriodicalIF":5.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond performance: Explaining generalisation failures of Robotic Foundation Models in industrial simulation 超越性能：解释工业仿真中机器人基础模型的泛化失败

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-07-09 DOI: 10.1016/j.birob.2025.100249

David Kube , Simon Hadwiger , Tobias Meisen

This study investigates the generalisation and explainability challenges of Robotic Foundation Models (RFMs) in industrial applications, using Octo as a representative case study. Motivated by the scarcity of domain-specific data and the need for safe evaluation environments, we adopt a simulation-first approach: instead of transitioning from simulation to real-world scenarios, we aim to adapt real-world-trained RFMs to synthetic, simulated environments — a critical step towards their safe and effective industrial deployment. While Octo promises zero-shot generalisation, our experiments reveal significant performance degradation when applied in simulation, despite minimal task and observation domain shifts. To explain this behaviour, we introduce a modified Grad-CAM technique that enables insight into Octo’s internal reasoning and focus areas. Our results highlight key limitations in Octo’s visual generalisation and language grounding capabilities under distribution shifts. We further identify architectural and benchmarking challenges across the broader RFM landscape. Based on our findings, we propose concrete guidelines for future RFM development, with an emphasis on explainability, modularity, and robust benchmarking — critical enablers for applying RFMs in safety-critical and data-scarce industrial environments.

本研究探讨了机器人基础模型（rfm）在工业应用中的泛化和可解释性挑战，使用Octo作为代表性案例研究。由于领域特定数据的稀缺性和对安全评估环境的需求，我们采用了模拟优先的方法：而不是从模拟过渡到现实世界的场景，我们的目标是使现实世界训练的rfm适应合成的模拟环境-这是迈向其安全和有效的工业部署的关键一步。虽然Octo承诺零射击泛化，但我们的实验显示，尽管最小的任务和观察域移位，但在模拟中应用时，性能会显著下降。为了解释这种行为，我们引入了一种改进的Grad-CAM技术，可以深入了解Octo的内部推理和重点领域。我们的研究结果突出了Octo在分布变化下的视觉泛化和语言基础能力的关键限制。我们进一步确定了更广泛的RFM环境中的架构和基准测试挑战。根据我们的发现，我们提出了未来RFM开发的具体指导方针，重点是可解释性、模块化和健壮的基准测试——在安全关键和数据稀缺的工业环境中应用RFM的关键因素。

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引用次数: 0

A novel underactuated exoskeleton rehabilitation glove for hand flexion and extension training 一种用于手部屈伸训练的新型欠驱动外骨骼康复手套

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-07-01 DOI: 10.1016/j.birob.2025.100248

Lei Zhao , Fenghe Guo , Fei Yang , Chao Li , Yufei Zhao , Jing Qu , Kun Li , Yan Liu , Lili Wang , Lingguo Bu

In recent years, the number of stroke patients worldwide has been steadily increasing, with approximately 70% of survivors experiencing upper limb dysfunction, particularly severe impairment of fine motor skills in the hand. This limitation significantly reduces patients’ ability to perform daily activities and increases the burden on both families and society. Existing hand rehabilitation exoskeletons suffer from issues such as complex structures, high production and usage costs, and limited application scenarios. This paper presents a flexible and portable hand rehabilitation robotic device based on the anatomical structure and movement characteristics of the human hand. First, a flexible exoskeleton glove based on underactuation is designed to accommodate various finger sizes. The portable device allows for rehabilitation in both hospital and home environments. Second, Adams simulation is used to verify the structural feasibility of the designed exoskeleton. Finally, device testing is performed on subjects to assess the assistive performance and motor dexterity of the hand exoskeleton using joint angle similarity tests, object grasping experiments, and force distribution tests. The experimental results show that the hand exoskeleton prototype can assist finger joints in achieving significant flexion and extension movements. Moreover, by adjusting the driving forces at each joint, it can stabilize the grasping of objects with different sizes, providing a high level of motion assistance in daily object grasping and finger joint movements. This study offers a practical and feasible technological path to reduce disability rates and improve the quality of life for patients with hand dysfunction following a stroke.

