Robotics and Autonomous Systems最新文献

{"title":"Vision-guided gripping process with minimizing folding for flexible fabric materials by integrating a sequential optimization algorithm and FEM analysis","authors":"Minh Khang Ngo ,&nbsp;Chanhee Won ,&nbsp;HyunKyo Lim ,&nbsp;Dae Young Lim ,&nbsp;Than Trong Khanh Dat ,&nbsp;Jonghun Yoon","doi":"10.1016/j.robot.2026.105349","DOIUrl":"10.1016/j.robot.2026.105349","url":null,"abstract":"<div><div>Automatic fabric handling in garment manufacturing presents significant challenges due to the soft, deformable, and highly variable nature of textile materials. This paper proposes an integrated robotic fabric gripping system capable of reliably identifying and manipulating fabric parts with minimal folding. The system comprises an industrial robotic arm, four needle grippers mounted on an adjustable jig mechanism, and a high-precision 3D vision camera for real-time fabric detection. A key contribution of this work is a sequential optimization model that determines four optimal gripping points for each fabric pattern, based on material characterization, deformation criteria, and folding definitions derived from experiments and finite element simulations. These points are mapped relative to CAD data and stored for retrieval. A vision-based matching algorithm then aligns real-time image inputs with CAD templates to localize the fabric piece and recover the precomputed optimal gripping points, which are transmitted to the robot for autonomous execution. Quantitative evaluations demonstrate that the proposed approach significantly reduces fabric folding and enhances the reliability of robotic garment handling, representing a substantial step toward fully automated garment production.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105349"},"PeriodicalIF":5.2,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Path planning of bionic robotic fish based on enhanced SAC algorithm combined with HER and CQL","authors":"Ming Wang,&nbsp;Tinglong Zhao,&nbsp;Lingchen Zuo,&nbsp;Yanling Gong,&nbsp;Guangxin Lv,&nbsp;Ruilong Wang","doi":"10.1016/j.robot.2026.105336","DOIUrl":"10.1016/j.robot.2026.105336","url":null,"abstract":"<div><div>Underwater robots play a crucial role in ocean exploration and resource development. As a type of underwater robot, bionic robotic fish can mimic the swimming patterns of real fish, offering unique advantages in complex underwater environments. However, traditional path planning methods perform poorly in unknown or dynamic environments. This paper proposes a path planning algorithm based on deep reinforcement learning, which improves the path planning capabilities of bionic robotic fish in unknown environments by combining the Soft Actor-Critic (SAC) algorithm with the Hindsight Experience Replay (HER) mechanism. Furthermore, the use of the Conservative Q-Learning (CQL) algorithm enhances the algorithm’s exploration capability and robustness. The effectiveness and practicality of the proposed algorithm are validated through simulation results.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105336"},"PeriodicalIF":5.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 self-supervised exploratory guided push and grasp approach to enhance robotic manipulation in clutter","authors":"Chiat Pin Tay ,&nbsp;Shijun Yan ,&nbsp;Chong Chen ,&nbsp;Wei Qi Toh ,&nbsp;Miaolong Yuan ,&nbsp;Dongkyu Choi","doi":"10.1016/j.robot.2026.105339","DOIUrl":"10.1016/j.robot.2026.105339","url":null,"abstract":"<div><div>Robot manipulation of cluttered and stacked objects remains a crucial yet challenging task with immense real-world applications. Integrating push actions into grasp strategies has been proposed to address this challenge. However, existing deep learning approaches, particularly those based on reinforcement learning, often suffer from lengthy training times and inefficient action selection. This paper introduces a novel exploratory guided push approach that leverages self-supervised learning and a memory buffer enhancement strategy to accelerate efficient data sampling for model training. We investigated and analysed various network architectures, data collection methods, and learning strategies to understand their impacts on push-and-grasp tasks. Extensive experiments conducted in both simulated and real environments demonstrate the effectiveness of our proposed solution.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105339"},"PeriodicalIF":5.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Enhancing sampling-based planning with a library of paths","authors":"Michal Minařík,&nbsp;Vojtěch Vonásek,&nbsp;Robert Pěnička","doi":"10.1016/j.robot.2026.105334","DOIUrl":"10.1016/j.robot.2026.105334","url":null,"abstract":"<div><div>Path planning for 3D solid objects is a challenging problem, requiring a search in a six-dimensional configuration space, which is, nevertheless, essential in many robotic applications such as bin-picking and assembly. The commonly used sampling-based planners, such as Rapidly-exploring Random Trees, struggle with narrow passages where the sampling probability is low, increasing the time needed to find a solution. In scenarios like robotic bin-picking, various objects must be transported through the same environment. However, traditional planners start from scratch each time, losing valuable information gained during the planning process. We address this by using a library of past solutions, allowing the reuse of previous experiences even when planning for a new, previously unseen object. Paths for a set of objects are stored, and when planning for a new object, we find the most similar one in the library and use its paths as approximate solutions, adjusting for possible mutual transformations. The configuration space is then sampled along the approximate paths. Our method is tested in various narrow passage scenarios and compared with state-of-the-art methods from the OMPL library. Results show significant speed improvements (up to <span><math><mrow><mn>85</mn><mtext>%</mtext></mrow></math></span> decrease in the required time) of our method, often finding a solution in cases where the other planners fail. Our implementation of the proposed method is released as an open-source package.