Mechatronics最新文献

英文中文

Learning-based modeling of a magnetically steerable soft suction device for endoscopic endonasal interventions 内窥镜内镜治疗用磁可控软吸装置的学习建模

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-02-04 DOI: 10.1016/j.mechatronics.2026.103468

Majid Roshanfar , Alex Zhang , Changyan He , Amir Hooshiar , Dale J. Podolsky , Thomas Looi , Eric Diller

This paper introduces a learning-based modeling framework for a magnetically steerable soft suction device designed for endoscopic endonasal brain tumor resection. The device is miniaturized (4 mm outer diameter, 2 mm inner diameter, 40 mm length), 3D printed using biocompatible SIL 30 material, and integrates embedded Fiber Bragg Grating (FBG) sensors for real-time shape feedback. Shape reconstruction is represented using four Bezier control points, allowing for a compact and smooth representation of the device’s deformation. A data-driven model was trained on 5097 experimental samples to learn the mapping from magnetic field parameters (magnitude: 0–14 mT, frequency: 0.2–1.0 Hz, and vertical tip distances from the surface of the electromagnet coil table: 90–100 mm) to the resulting geometric configuration of the soft robot, represented by four Bezier control points that define its 3D shape. The model was implemented and compared using both Neural Network (NN) and Random Forest (RF) architectures. The RF model outperformed the NN across all metrics, achieving a mean root mean square error of 0.087 mm in control point prediction and a mean shape reconstruction error of 0.064 mm. Feature importance analysis further revealed that magnetic field components predominantly influence distal control points, while frequency and distance affect the base configuration. Unlike prior studies that apply general machine learning methods to soft robotic data, the proposed framework introduces a new modeling paradigm that links magnetic actuation inputs directly to geometric Bezier control points, creating an interpretable and low-dimensional representation of deformation. This conceptual integration of magnetic field characterization, embedded FBG sensing, and Bezier-based learning provides a unified modeling strategy that can be extended to other magnetically actuated continuum robots. This learning-based approach effectively models the complex nonlinear behavior of hyperelastic soft robots under magnetic actuation without relying on simplified physical assumptions. By enabling sub-millimeter shape prediction accuracy and real-time inference, this work establishes an advancement toward the intelligent control of magnetically actuated soft robotic tools in minimally invasive neurosurgery.

介绍了一种基于学习的内镜下鼻内脑肿瘤切除术磁可控软吸装置建模框架。该设备是小型化的（外径4毫米，内径2毫米，长度40毫米），使用生物相容性SIL 30材料进行3D打印，并集成了嵌入式光纤布拉格光栅（FBG）传感器，用于实时形状反馈。形状重建使用四个贝塞尔控制点表示，允许设备变形的紧凑和平滑表示。在5097个实验样本上训练数据驱动模型，学习从磁场参数（量级：0-14 mT，频率：0.2-1.0 Hz，垂直尖端与电磁线圈表表面的距离：90-100 mm）到最终软机器人几何结构的映射，由四个定义其三维形状的贝塞尔控制点表示。采用神经网络（NN）和随机森林（RF）两种结构对模型进行了实现和比较。RF模型在所有指标上都优于神经网络，在控制点预测中平均均方根误差为0.087 mm，平均形状重建误差为0.064 mm。特征重要性分析进一步表明，磁场分量主要影响远端控制点，而频率和距离影响基座配置。与之前将一般机器学习方法应用于软机器人数据的研究不同，所提出的框架引入了一种新的建模范式，将磁致动输入直接链接到几何贝塞尔控制点，从而创建了可解释的低维变形表示。磁场表征、嵌入式FBG传感和基于bezier的学习的概念集成提供了一个统一的建模策略，可以扩展到其他磁驱动连续体机器人。这种基于学习的方法有效地模拟了磁驱动下超弹性软机器人复杂的非线性行为，而不依赖于简化的物理假设。通过实现亚毫米形状预测精度和实时推断，这项工作为微创神经外科中磁驱动软机器人工具的智能控制奠定了基础。

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引用次数: 0

Resilient multi-UAV formation control with leader replacement under agent failures 智能体失效下具有leader替换的多无人机编队弹性控制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-24 DOI: 10.1016/j.mechatronics.2026.103465

