Mechatronics最新文献

{"title":"Testing human-hand segmentation on in-distribution and out-of-distribution data in human–robot interactions using a deep ensemble model","authors":"Reza Jalayer ,&nbsp;Yuxin Chen ,&nbsp;Masoud Jalayer ,&nbsp;Carlotta Orsenigo ,&nbsp;Masayoshi Tomizuka","doi":"10.1016/j.mechatronics.2025.103365","DOIUrl":"10.1016/j.mechatronics.2025.103365","url":null,"abstract":"<div><div>Reliable detection and segmentation of human hands are critical for enhancing safety and facilitating advanced interactions in human–robot collaboration. Current research predominantly evaluates hand segmentation under in-distribution (ID) data, which reflects the training data of deep learning (DL) models. However, this approach fails to address out-of-distribution (OOD) scenarios that often arise in real-world human–robot interactions. In this work, we make three key contributions: first we assess the generalization of deep learning (DL) models for hand segmentation under both ID and OOD scenarios, utilizing a newly collected industrial dataset that captures a wide range of real-world conditions including simple and cluttered backgrounds with industrial tools, varying numbers of hands (0 to 4), gloves, rare gestures, and motion blur. Our second contribution is considering both egocentric and static viewpoints. We evaluated the models trained on four datasets, i.e. EgoHands, Ego2Hands (egocentric mobile camera), HADR, and HAGS (static fixed viewpoint) by testing them with both egocentric (head-mounted) and static cameras, enabling robustness evaluation from multiple points of view. Our third contribution is introducing an uncertainty analysis pipeline based on the predictive entropy of predicted hand pixels. This procedure enables flagging unreliable segmentation outputs by applying thresholds established in the validation phase. This enables automatic identification and filtering of untrustworthy predictions, significantly improving segmentation reliability in OOD scenarios. For segmentation, we used a deep ensemble model composed of UNet and RefineNet as base learners. Our experiments demonstrate that models trained on industrial datasets (HADR, HAGS) outperform those trained on non-industrial datasets, both in segmentation accuracy and in their ability to flag unreliable outputs via uncertainty estimation. These findings underscore the necessity of domain-specific training data and show that our uncertainty analysis pipeline can provide a practical safety layer for real-world deployment.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103365"},"PeriodicalIF":3.1,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural efficient gaits from Nonholonomic Locomotion Nonlinear Normal Mode (NL-NNM): The Pendrivencar case","authors":"Mirado Rajaomarosata,&nbsp;Luc Jaulin,&nbsp;Lionel Lapierre,&nbsp;Simon Rohou","doi":"10.1016/j.mechatronics.2025.103366","DOIUrl":"10.1016/j.mechatronics.2025.103366","url":null,"abstract":"<div><div>Bio-inspired robots remain far less energy-efficient than animals because conventional controllers impose trajectories that fight passive dynamics, whereas animals exploit resonance through <em>natural nonlinear normal modes (NNM)</em>, whose periodic internal motions form a smooth 2D invariant surface; We ask how to define and compute the <em>natural motions of a conservative locomotion system</em>: propulsion arises only from <em>no-slip constraints</em>, and once initiated, a gait persists without actuation—like a frictionless pendulum. We tackle non-holonomic constraints on the <em>Pendrivencar</em>, a vehicle driven by a <em>motorised pendulum with a cubic torsional spring</em>; We introduce the <strong>Nonholonomic Locomotion - NNM (NL-NNM)</strong>: extract a <em>high-speed spectral seed</em> – where chassis oscillations vanish and the pendulum is neutrally stable – refine the periodic orbit, and continue the resulting <em>2D invariant manifold</em> via pseudo-arclength across <em>three slow centre manifolds</em> (stable for positive speed, neutral at zero, unstable for negative) from non-isolated rectilinear equilibria; We demonstrate the first NL-NNM for a moving non-holonomic robot: internal orbits produce a <em>pendulum–chassis choreography</em> whose <em>energy-dependent frequency shifts</em> and <em>harmonic richness</em> exceed linear predictions. Via <em>geometric phase</em>, each orbit yields undulatory straight-line motion. A <em>dual-loop control simulation</em> confirms autonomous path tracking with only the pendulum; Extending to dissipative regimes via <em>non-linear resonant modes</em> offers a path to high-efficiency locomotion in aquatic, aerial, legged, soft-bodied, and other robots.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103366"},"PeriodicalIF":3.