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105334"},"PeriodicalIF":5.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Reinforcement learning in robotic systems : A review on sim-to-real transfer","authors":"Rajesh Tiwari,&nbsp;Shailesh Khapre,&nbsp;Avantika Singh","doi":"10.1016/j.robot.2025.105327","DOIUrl":"10.1016/j.robot.2025.105327","url":null,"abstract":"<div><div>Sim-to-real transfer reinforcement learning has become a pivotal approach for narrowing the gap between simulation environments and real-world robotic applications. While reinforcement learning methods achieve remarkable success in simulation, their direct deployment in real environments remains challenging due to the inherent reality gap caused by mismatches in physical dynamics, sensory inputs, and environmental variability. This paper presents a comprehensive review of recent advances in sim-to-real transfer, emphasizing improved simulation fidelity, actuator-level modeling, and domain randomization encompassing both environmental and robotic parameters. A unified framework is proposed that outlines key processes, including simulation model optimization, strategy transfer, and iterative policy refinement across virtual and real domains. The study also discusses emerging developments such as Progressive Neural Networks for policy migration and modern benchmarking platforms. The review concludes with open challenges and future directions, including automated simulator tuning, adaptive domain adaptation, and establishing theoretical guarantees for robust and generalizable transfer learning.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105327"},"PeriodicalIF":5.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Automatic measurement process for hand–eye calibration based on Archimedean solids pose distribution","authors":"Kaifan Zhong,&nbsp;Nianfeng Wang,&nbsp;Xianmin Zhang","doi":"10.1016/j.robot.2026.105333","DOIUrl":"10.1016/j.robot.2026.105333","url":null,"abstract":"<div><div>Hand–eye calibration is a vital process for determining an unknown transformation between sensors and a robot end frame before applying robot vision. In addition to optimizing the mathematical solution, refining the pose distribution involved in the calibration can improve the calibration accuracy and efficiency. To optimize the pose distribution, the 3-D position distribution of the tool centre point is designed first, and then the final poses are determined considering the current application scenario. In this paper, an automatic pose generation method is proposed to stably output suitable poses in on-site calibration scenes when an arbitrary 3-D position distribution of the tool centre point is input. Based on this, different pose distributions are discussed regarding their effect on the calibration error, and an indicator is presented to evaluate the performance of these distributions before executing a calibration process. Moreover, a special pose distribution formed by an Archimedean solid is presented, and it shows better performance in improving the hand–eye calibration accuracy and efficiency. Both simulation and on-site experiments are carried out to verify the proposed methods and analyse the effect of different distributions on the calibration results.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105333"},"PeriodicalIF":5.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"System identification and model reference adaptive control of bipedal locomotion with neural networks","authors":"Burak Çatalbaş ,&nbsp;Bahadır Çatalbaş ,&nbsp;Ömer Morgül","doi":"10.1016/j.robot.2026.105331","DOIUrl":"10.1016/j.robot.2026.105331","url":null,"abstract":"<div><div>Biped robots have enormous potential to transform our lives with the usability of the infrastructure already prepared for humans, thanks to our morphological similarities. Unfortunately, the realization of this potential requires solutions to inherited difficulties of bipedal locomotion dynamics. Controlling these systems poses challenges due to their high degree of freedom, small support polygon, hybrid dynamics, etc. Nervous system ensures the locomotion of biological counterparts of biped robots. Similarly, artificial neural networks show competence to control bipedal locomotion. Unfortunately, complexity of neural network-based controllers (NNBCs) makes it difficult to adapt them to changes in robot model dynamics. In this paper, we propose a model reference adaptive control algorithm for bipedal locomotion, together with neural networks consisting of recurrent and feedforward layers in the controller and system identification tasks to overcome this drawback. Controller weights are updated via error gradient calculated through system identification neural network to force the controlled system output to desired behavior in an iterative manner. Moreover, we examine the effectiveness of our algorithm in decreasing the speed of steady-state error of neural controllers under different simulated scenarios. It is shown that recurrent and feedforward layers are beneficial for walking control and system identification with neural networks and implementable for real-time applications on various Jetson single-board computers. Results suggest that our method is capable of adapting neural controllers without requiring training from scratch. Under different scenarios, up to 33.6%, 34%, 31.8% in the target speed tracking error decrease is acquired with our algorithm on training, validation, and test sets.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105331"},"PeriodicalIF":5.