Takuya Murakami , Toru Namerikawa

This paper develops and validates a resilient formation control for multi-UAV systems under agent failures. Building on our previous work, we extend resilient formation control that tolerates non-compensable faults and enables leader replacement to second-order UAV dynamics, and provide a theoretical stability analysis of the proposed control law. To address leader failures, we integrate a coordinated leader replacement algorithm with the resilient controller. The proposed scheme is validated through numerical simulations and multi-UAV flight experiments on small quadrotors. The results demonstrate that the system preserves formation even under severe leader failures, while the non-faulty agents asymptotically maintain consensus. These theoretical, simulation, and experimental results confirm the effectiveness of the proposed approach and highlight its practical applicability for resilient multi-agent systems.

本文开发并验证了agent失效情况下多无人机系统的弹性编队控制方法。在我们之前工作的基础上，我们将弹性编队控制扩展到二阶无人机动力学中，该控制可以容忍不可补偿的故障并使领导者替换，并提供了所提出的控制律的理论稳定性分析。为了解决领导者故障，我们将一种协调的领导者替换算法与弹性控制器集成在一起。通过数值仿真和小型四旋翼飞行器多无人机飞行实验验证了该方案的有效性。结果表明，即使在严重的领导失效情况下，系统也能保持队形，而无故障的代理则渐近地保持一致。这些理论、仿真和实验结果证实了所提出方法的有效性，并突出了其在弹性多智能体系统中的实际适用性。

引用次数: 0

Development of a novel dual-motor driven wheel–foot transformation mechanism for wheel-biped robots 一种新型双电机驱动轮式双足机器人轮足转换机构的研制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-23 DOI: 10.1016/j.mechatronics.2026.103466

Jindai Zhang , Kangcheng Zhang , Jianlin Zhang , Xuechao Chen , Zhangguo Yu , Qiang Huang

Balancing mobility and adaptability in complex environments remains a major challenge for wheeled and biped robots, highlighting multi-modal actuation as a key research focus. This study proposes, designs, and experimentally validates a dual-motor wheel–foot transformation mechanism for wheel-biped robots, enabling both wheeled and legged locomotion. Guided by a “flip-deploy” design concept, the mechanism integrates a screw-nut drive, linkage system, and sliding block-slot mechanism in a coaxial dual-motor layout, achieving reliable bi-directional switching between wheeled and footed modes. The transmission relationships during the mechanism’s mode-switching process are analyzed, and the linkage lengths are optimized based on static force analysis in the footed mode. No-load, model validation and on-robot experiments demonstrate that the mechanism satisfies the design requirements, enabling stable, rapid, and robust posture transitions while exerting negligible influence on the robot’s overall posture stability. Overall, the proposed design makes it possible for wheel-biped robots to achieve multimodal locomotion and adapt to diverse terrains.

在复杂环境中平衡机动性和适应性是轮式和双足机器人面临的主要挑战，多模态驱动是一个重要的研究热点。本研究提出、设计并实验验证了一种用于轮式双足机器人的双电机轮足转换机构，实现轮式和腿式运动。该机构以“翻转部署”设计理念为指导，在同轴双电机布局中集成了螺杆螺母驱动、联动系统和滑块槽机构，实现了轮式和脚踏模式之间可靠的双向切换。分析了机构在模式切换过程中的传动关系，并基于足部静力分析优化了机构的连杆长度。空载、模型验证和机器人上实验表明，该机构满足设计要求，能够实现稳定、快速和稳健的姿态转换，同时对机器人整体姿态稳定性的影响可以忽略不计。总体而言，所提出的设计使轮式双足机器人实现多模式运动和适应不同的地形成为可能。

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引用次数: 0

Trajectory tracking through Multiple Weight Control with slackness avoidance for modular parallel tendon-driven joints 基于多重重量控制的模块化平行肌腱驱动关节轨迹跟踪

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-16 DOI: 10.1016/j.mechatronics.2026.103463

