Biomimetic Intelligence and Robotics最新文献_第5页

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。

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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

Snake-inspired trajectory planning and control for confined pipeline inspection with hyper-redundant manipulators 基于超冗余机械手的受限管道检测蛇形轨迹规划与控制

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-06-03 DOI: 10.1016/j.birob.2025.100245

Junjie Zhu , Mingming Su , Longchuan Li , Yuxuan Xiang , Jianming Wang , Xuan Xiao

The hyper-redundant manipulator (HRM) can explore narrow and curved pipelines by leveraging its high flexibility and redundancy. However, planning collision-free motion trajectories for HRMs in confined environments remains a significant challenge. To address this issue, a pipeline inspection approach that combines nonlinear model predictive control (NMPC) with the snake-inspired crawling algorithm(SCA) is proposed. The approach consists of three processes: insertion, inspection, and exit. The insertion and exit processes utilize the SCA, inspired by snake motion, to significantly reduce path planning time. The inspection process employs NMPC to generate collision-free motion. The prototype HRM is developed, and inspection experiments are conducted in various complex pipeline scenarios to validate the effectiveness and feasibility of the proposed method. Experimental results demonstrate that the approach effectively minimizes the computational cost of path planning, offering a practical solution for HRM applications in pipeline inspection.

超冗余机械手利用其高灵活性和冗余性，可以探索狭窄弯曲的管道。然而，在受限环境中规划hrm的无碰撞运动轨迹仍然是一个重大挑战。为了解决这一问题，提出了一种将非线性模型预测控制（NMPC）与蛇启发爬行算法（SCA）相结合的管道检测方法。该方法包括三个过程：插入、检查和退出。插入和退出过程利用SCA，灵感来自蛇的运动，以显著减少路径规划时间。检测过程采用NMPC产生无碰撞运动。开发了原型HRM，并在各种复杂的管道场景下进行了检测实验，验证了所提方法的有效性和可行性。实验结果表明，该方法有效地降低了路径规划的计算成本，为人力资源管理在管道检测中的应用提供了一种实用的解决方案。

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引用次数: 0

Genetic Informed Trees (GIT*): Path planning via reinforced genetic programming heuristics 遗传信息树（GIT*）：基于强化遗传规划启发式的路径规划

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-05-20 DOI: 10.1016/j.birob.2025.100237

Liding Zhang , Kuanqi Cai , Zhenshan Bing , Chaoqun Wang , Alois Knoll

Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective. This process relies on heuristic functions to guide the search direction. While a robust function can improve search efficiency and solution quality, current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships. This study introduces Genetic Informed Trees (GIT*), which improves upon Effort Informed Trees (EIT*) by integrating a wider array of environmental data, such as repulsive forces from obstacles and the dynamic importance of vertices, to refine heuristic functions for better guidance. Furthermore, we integrated reinforced genetic programming (RGP), which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT*. RGP leverages a multitude of data types, thereby improving computational efficiency and solution quality within a set timeframe. Comparative analyses demonstrate that GIT* surpasses existing single-query, sampling-based planners in problems ranging from

R^{4}

to

R^{16}

and was tested on a real-world mobile manipulation task. A video showcasing our experimental results is available at https://youtu.be/URjXbc_BiYg.

最优路径规划包括在起点和目标之间找到一个可行的状态序列，以优化目标。该过程依靠启发式函数来指导搜索方向。虽然鲁棒函数可以提高搜索效率和求解质量，但由于信息关系的复杂性，目前的方法往往忽略了可用的环境数据，并简化了函数结构。本研究引入了遗传信息树（GIT*），它在努力信息树（EIT*）的基础上改进了遗传信息树（GIT*），通过整合更广泛的环境数据，如障碍物的排斥力和顶点的动态重要性，来改进启发式函数，以获得更好的指导。此外，我们将强化遗传规划（RGP）与奖励系统反馈相结合，对GIT*的基因型生成启发式函数进行了突变。RGP利用多种数据类型，从而在设定的时间范围内提高计算效率和解决方案质量。对比分析表明，GIT*在R4到R16的问题中超越了现有的单查询、基于抽样的计划器，并在现实世界的移动操作任务中进行了测试。展示我们实验结果的视频可以在https://youtu.be/URjXbc_BiYg上找到。