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning an inverse thermodynamic model for Pneumatic Artificial Muscles control","authors":"G. Wang ,&nbsp;R. Chalard ,&nbsp;J.A. Cifuentes ,&nbsp;M.T. Pham","doi":"10.1016/j.mechatronics.2025.103359","DOIUrl":"10.1016/j.mechatronics.2025.103359","url":null,"abstract":"<div><div>Pneumatic Artificial Muscles (PAMs) are highly nonlinear actuators widely used in robotics, rehabilitation, and other dynamic applications. Their complex behavior poses significant challenges for traditional system identification methods. Although machine learning techniques have shown remarkable success in modeling nonlinear systems, their black-box nature often leads to interpretability issues and susceptibility to overfitting. This study proposes a novel hybrid modeling approach that combines the strengths of analytical models with neural networks to capture the inverse thermodynamic behavior of PAMs. The results demonstrate that the hybrid model outperformed both analytical and purely neural network models. The obtained models were further used for model-based control design and the results show that the application of hybrid model improved the tracking performance.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103359"},"PeriodicalIF":3.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotized Incremental Sheet Forming trajectory control using deep neural network for force/torque compensator and task-space error tracking controller","authors":"Xuan Dung To ,&nbsp;Jefferson Roman Blanco ,&nbsp;Sandra Zimmer-Chevret ,&nbsp;Ghinwa Ouaidat ,&nbsp;Thibaut Raharijaona ,&nbsp;Farid Noureddine ,&nbsp;Micky Rakotondrabe","doi":"10.1016/j.mechatronics.2025.103360","DOIUrl":"10.1016/j.mechatronics.2025.103360","url":null,"abstract":"<div><div>In Robotized Incremental Sheet Forming (ISF), achieving precise geometrical accuracy is a challenging task due to trajectory tool center point (TCP) position errors at the forming tool attached to the robot’s end-effector. These errors primarily arise from external disturbance forces and torques generated during the interaction between the forming tool and the elastic metal sheet. While joint-torque space controllers can mitigate reaction forces and torques through dynamic modeling, joint-space control has inherent limitations, particularly for industrial high-load robots like the ABB IRB 8700. To overcome these challenges, this work implements an external force/torque (F/T) compensator in task-space using a deep neural network. The network predicts trajectory errors induced by reaction forces and torques measured via a 6-axis F/T sensor. Additionally, the forming tool’s trajectory is precisely monitored using a laser tracker, which serves as a feedback mechanism in a closed-loop task-space error-tracking controller. This controller detects and corrects trajectory deviations in real time. By integrating the F/T compensator and the task-space error-tracking controller, the proposed approach effectively compensates for reaction forces and torques while addressing additional errors introduced by other process-related factors. This integration results in significantly enhanced accuracy in robotic incremental forming processes.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103360"},"PeriodicalIF":3.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive backstepping finite-time output feedback control for path tracking of autonomous vehicle with asymmetric dead-zone","authors":"Sucai Zhang ,&nbsp;Yongfu Wang ,&nbsp;Gang Li","doi":"10.1016/j.mechatronics.2025.103362","DOIUrl":"10.1016/j.mechatronics.2025.103362","url":null,"abstract":"<div><div>A finite time adaptive output feedback control scheme with state constraint is proposed for the path tracking control of autonomous vehicle considering the asymmetric dead-zone. Firstly, the vehicle dynamics model and path tracking model are established by combining the dead-zone model, and the adaptive law is designed to approximate the parameters of dead-zone model. On this basis, an adaptive backstepping controller with output-constrained feedback control is designed by combining the filtering error compensation mechanism and the finite time technique, introducing the barrier Lyapunov function and the backstepping control technique. In order to save communication resources, a dynamic threshold event triggering mechanism is introduced. Finally, a rigorous stability analysis based on Lyapunov stability theory is presented to ensure that all signals of the closed-loop system are bounded in finite time. The effectiveness of the proposed method is verified by different simulations, hardware-in-the-loop experiments and real-time vehicle experiments. The results show that the proposed method is effective under different working conditions. The results of real-time vehicle experiments show that the controller can effectively improve the accuracy of path tracking control and reduce the maximum lateral position error to 0.1752 m compared with other methods, and the scheme can provide a theoretical reference for the control practice of autonomous vehicle.