Josef Neto , Yiwei Wang , Shunta Togo , Hiroshi Yokoi , Yinlai Jiang

This study proposes a novel Multiple Weighted Control (MWC) framework that integrates a Neuro-Adaptive Sliding Mode Controller (NASMC) with a double-reaching switching power law and a Neuro-Adaptive Backstepping (NAB) controller incorporating a robust differentiator for a tendon-driven mechanism (TDM). To mitigate tendon slackness, a trajectory modification algorithm is developed to maintain appropriate tension throughout motion. The proposed framework addresses key challenges in real-time control implementation and offers practical solutions verified through both simulations and hardware experiments on a single-degree-of-freedom TDM. Comparative analyses demonstrate that the proposed controller achieves superior tracking accuracy relative to other control methods. Moreover, the slackness prevention algorithm effectively avoids motion obstruction due to excessive tension. System performance, evaluated using metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE), confirms high precision in both position tracking and tension estimation.

本研究提出了一种新的多重加权控制（MWC）框架，该框架集成了具有双到达开关幂律的神经自适应滑模控制器（NASMC）和具有用于肌腱驱动机制（TDM）的鲁棒微分器的神经自适应反演（NAB）控制器。为了减轻肌腱松弛，开发了一种轨迹修改算法，以在整个运动中保持适当的张力。提出的框架解决了实时控制实现中的关键挑战，并提供了通过单自由度TDM仿真和硬件实验验证的实用解决方案。对比分析表明，与其他控制方法相比，该控制器具有更好的跟踪精度。此外，松弛预防算法有效地避免了由于张力过大而造成的运动障碍。使用均方误差（MSE）和平均绝对误差（MAE）等指标评估系统性能，确认了位置跟踪和张力估计的高精度。

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引用次数: 0

Lyapunov-based model predictive control for path-following of an autonomous underwater vehicle using line-of-sight guidance 基于lyapunov的自主水下航行器路径跟踪模型预测控制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-15 DOI: 10.1016/j.mechatronics.2026.103464

Guanghao Yang , Le Li , Kang Zhang , Weidong Liu

This paper studies the path-following problem of an underactuated autonomous underwater vehicle (AUV) with the ocean current disturbances. A line-of-sight (LOS) guidance law is employed at the outer loop, while a Lyapunov-based model predictive controller (LMPC) is developed at the inner loop to ensure that the AUV can accomplish the path-following task under ocean current disturbances. The proposed LOS-LMPC inherits the stability and robustness of the extended state observer (ESO)-based auxiliary control law and utilizes online optimization to enhance the path-following performance of the AUV system. Simulations and hardware experiments conducted on the “Qilin” AUV demonstrate the effectiveness of the proposed method.

研究了受洋流干扰的欠驱动自主水下航行器的路径跟踪问题。外环采用视距制导律，内环采用基于lyapunov的模型预测控制器（LMPC），保证了水下机器人在洋流干扰下能够完成路径跟踪任务。所提出的LOS-LMPC继承了基于扩展状态观测器（ESO）的辅助控制律的稳定性和鲁棒性，并利用在线优化来提高AUV系统的路径跟踪性能。在“麒麟”号水下航行器上进行的仿真和硬件实验验证了该方法的有效性。

引用次数: 0

Dynamic modeling and control of an inextensible pneumatically actuated soft continuum manipulator 不可扩展气动驱动软连续体机械臂的动力学建模与控制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-14 DOI: 10.1016/j.mechatronics.2026.103462

Samuel Pilch , Eva Menrad , Artem Beger , Oliver Sawodny

Modeling and control of soft continuum manipulators remain challenging due to their infinite degrees of freedom, nonlinear material properties, and demanding sensing requirements. This work presents a dynamic model-based centralized control approach for a spatially moving, inextensible soft continuum manipulator actuated by pneumatic network segments. The equations of motion are formulated using the Euler–Lagrange formalism, with segment stiffness represented by an experimentally identified spring moment model incorporating curvature, orientation, and pressure dependencies. Nonlinear system dynamics are linearized around the desired generalized coordinates, enabling a feedforward controller based on the linearized state representation combined with a PID feedback loop. State feedback is reconstructed from IMU measurements using spherical coordinates expressed in azimuth and zenith angles. The desired pressures are obtained through linear mapping from the target moments and further adapted by introducing a mean pressure, allowing simultaneous pressurization and depressurization of adjacent pneumatic networks for faster actuation. These adapted pressures are realized by an external, model-free pressure controller. The proposed method is experimentally validated, demonstrating accurate control of the continuum manipulator.

