Robotics and Autonomous Systems最新文献

{"title":"Research on VSLAM algorithm based on landmark assistance","authors":"Dianfan Zhang ,&nbsp;Lei Zhou ,&nbsp;Yu Rong ,&nbsp;Xiaodi Wang ,&nbsp;Yingfeng Wang ,&nbsp;Junbo Wang","doi":"10.1016/j.robot.2025.105289","DOIUrl":"10.1016/j.robot.2025.105289","url":null,"abstract":"<div><div>The rapid advancement of intelligent driving technology has enabled functions such as autonomous parking, adaptive cruise control, and lane-keeping, which rely on precise vehicle localization. However, traditional satellite-based positioning systems fail to provide accurate localization in complex environments such as tunnels and urban canyons. To overcome these limitations, Simultaneous Localization and Mapping (SLAM) combined with sensor technologies has been widely explored for high-precision localization. However, conventional SLAM methods in outdoor environments are often susceptible to interference from dynamic objects and sensor noise, leading to degraded localization accuracy. To address these challenges, this study proposes an improved localization framework that integrates YOLOv8 with ORB-SLAM2, utilizing detected road signs as robust feature points for enhanced vehicle localization. To mitigate false positives and missed detections in YOLOv8, we optimize the dataset, enhance the feature fusion network, and refine the loss function to improve object detection accuracy. Furthermore, an edge-feature-based point-matching algorithm is introduced to reduce feature point mismatches and improve localization precision. Experimental results on the Apollo Scape dataset and real-world scenarios demonstrate that the proposed approach significantly enhances localization accuracy in dynamic environments with abundant road signs, outperforming conventional SLAM methods.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105289"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037690","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":"Dynamic merging and splitting for large-scale swarm navigation of UAVs and UGVs in unknown area","authors":"Jiang Zhao ,&nbsp;Shukun Chen ,&nbsp;Pei Chi ,&nbsp;Yingxun Wang","doi":"10.1016/j.robot.2026.105337","DOIUrl":"10.1016/j.robot.2026.105337","url":null,"abstract":"<div><div>This paper addresses the challenge of achieving efficient collaborative navigation and obstacle avoidance for aerial-ground unmanned swarm in unknown area by proposing a dynamic merge and split method. Firstly, a UAV-dominated collaborative decision-making framework is proposed, which includes a UAV allocation method driven by environmental information and a UGV planning and allocation method guided by UAVs. This framework enables collaborative and dynamic decision-making for the merge and split of the aerial-ground unmanned swarm. Secondly, an integrated dual-model and self-organizing motion control scheme for the aerial-ground unmanned swarm is designed. For the UAV swarm, a tracking–navigation dual-model motion control is developed to enhance robustness and sensing efficiency in unknown area. For the ground vehicles, a dynamic-boundary-based self-organizing motion control is proposed to leverage their high autonomy. Collectively, these control strategies yield highly efficient and coordinated motion control for the aerial-ground unmanned swarm. Finally, numerical simulation experiments are conducted and performance metrics are designed to compare the proposed method with existing representative approaches. The results indicate that the advantages of the proposed method gradually become more pronounced as the swarm scale increases.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105337"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037688","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":"2026-04-01","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":"RoCeDiRNet-3DoF: Robust center direction network for cloth grasping point localization in complex scenes","authors":"Jiaxiang Luo,&nbsp;Yufan Hu","doi":"10.1016/j.robot.2026.105328","DOIUrl":"10.1016/j.robot.2026.105328","url":null,"abstract":"<div><div>Cloth grasping poses a fundamental challenge in robotics and computer vision, and constitutes a key capability for service robots. Accurate and robust localization of fabric grasping points is a critical prerequisite for enabling efficient and dexterous fabric manipulation. While recent deep learning approaches have achieved promising results on standard benchmarks, their performance still degrades substantially in complex scenarios involving severe folding, occlusion, and unknown backgrounds. This limitation primarily arises from insufficient global contextual modeling in existing dense regression frameworks, which rely heavily on local feature extraction while lacking the ability to capture global dependencies crucial for understanding fabric