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103362"},"PeriodicalIF":3.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics modeling and μ synthesis for a parallel - suspension type inertially stabilized platform","authors":"Yin Sun,&nbsp;Feng Zhao,&nbsp;Zhenjing Guo,&nbsp;Xiaojun Yan","doi":"10.1016/j.mechatronics.2025.103363","DOIUrl":"10.1016/j.mechatronics.2025.103363","url":null,"abstract":"<div><div>The parallel-suspension type inertially stabilized platform utilizes a unique flexible support structure and non-contact linear actuators to enable simultaneous high-efficiency vibration suppression control of optical payloads across multiple degrees of freedom. Compared to traditional series – gimbals type stabilized platforms, it offers a higher payload-to-weight ratio and rapid response characteristics. In this paper, a 6-degree-of-freedom dynamic model for the parallel-suspension inertially stabilized platform is established, a control method is designed, and an actual engineering prototype is constructed. Specifically, a flexible support element model that accounts for column instability phenomenon is developed. Based on the parallel mount configuration a complete 6-degree-of-freedom dynamic model of the entire platform is constructed. Furthermore, due the variable parameter characteristics of flexible elastic elements, a <em>μ</em> synthesis control method considering the uncertainty of model parameters is designed. The experimental results show that the <em>μ</em> controller can effectively reduce the external sinusoidal angular disturbance to less than 25 % and the linear vibration disturbance to less than 3 % of the original disturbance while maintaining the robustness. Both simulation and experimental results verify the correctness and effectiveness of the proposed model and method.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103363"},"PeriodicalIF":3.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust feedback-linearization MPC with artificial target for UGVs","authors":"Bruno S.S. Pereira ,&nbsp;Tito L.M. Santos ,&nbsp;Andre G.S. Conceicao","doi":"10.1016/j.mechatronics.2025.103358","DOIUrl":"10.1016/j.mechatronics.2025.103358","url":null,"abstract":"<div><div>This paper proposes a new robust feedback-linearization MPC for a class of Unmanned Ground Vehicles. A robust MPC for trajectory tracking with an artificial target is combined with a suitable constraint mapping to ensure robust constraint satisfaction and recursive feasibility despite the effect of bounded disturbances. The artificial reference provides a potentially enlarged domain of attraction, and an analytical target modification is used to achieve the convergence of the tracking error to a minimal robust positively invariant set. The feedback-linearization trade-off concerning the transformed constraints is also analyzed. A case study demonstrating the control strategy’s performance is presented using the Clearpath Husky A200 UGV and the OptiTrack motion capture system.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103358"},"PeriodicalIF":3.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comfort-enhanced longitudinal control for DDEVs: A robust brake coordination approach leveraging reactive anti-dive forces","authors":"Yanjun Ren ,&nbsp;Tong Shen ,&nbsp;Mingzhuo Zhao ,&nbsp;Fanxun Wang ,&nbsp;Liwei Xu ,&nbsp;Guodong Yin","doi":"10.1016/j.mechatronics.2025.103357","DOIUrl":"10.1016/j.mechatronics.2025.103357","url":null,"abstract":"<div><div>Distributed drive electric vehicles actuated by in-wheel motors and brake-by-wire systems enable tracking target motion while improving extra vehicle performance. Outboard brake torque allocated on front and rear wheels generates diverse vertically reactive anti-dive forces, providing an innovative approach to mitigate brake dive without requiring active suspensions. However, the differing dynamics of regenerative and hydraulic braking, along with multiple uncertain vehicle parameters, pose significant challenges to achieving robustness under mixed uncertainties. Moreover, pitch-induced bias in onboard acceleration measurements further degrades control accuracy. To address above problems, this paper proposes a robust, comfort-enhanced longitudinal control system with coordinated braking. A three-degree-of-freedom vehicle dynamics model is developed to incorporate the effect of anti-dive forces. For accurate feedback, a robust <span><math><mrow><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>/</mo><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></mrow></math></span> observer is designed to compensate pitch-variation-related acceleration measurement biases. By integrating dynamic and parametric uncertainties into the control-oriented model, the mixed <span><math><mi>μ</mi></math></span>-synthesis is employed to design a two-degree-of-freedom controller to robustly optimize the acceleration tracking and anti-dive performance. Compared to the controller designed by standard <span><math><mi>μ</mi></math></span>-synthesis, the proposed approach achieves a 10% improvement in robust performance. Real-vehicle experiments validate the system’s effectiveness, demonstrating over a 27% reduction in pitch angle while maintaining satisfactory acceleration responses under blended braking conditions.