由于软连续体机械臂具有无限自由度、非线性材料特性和苛刻的传感要求，其建模和控制仍然具有挑战性。本文提出了一种基于动态模型的集中控制方法，用于由气动网络段驱动的空间运动、不可扩展的软连续机械臂。运动方程采用欧拉-拉格朗日公式，部分刚度由实验确定的弹簧力矩模型表示，包括曲率、方向和压力依赖关系。非线性系统动力学在期望的广义坐标周围线性化，使基于线性化状态表示的前馈控制器与PID反馈环相结合。用方位角和天顶角表示的球坐标重建IMU测量的状态反馈。通过目标力矩的线性映射获得所需压力，并通过引入平均压力进一步调整，允许相邻气动网络同时增压和减压，以实现更快的驱动。这些自适应压力由外部无模型压力控制器实现。实验验证了该方法的有效性，证明了连续统机械臂的精确控制。

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引用次数: 0

The ForceCAST framework: Methodology and tools for benchmarking force control algorithms ForceCAST框架：力控制算法基准的方法和工具

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-12 DOI: 10.1016/j.mechatronics.2026.103460

Eldison Dimo, Matteo Meneghetti, Noè Murr, Andrea Calanca

This paper describes the outcomes of the Forecast project, aiming at providing tools and metrics to benchmark force control algorithms for robotics applications. The Forecast project recognizes the importance of considering the interacting environment in order to assess the performance of a force-controlled system. In many papers, force-controlled systems are often evaluated on too specific (and often favorable) environmental conditions, preventing readers from fairly understanding the overall system behavior. Starting from these observations, the Forecast project has developed tools and metrics to ease and standardize the benchmarking process. The objective of this paper is to present such tools and metrics and to foster their diffusion within the robotics community. A case study is proposed to practically showcase the benchmarking process.

本文描述了预测项目的结果，旨在为机器人应用程序提供基准力控制算法的工具和指标。Forecast项目认识到为了评估力控系统的性能而考虑交互环境的重要性。在许多论文中，力控系统通常在过于具体（通常是有利的）环境条件下进行评估，从而使读者无法公平地理解系统的整体行为。从这些观察开始，Forecast项目开发了工具和度量来简化和标准化基准测试过程。本文的目的是介绍这些工具和指标，并促进它们在机器人社区中的传播。提出了一个案例研究，以实际展示对标过程。

引用次数: 0

Hydraulic actuated leg with passive flexibility and energy efficiency for heavy-duty quadruped robots 重型四足机器人具有被动柔性和能效的液压驱动腿

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-10 DOI: 10.1016/j.mechatronics.2026.103461

Zisen Hua , Dongyue Hua , Xuewen Rong , Yaru Sun

To address the excessive foot-ground impact caused by inherent limitations of hydraulic systems in quadruped robots, while mitigating the over-dependence of traditional passive buffer structures on the touchdown posture of robot legs, this study proposes a novel limb structure design method based on joint passive compliance. The approach involves introducing a transitional link at the knee joint, thereby evolving the traditional three-segment leg topology with three-degree-of-freedom into a four-segment structure with three-degree-of-freedom. Additionally, a fully symmetric four-piston-rod pneumatic support mechanism charactered by tunable high stiffness and minimal deformation is incorporated to constrain the motion of the links at the knee joint, thereby ensuring determinacy in the kinematic relationships among the limb segments. Simultaneously, by optimizing link lengths and hinge point positions, the fixed deformation direction of the elastic element is achieved across almost the entire workspace of the foot-end postures, while joint driving forces during dynamic motion are further reduced. The effectiveness of the proposed leg design, in terms of energy efficiency, motion stability, and impact attenuation, is validated through experiments on a single-leg test platform.