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引用次数: 0

Image segmentation network for laparoscopic surgery 用于腹腔镜手术的图像分割网络

Biomimetic Intelligence and Robotics

Pub Date : 2025-05-06 DOI: 10.1016/j.birob.2025.100236

Kang Peng , Yaoyuan Chang , Guodong Lang , Jian Xu , Yongsheng Gao , Jiajun Yin , Jie Zhao

Surgical image segmentation serves as the foundation for laparoscopic surgical navigation technology. The indistinct local features of biological tissues in laparoscopic image pose challenges for image segmentation. To address this issue, we develop an image segmentation network tailored for laparoscopic surgery. Firstly, we introduce the Mixed Attention Enhancement (MAE) module that sequentially conducts the Channel Attention Enhancement (CAE) module and the Global Feature Enhancement (GFE) module linked in series. The CAE module enhances the network’s perception of prominent channels, allowing feature maps to exhibit clear local features. The GFE module is capable of extracting global features from both the height and width dimensions of images and integrating them into three-dimensional features. This enhancement improves the network’s ability to capture global features, thereby facilitating the inference of regions with indistinct local features. Secondly, we propose the Multi-scale Feature Fusion (MFF) module. This module expands the feature map into various scales, further enlarging the network’s receptive field and enhancing perception of features at multiple scales. In addition, we tested the proposed network on the EndoVis 2018 and a human minimally invasive liver resection image segmentation dataset, comparing it against six other advanced image segmentation networks. The comparative test results demonstrate that the proposed network achieves the most advanced performance on both datasets, proving its potential in improving surgical image segmentation outcome. The codes of MAMNet are available at: https://github.com/Pang1234567/MAMNet.

手术图像分割是腹腔镜手术导航技术的基础。腹腔镜图像中生物组织的局部特征不明确，给图像分割带来了挑战。为了解决这个问题，我们开发了一个适合腹腔镜手术的图像分割网络。首先，我们介绍了混合注意增强（MAE）模块，该模块依次将信道注意增强（CAE）模块和全局特征增强（GFE）模块串联起来。CAE模块增强了网络对突出通道的感知，允许特征图显示清晰的局部特征。GFE模块能够从图像的高度和宽度两个维度提取全局特征，并将其整合为三维特征。这种增强提高了网络捕获全局特征的能力，从而促进了局部特征不明确的区域的推断。其次，提出了多尺度特征融合（MFF）模块。该模块将特征映射扩展到不同的尺度，进一步扩大了网络的接受野，增强了对多尺度特征的感知。此外，我们在EndoVis 2018和人类微创肝切除图像分割数据集上测试了所提出的网络，并将其与其他六种先进的图像分割网络进行了比较。对比测试结果表明，本文提出的网络在两个数据集上都取得了最先进的性能，证明了其在提高手术图像分割效果方面的潜力。MAMNet的代码可在https://github.com/Pang1234567/MAMNet获得。

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引用次数: 0

An adaptive compensation strategy for sensors based on the degree of degradation 基于退化程度的传感器自适应补偿策略

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-04-30 DOI: 10.1016/j.birob.2025.100235

Yanbin Li , Wei Zhang , Zhiguo Zhang , Xiaogang Shi , Ziruo Li , Mingming Zhang , Wenzheng Chi

Simultaneous Localization and Mapping (SLAM) is widely used to solve the localization problem of unmanned devices such as robots. However, in degraded environments, the accuracy of SLAM is greatly reduced due to the lack of constrained features. In this article, we propose a deep learning-based adaptive compensation strategy for sensors. First, we create a dataset dedicated to training a degradation detection model, which contains coordinate data of particle swarms with different distributional features, and endow the model with degradation detection capability through supervised learning. Second, we design a lightweight network model with short computation time and good accuracy for real-time degradation detection tasks. Finally, an adaptive compensation strategy for sensors based on the degree of degradation is designed, where the SLAM is able to assign different weights to the sensor information according to the degree of degradation given by the model, to adjust the contribution of different sensors in the pose optimization process. We demonstrate through simulation experiments and real experiments that the robustness of the improved SLAM in degraded environments is significantly enhanced, and the accuracy of localization and mapping are improved.