近年来，世界范围内的中风患者数量稳步增加，约70%的幸存者出现上肢功能障碍，特别是手部精细运动技能严重受损。这种限制大大降低了患者进行日常活动的能力，增加了家庭和社会的负担。现有的手部康复外骨骼存在结构复杂、生产和使用成本高、应用场景有限等问题。基于人手的解剖结构和运动特点，设计了一种灵活便携的手部康复机器人装置。首先，设计了一种基于欠驱动的柔性外骨骼手套，以适应不同的手指尺寸。这种便携式设备可以在医院和家庭环境中进行康复。其次，采用Adams仿真验证所设计外骨骼结构的可行性。最后，通过关节角度相似测试、物体抓取实验和力分布测试，对受试者进行设备测试，评估手外骨骼的辅助性能和运动灵巧性。实验结果表明，手外骨骼原型可以辅助手指关节实现明显的屈伸运动。此外，通过调节各关节的驱动力，可以稳定抓取不同大小的物体，为日常抓取物体和手指关节运动提供高水平的运动辅助。本研究为脑卒中后手功能障碍患者降低致残率、提高生活质量提供了一条切实可行的技术途径。

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引用次数: 0

Design and dynamics modeling of a hybrid drive bionic robotic fish 混合驱动仿生机器鱼的设计与动力学建模

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-06-25 DOI: 10.1016/j.birob.2025.100247

Haoyu Huang, Shuai Xian, Chengye Xiong, Weihua Li, Yong Zhong

While recent advancements in hybrid propulsion systems for bionic robotic fish—combining biomimetic mechanisms with classical vector thrusters—demonstrate enhanced locomotion capabilities and application potential, challenges remain in modeling the coupled dynamics of heterogeneous propulsion mechanisms. This paper presents a hybrid-drive robotic fish architecture that synergistically integrates pectoral-fin-mounted propellers with a caudal-fin-based propulsion system. A three-dimensional dynamical model is developed to characterize the coupled interactions between the dual propulsion modes, incorporating a hydrodynamic computation framework that accounts for propeller wake effects on caudal fin performance. Systematic experimental validation confirms the model’s fidelity through quantitative analysis of swimming performance metrics, including cruising speed, turning radius, and trajectory tracking. The results show that the proposed hybrid propulsion strategy can effectively improve the swimming performance of the robotic fish, and the model can effectively predict the motions such as speed, turning diameter, and trajectory of the robotic fish, which provides a new idea for the development of bionic robotic fish.

虽然仿生机器鱼的混合推进系统最近取得了进展——将仿生机制与经典矢量推进器相结合——展示了增强的运动能力和应用潜力，但在建模异质推进机制的耦合动力学方面仍然存在挑战。本文提出了一种混合驱动机器鱼结构，该结构将安装在胸鳍上的螺旋桨与基于尾鳍的推进系统协同集成。建立了双推进模式耦合相互作用的三维动力学模型，并结合了考虑尾流对尾鳍性能影响的水动力计算框架。通过对游泳性能指标（包括巡航速度、转弯半径和轨迹跟踪）的定量分析，系统实验验证了该模型的保真度。结果表明，所提出的混合推进策略能有效提高机器鱼的游动性能，模型能有效预测机器鱼的速度、转弯直径、轨迹等运动，为仿生机器鱼的发展提供了新的思路。

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引用次数: 0

Joint moment estimation for hip exoskeleton control: A generalized moment feature generation method 髋部外骨骼控制关节力矩估计：一种广义矩特征生成方法

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-06-09 DOI: 10.1016/j.birob.2025.100246

Yuanwen Zhang , Jingfeng Xiong , Haolan Xian , Chuheng Chen , Xinxing Chen , Haipeng Liang , Chenglong Fu , Yuquan Leng

Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 ± 0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction (

p < 0.01

) for users compared to assist-off condition in level-ground walking.

行走过程中的髋关节力矩是髋关节外骨骼辅助控制的重要基础。最近的大多数研究表明，与基于步态估计生成辅助扭矩曲线相比，即时估计髋关节力矩具有许多优势，例如简单的传感器要求和对可变步行速度的适应性。然而，现有的关节力矩估计方法仍然缺乏个性化，导致新用户的估计精度下降。针对这一问题，提出了一种基于广义矩特征（GMF）的髋关节矩估计方法。构造了一个GMF生成器来学习关节矩的GMF，该GMF对个体变化保持不变，同时通过专用解码器可解码为关节矩。利用这种良好的特征表示，利用基于gru的神经网络，利用髋关节外骨骼编码器易于获取的关节运动学数据来预测GMF。在跑步机数据集上，在28种步行速度条件下，该方法的估计均方根误差为0.1180±0.0021 Nm/kg，比不融合身体参数的模型提高6.5%，比融合身体参数的传统融合模型提高8.3%。此外，所提出的方法被用于只有编码器传感器的髋关节外骨骼，与平地行走的辅助条件相比，用户的代谢平均减少20.5% （p<0.01）。

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引用次数: 0