为了解决四足机器人液压系统固有局限性导致的过大的足地冲击，同时减轻传统被动缓冲结构对机器人腿着地姿态的过度依赖，本研究提出了一种基于关节被动顺应性的肢体结构设计方法。该方法在膝关节处引入过渡连杆，从而将传统的三自由度三节段腿拓扑结构演变为三自由度四节段结构。此外，一个全对称的四活塞杆气动支撑机构，其特点是可调的高刚度和最小的变形，以约束膝关节连杆的运动，从而确保肢体段之间运动关系的确定性。同时，通过优化连杆长度和铰点位置，实现了弹性元件在几乎整个足端姿态工作空间的固定变形方向，同时进一步减小了关节动态运动时的驱动力。通过在单腿测试平台上的实验，验证了所提出的腿设计在能效、运动稳定性和冲击衰减方面的有效性。

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引用次数: 0

On hybrid inverse dynamic modeling for industrial robots 工业机器人混合逆动力学建模研究

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-07 DOI: 10.1016/j.mechatronics.2025.103451

S. Clavel , M. Alamir , J. Faure-Favre

Inverse dynamic modeling plays a crucial role in developing optimal feedforward control laws in robotics. A promising approach to improve its accuracy involves using hybrid approaches combining physics-based models with data-driven models. This paper presents an in-depth study of such an approach, based on Gated Recurrent Unit (GRU) neural networks for application on 4-joints and 6-joints Stäubli industrial robots. We start with a comprehensive review of the recent literature. Subsequently, we demonstrate that our model significantly surpasses more traditional black-box models in terms of accuracy and extrapolation. Furthermore, we explore the various factors that influence its accuracy with real data.

在机器人系统中，逆动力学建模在建立最优前馈控制律方面起着至关重要的作用。提高其准确性的一种有希望的方法是使用混合方法，将基于物理的模型与数据驱动的模型相结合。本文对基于门控循环单元（GRU）神经网络的四关节和六关节Stäubli工业机器人应用方法进行了深入研究。我们首先全面回顾一下最近的文献。随后，我们证明了我们的模型在精度和外推方面明显优于传统的黑盒模型。并结合实际数据探讨了影响其精度的各种因素。

引用次数: 0

Tiny learning-based MPC for multirotors: Solver-aware learning for efficient embedded predictive control 基于微型学习的多旋翼MPC：求解器感知学习的高效嵌入式预测控制

IF 3.1 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Mechatronics

Pub Date : 2026-01-05 DOI: 10.1016/j.mechatronics.2025.103452

Babak Akbari, Justin Frank, Melissa Greeff

Tiny aerial robots hold great promise for applications such as environmental monitoring and search-and-rescue, yet face significant control challenges due to limited onboard computing power and nonlinear dynamics. Model Predictive Control (MPC) enables agile trajectory tracking and constraint handling but depends on an accurate dynamics model. While existing Learning-Based (LB) MPC methods, such as Gaussian Process (GP) MPC, enhance performance by learning residual dynamics, their high computational cost restricts onboard deployment on tiny robots. This paper introduces Tiny LB MPC, a co-designed MPC framework and optimization solver for resource-constrained micro multirotor platforms. The proposed approach achieves 100 Hz control on a Crazyflie 2.1 equipped with a Teensy 4.0 microcontroller, demonstrating a 43% average improvement in tracking performance over existing embedded MPC methods under model uncertainty, and achieving the first onboard implementation of LB MPC on a 53 g multirotor.

微型空中机器人在环境监测和搜救等应用方面前景广阔，但由于机载计算能力有限和非线性动力学，它们面临着重大的控制挑战。模型预测控制（MPC）能够实现敏捷的轨迹跟踪和约束处理，但依赖于精确的动力学模型。虽然现有的基于学习（LB）的MPC方法，如高斯过程（GP） MPC，通过学习剩余动力学来提高性能，但其高昂的计算成本限制了微型机器人的机载部署。本文介绍了资源受限微型多转子平台协同设计的MPC框架和优化求解器Tiny LB MPC。所提出的方法在配备了tetey 4.0微控制器的crazyfly 2.1上实现了100 Hz的控制，在模型不确定性下，与现有的嵌入式MPC方法相比，跟踪性能平均提高了43%，并首次在53克多转子上实现了LB MPC。

引用次数: 0

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