deformation patterns. To address these challenges, we propose <strong>RoCeDiRNet-3DoF</strong>, a novel framework built upon an InceptionNeXt encoder and a <strong>Wavelet Multiscale Convolutional Attention Decoder (WMCAD)</strong>. WMCAD adopts a three-stage hierarchical architecture, incorporating Wavelet Convolution Blocks for global feature extraction, Dynamic Wavelet Upsampling Blocks to preserve semantic details during interpolation, and Multi-scale Mixed Attention Gates for effective cross-layer feature fusion. By leveraging WMCAD’s enhanced global feature modeling, RoCeDiRNet-3DoF achieves superior performance in challenging scenarios, achieving state-of-the-art (SOTA) results on the ViCoS dataset with an F1 score of 82.6%. Furthermore, across various complex scenario configurations, RoCeDiRNet-3DoF consistently outperforms competing methods, representing the current optimal solution for this task. The source code is available at: <span><span>https://github.com/hyf381752569-stack/RoCeDiRNet-3DoF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105328"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940038","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":"HAD-TAMP: Human adaptive task and motion planning for human–robot collaboration in industrial scenario","authors":"Alberto Gottardi ,&nbsp;Matteo Terreran ,&nbsp;Enrico Pagello ,&nbsp;Emanuele Menegatti","doi":"10.1016/j.robot.2025.105318","DOIUrl":"10.1016/j.robot.2025.105318","url":null,"abstract":"<div><div>Task and Motion Planning (TAMP) is essential for efficient Human–Robot Collaboration (HRC) in industrial settings, yet existing approaches struggle to handle human interventions and dynamic environments. This paper presents a Human Adaptive Task and Motion Planning (HAD-TAMP) framework that seamlessly integrates human pose and actions into the planning process to quickly adapt to human requests or deviations from the process plan. The framework consists of three key modules: a task planning module, which generates and updates task sequences based on real-time human input, a motion planning module composed of a set of motion planners specialized for different phases of the collaboration (e.g., collaborative transportation of materials), and a context reasoner module which coordinates the overall process based on the sensory information available. A key contribution is using a receding horizon strategy, enabling real-time adaptation to human inputs and environmental changes. The approach is validated in a real industrial HRC scenario through two applications: gesture-based human–robot interaction and close human–robot collaboration in carbon fiber draping. Experimental results demonstrate the framework’s effectiveness in ensuring adaptability to multiple human requests and efficiency: the re-planning time is 4 times and 5 times faster than the generation of a new plan.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105318"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885483","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":"2026-04-01","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":"Novel prescribed performance control for a kind of back-support exoskeleton with time delays","authors":"Xiaogang Li ,&nbsp;Yuan Ke","doi":"10.1016/j.robot.2026.105350","DOIUrl":"10.1016/j.robot.2026.105350","url":null,"abstract":"<div><div>Back-support exoskeletons (BSEs) offer significant potential for reducing lumbar load and preventing lower-back injuries; however, their control is complicated by strong inter-channel coupling, time delays in actuation and sensing, and uncertainties induced by human–robot interaction. This paper develops a unified dynamic–kinematic model for a multi-input multi-output BSE that explicitly accounts for coupling effects, compliance, and delay characteristics, providing a control-oriented and physically consistent foundation. Based on this model, a prescribed performance control (PPC) framework is proposed to guarantee bounded tracking errors with predefined transient and steady-state behaviour in the presence of multidimensional, time-varying delays, without requiring model decoupling. To enhance robustness against lumped uncertainties, a hybrid observer integrating a linear extended state observer and a sliding mode observer is designed for real-time disturbance estimation and compensation. Simulation results obtained on a biomechanically realistic BSE platform demonstrate that the proposed PPC–LESO–SMO scheme achieves superior tracking accuracy, robustness, and convergence speed compared with conventional PPC and existing observer-based control approaches.