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103357"},"PeriodicalIF":3.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for automation of 3-DoF control of magnetically-levitated microrobots","authors":"Joseph Nofech,&nbsp;Mir Behrad Khamesee","doi":"10.1016/j.mechatronics.2025.103356","DOIUrl":"10.1016/j.mechatronics.2025.103356","url":null,"abstract":"<div><div>This study presents a novel methodology for achieving three-degree-of-freedom (3-DoF) control for an attractive-type magnetically-levitated (maglev) microrobot using machine learning. Contact micromanipulation methods face challenges associated with friction, backlash, and maintenance requirements; particularly in delicate applications such as cell injection. The frictionless and low-maintenance nature of attractive-type maglev makes it a viable alternative to traditional methods, but achieving precise 3-DoF control for such systems is not straightforward due to the complexity of their magnetic fields. This research addresses this problem by introducing a machine learning-based methodology that automates the learning of levitation dynamics across the workspace, effectively bypassing a major challenge associated with cross-disciplinary applications of attractive-type maglev.</div><div>Our presented approach introduces an automated system for generating training data with minimal human intervention, allowing a machine learning model to quantify the levitated microrobot’s physical response to system inputs while accounting for position-dependent variations in levitation dynamics across the workspace. This model is then used to establish 3-DoF position control of the levitated microrobot. In addition to simplifying the setup process for new and newly-modified attractive-type levitation platforms, this new data-driven methodology is demonstrated to improve performance over conventional methods; achieving up to a 20% reduction in root mean square error during trajectory tracking and up to a 36% reduction in step response settling times.</div><div>The results demonstrate the ability of our automated methodology to significantly reduce the accessibility barriers associated with establishing and modifying attractive-type maglev platforms; effectively replacing the usual methods of finite element simulation, precise magnetic field measurements, and/or analytical calculations while providing enhanced levitation control over traditional methods. This advancement contributes to the field of micromanipulation and microforce sensing by offering a more accessible and efficient approach to achieving precise control in attractive-type maglev systems.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103356"},"PeriodicalIF":3.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and prediction of nonlinear cable slab dynamics using Koopman operators","authors":"Michael Pumphrey ,&nbsp;Almuatazbellah M. Boker ,&nbsp;Mohammad Al Saaideh ,&nbsp;Natheer Alatawneh ,&nbsp;Yazan M. Al-Rawashdeh ,&nbsp;Khaled Aljanaideh ,&nbsp;Mohammad Al Janaideh","doi":"10.1016/j.mechatronics.2025.103353","DOIUrl":"10.1016/j.mechatronics.2025.103353","url":null,"abstract":"<div><div>A novel approach for modeling the nonlinear dynamics of cable slabs using Koopman operator theory is presented. Cable slab dynamics are a critical challenge in precision motion systems, as the cables can induce undesired vibrations and disturbances on motion stages. To address this, a higher-dimensional state-space model with nonlinear observable functions is developed to approximate the cable slab dynamics. The proposed model achieves a prediction error within <span><math><mrow><mo>∼</mo><mn>1</mn></mrow></math></span>% over the specified motion range and demonstrates robustness in predicting untrained, randomized, acyclic cable slab motions. A systematic evaluation of various observable functions was conducted to minimize the modeling errors, leading to an optimized model with fractional-order exponents. When compared with a neural network-based state-space model (NN-SS), the Koopman approach demonstrated faster training and better performance. For force prediction, the Koopman approach achieved a reduction of three-quarters in maximum error when compared with the NN-SS method. This work offers a concise and experimentally validated analytical framework specifically for developing accurate predictive models of nonlinear cable slab dynamics.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103353"},"PeriodicalIF":3.1,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}