同时定位与制图（SLAM）被广泛应用于解决机器人等无人设备的定位问题。然而，在退化环境中，由于缺乏约束特征，SLAM的精度大大降低。在本文中，我们提出了一种基于深度学习的传感器自适应补偿策略。首先，我们创建一个专门用于训练退化检测模型的数据集，该数据集包含具有不同分布特征的粒子群坐标数据，并通过监督学习赋予模型退化检测能力。其次，针对实时退化检测任务，设计了计算时间短、精度好的轻量级网络模型。最后，设计了基于退化程度的传感器自适应补偿策略，SLAM能够根据模型给出的退化程度对传感器信息赋予不同的权重，以调整不同传感器在位姿优化过程中的贡献。通过仿真实验和实际实验证明，改进后的SLAM在退化环境下的鲁棒性显著增强，定位和映射精度得到提高。

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引用次数: 0

Tendon friction compensation and slack avoidance for trajectory tracking control of the tendon-driven medical continuum manipulator 肌腱驱动医疗连续体机械臂轨迹跟踪控制的肌腱摩擦补偿与松弛避免

IF 5.4

Biomimetic Intelligence and Robotics

Pub Date : 2025-04-23 DOI: 10.1016/j.birob.2025.100234

Pengyu Du , Jianxiong Hao , Kun Qian , Yue Zhang , Zhiqiang Zhang , Chaoyang Shi

Tendon-driven continuum manipulators can perform tasks in confined environments due to their flexibility and curvilinearity, especially in minimally invasive surgeries. However, the friction along tendons and tendon slack present challenges to their motion control. This work proposes a trajectory tracking controller based on adaptive fuzzy sliding mode control (AFSMC) for the tendon-driven continuum manipulators. It consists of a sliding mode control (SMC) law with two groups of adaptive fuzzy subcontrollers. The first one is utilized to estimate and compensate for friction forces along tendons. The second one adapts the switching terms of SMC to alleviate the chattering phenomenon and enhance control robustness. To prevent tendon slack, an antagonistic strategy along with the AFSMC controller is adopted to allocate driving forces. Simulation and experiment studies have been conducted to investigate the efficacy of the proposed controller. In free space experiments, the AFSMC controller generates an average root-mean-square error (RMSE) of 0.42% compared with 0.90% of the SMC controller. In the case of a 50 g load, the proposed controller reduces the average RMSE to 1.47% compared with 4.29% of the SMC controller. These experimental results demonstrate that the proposed AFSMC controller has high control accuracy, robustness, and reduced chattering.

肌腱驱动的连续机械臂由于其灵活性和曲线性可以在受限环境中执行任务，特别是在微创手术中。然而，沿肌腱和肌腱松弛的摩擦对其运动控制提出了挑战。提出了一种基于自适应模糊滑模控制（AFSMC）的肌腱驱动连续体机械臂轨迹跟踪控制器。它由滑模控制律和两组自适应模糊子控制器组成。第一个用来估计和补偿沿肌腱的摩擦力。第二种方法采用小波控制的开关项来减轻系统的抖振现象，增强系统的鲁棒性。为了防止肌腱松弛，采用对抗策略和AFSMC控制器来分配驱动力。通过仿真和实验研究验证了所提控制器的有效性。在自由空间实验中，AFSMC控制器产生的均方根误差（RMSE）为0.42%，而SMC控制器产生的均方根误差为0.90%。在50g负载的情况下，所提出的控制器将平均RMSE降低到1.47%，而SMC控制器的RMSE为4.29%。实验结果表明，所提出的AFSMC控制器具有较高的控制精度、鲁棒性和较低的抖振。

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引用次数: 0