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105350"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978326","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":"2026-04-01","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":"Uncertainty Aware-Predictive Control Barrier Functions: Safer human–robot interaction through probabilistic motion forecasting","authors":"Lorenzo Busellato ,&nbsp;Federico Cunico ,&nbsp;Diego Dall’Alba ,&nbsp;Marco Emporio ,&nbsp;Andrea Giachetti ,&nbsp;Riccardo Muradore ,&nbsp;Marco Cristani","doi":"10.1016/j.robot.2025.105291","DOIUrl":"10.1016/j.robot.2025.105291","url":null,"abstract":"<div><div>To enable flexible, high-throughput automation in settings where people and robots share workspaces, collaborative robotic cells must reconcile stringent safety guarantees with the need for responsive and effective behavior. A dynamic obstacle is the stochastic, task-dependent variability of human motion: when robots fall back on purely reactive or worst-case envelopes, they brake unnecessarily, stall task progress, and tamper with the fluidity that true Human–Robot Interaction (HRI) demands. In recent years, learning-based human-motion prediction has rapidly advanced, although most approaches produce worst-case scenario forecasts that often do not treat prediction uncertainty in a well-structured way, resulting in over-conservative planning algorithms, limiting their flexibility. This paper introduces Uncertainty-Aware Predictive Control Barrier Functions (UA-PCBFs), a unified framework that fuses probabilistic human hand motion forecasting with the formal safety guarantees of Control Barrier Functions (CBFs). In contrast to CBFs and other variants, our framework allows for a dynamic adjustment of the safety margin thanks to the human motion uncertainty estimation provided by the deep-learning forecasting module. Thanks to the awareness of prediction uncertainty, UA-PCBFs empower collaborative robots with a deeper understanding of future human states, facilitating more fluid and intelligent interactions through informed motion planning. Our key contribution is the first integration of epistemic prediction uncertainty directly into predictive CBFs, dynamically adjusting safety margins based on forecast confidence without assumptions about uncertainty evolution. We validate UA-PCBFs through comprehensive real-world experiments with an increasing level of realism, including automated setups (to perform exactly repeatable motions) with a robotic hand and direct human–robot interactions (to validate promptness, usability, and human confidence). Relative to state-of-the-art HRI architectures, UA-PCBFs show better performance in task-critical metrics, significantly reducing the number of violations of the robot’s safe space during interaction with respect to the state-of-the-art. Data and code will be released upon acceptance.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105291"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790304","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":"Many-objective-optimized semi-automated robotic disassembly sequences","authors":"Takuya Kiyokawa ,&nbsp;Kensuke Harada ,&nbsp;Weiwei Wan ,&nbsp;Tomoki Ishikura ,&nbsp;Naoya Miyaji ,&nbsp;Genichiro Matsuda","doi":"10.1016/j.robot.2025.105301","DOIUrl":"10.1016/j.robot.2025.105301","url":null,"abstract":"<div><div>This study tackles the problem of many-objective sequence optimization for semi-automated robotic disassembly operations. To this end, we employ a many-objective genetic algorithm (MaOGA) inspired by the non-dominated sorting genetic algorithm (NSGA)-III, along with robotic-disassembly-oriented constraints and objective functions derived from geometrical and robot simulations using three-dimensional (3D) geometrical information stored in a 3D computer-aided design (CAD) model of the target product. The MaOGA begins by generating a set of initial chromosomes based on a contact and connection graph (CCG), rather than random chromosomes, to avoid falling into a local minimum and yield repeatable convergence. The optimization imposes constraints on feasibility and stability as well as objective functions regarding difficulty, efficiency, prioritization, and allocability to generate a sequence that satisfies many preferred conditions under mandatory requirements for semi-automated robotic disassembly. The NSGA-III-inspired MaOGA also utilizes non-dominated sorting and niching with reference lines to further encourage steady and stable exploration and uniformly lower the overall evaluation values. Our sequence generation experiments for a complex product (36 parts) demonstrated that the proposed method can consistently produce feasible and stable sequences with a 100% success rate, bringing the multiple preferred conditions closer to the optimal solution required for semi-automated robotic disassembly operations.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105301"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790307","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}