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Human–robot collaborative control method based on command-weighted fusion strategy for manned legged robot","authors":"Yaojin Fan ,&nbsp;Bo You ,&nbsp;Jiayu Li ,&nbsp;Yufei Liu ,&nbsp;Chen Chen ,&nbsp;Xiaolei Chen ,&nbsp;Liang Ding","doi":"10.1016/j.robot.2025.105323","DOIUrl":"10.1016/j.robot.2025.105323","url":null,"abstract":"<div><div>This paper proposes a human–robot collaborative control method based on command-weighted fusion strategy for manned legged robot, addressing challenges posed by the complex structure of manned legged robots. These challenges affect both the safety of autonomous decision-making algorithms and the complexity of manual control. First, we design an autonomous command optimization method integrating terrain information and cost functions to enhance decision-making in complex terrains. Subsequently, a method for optimizing driving weighting factors is designed, utilizing a prior mechanism and rule knowledge base, while considering the influence of driver reliability and terrain complexity on driving safety and stability. Through analysis of human-machine driving intentions and the autonomous driving weighting factor, a commands weighted fusion strategy for human-machine commands is devised to achieve rational dynamic allocation of driving weighting and command fusion. Finally, validation through a human–robot collaborative control experiment demonstrates that the proposed control strategy effectively leverages the strengths of both human drivers and intelligent systems, yielding satisfactory control performance.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105323"},"PeriodicalIF":5.2,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"On the video quality captured by a surveillance mobile robot","authors":"Adwaith Vijayakumar ,&nbsp;Ishank Juneja ,&nbsp;Leena Vachhani","doi":"10.1016/j.robot.2026.105330","DOIUrl":"10.1016/j.robot.2026.105330","url":null,"abstract":"<div><div>In many robot surveillance applications, the major contributor to the quality degradation of the captured video is the unintended relative motion between the camera and the scene. This unintended motion induces an unintended effect called <em>jitter</em> in the captured video sequence. The evaluation of video quality captured by a mobile robot in surveillance scenarios is often application-specific and is often based on the amount of jitter obtained through feature tracking or camera path reconstruction or intensity patterns obtained across video frames. The contributions in this paper are two folds: development and benchmarking of a novel algorithm for video quality assessment, and jitter-specific recommendations for stabilization approaches. Unlike existing Video Quality Assessment (VQA) scores, the proposed Topology Score (TS) is a non-reference technique that does not involve feature tracking or camera path reconstruction, suitable for mobile robots used for surveillance. We adopt sliding window geometry using persistent homology concept for quantifying the jitter associated with the periodic/quasiperiodic oscillations induced by the moving mobile robots, which in turn gives a VQA score. The experimental results suggest that the trend of the proposed score aligns with the existing rhythm scores that correlate highly with human subjective evaluation, but needs reference video for the assessment. Additionally, we perform a comparative study on various video stabilization algorithms on three categories of robots based on the jitter characteristics: (1) Spherical robot videos with second-order damped oscillations causing low-frequency high-amplitude jitters, (2) Autonomous drone videos with intermittent jitters, and (3) Humanoids mimicked by the casual movements of hand-held video recorder (gait motions have a periodic structure) that contain high-frequency low-amplitude jitters from the recorder’s movement, using the proposed and existing VQA scores. We apply seven different stabilization approaches to the selected robot categories and quantify the tested algorithms’ output quality and resource requirements. Finally, we report the decision matrix based on the robot’s available resources to readily use the state-of-the-art stabilization methods in mobile robot surveillance. Our findings show that the proposed topology score is most suitable for evaluating videos captured by mobile robots in unknown environments due to non-reference assessment associated with periodic/quasiperiodic jitter, and the decision matrix to select a video stabilization algorithm based on the jitter characteristics of mobile robot for the quality improvement of captured video.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105330"},"PeriodicalIF":5.2,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Reliable Nonsingularity Adaptive fixed-time sliding mode control under input saturation for an uncertain robotic manipulator","authors":"Van Tinh Nguyen ,&nbsp;Thanh Tung Bui ,&nbsp;Hai Yen Pham ,&nbsp;Ngoc Thanh Pham ,&nbsp;Dang-Khoa Nguyen ,&nbsp;Saleh Mobayen","doi":"10.1016/j.robot.2025.105326","DOIUrl":"10.1016/j.robot.2025.105326","url":null,"abstract":"<div><div>This paper proposes a novel reliable terminal sliding mode control (TSMC) scheme for an uncertain robotic manipulator that is susceptible to parameter uncertainties, external disturbances, and input saturation. The suggested approach guarantees constant time tracking errors and non-singular convergence regardless of initial conditions by combining the classic pole placement technique with a well-designed sliding manifold. Both the unknown nonlinear dynamics and uncertainties are approximated using a radial basis function neural network (RBFNN), and the effects of input saturation are lessened by an appropriate solution. The system states converge to a small neighborhood of the origin in a limited amount of time, according to theoretical analysis based on Lyapunov stability lemmas and constant time stability. Simulation results confirm the superior performance of the proposed approach compared to existing methods, showing better accuracy, reduced chatter, and saved energy. This control strategy offers a practical and effective solution for high-precision path tracking in robotic systems operating in challenging environments.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105326"},"PeriodicalIF":5.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}