Robotics and Autonomous Systems最新文献

{"title":"Obstacle crossing in revolute and prismatic knee underactuated biped robots","authors":"Krishnendu Roy ,&nbsp;R. Prasanth Kumar","doi":"10.1016/j.robot.2026.105340","DOIUrl":"10.1016/j.robot.2026.105340","url":null,"abstract":"<div><div>Obstacle crossing is an important ability in biped and humanoid robots that are designed to traverse unstructured terrain. We consider the problem of determining the maximum (a) height, (b) width, (c) cross-sectional area, (d) thin vertical barrier height, and (e) square area of the obstacle that an underactuated biped robot with point-feet can cross while walking slowly. Two different biped robot configurations are compared for obstacle crossing: revolute knee and prismatic knee. The path needed to overcome the obstacle without touching it is determined with the help of binary occupancy grid in the sagittal plane and using genetic algorithm based maximization for each of the five cases, considering thin links as well as thick links for the biped robots. The determined collision free path for obstacle crossing is implemented as a trajectory and demonstrated in dynamic simulation in Mujoco simulation environment. In order to control the position of zero moment point (ZMP) and the ground projection of center of mass for stability, a reaction wheel in the torso is utilized. It is observed that increasing the thicknesses of the biped robot links in general has an effect of reducing the maximum size of the obstacle that can be crossed. Further, prismatic knee biped robot performs better than revolute knee biped robot in crossing large obstacles, especially with thick links. Experiments on a prismatic-knee biped robot further validate the results of GA and MuJoCo simulations.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105340"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939678","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":"2026-04-01","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}

{"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":"2026-04-01","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":"An expansion and evolution framework of frame transformation for complex robotic systems","authors":"Si-Hao Zuo ,&nbsp;Yan-Jiang Zhao ,&nbsp;Yong-De Zhang ,&nbsp;Le Ye","doi":"10.1016/j.robot.2025.105312","DOIUrl":"10.1016/j.robot.2025.105312","url":null,"abstract":"<div><div>Frame transformation is the basis in the robotic system, and the topology structural linkage between the frames is the foundation of the frame transformation. However, the frame transformation process for a complex robotic system is rather challenging and complicated because there may exist a lot of frames to be defined and linked in different technical scenarios, which are inexplicit and individual. This severely hinders the exchanges between the engineers and hampers the development of robotic technologies as well. This paper proposes a novel framework to explicitly describe the linkages between the frames, and automatically realize a complete frame transformation for the robotic system. The framework involves three layers: the semantic description layer, the frame topology relationship layer, and the mathematical calculation layer, and it achieves a unity of the three layers. We define an element module to semantically describe each object with a fixed frame, design a relative position transformation chain (RPTC) to explicitly describe the topology structural linkages between the frames. Then, an expansionary and evolutionary strategy is proposed to obtain a final result of the RPTC for the robotic system by the expansions and evolutions of the short RPTCs. Finally, a software platform is developed for the realization of the framework, and a case of a classical medical robotic system is performed as an example. And the results of the expansion and the evolution degrees prove the validity of the proposed method.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105312"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940040","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":"Vision-driven river following of UAV via safe reinforcement learning using semantic dynamics model","authors":"Zihan Wang,&nbsp;Nina Mahmoudian","doi":"10.1016/j.robot.2026.105357","DOIUrl":"10.1016/j.robot.2026.105357","url":null,"abstract":"<div><div>Vision-driven autonomous river following by Unmanned Aerial Vehicles is critical for applications such as rescue, surveillance, and environmental monitoring, particularly in dense riverine environments where GPS signals are unreliable. These safety-critical navigation tasks must satisfy hard safety constraints while optimizing performance. Moreover, the reward in river following is inherently history-dependent (non-Markovian) by which river segment has already been visited, making it challenging for standard safe Reinforcement Learning (SafeRL). To address these gaps, we cast river following as a coverage control problem with a submodular reward that exhibits diminishing returns as more river segments are visited, framing the task as a Submodular Markov Decision Process. Building on the SafeRL paradigm and the First Order Constrained Optimization in Policy Space (FOCOPS) algorithm, we propose three contributions.</div><div>First, we introduce the Marginal Gain Advantage Estimation (MGAE), which refines the reward advantage function using a sliding-window baseline calculated from historical episodic returns, aligning the advantage estimate with non-Markovian dynamics. Second, we develop a Semantic Dynamics Model (SDM) based on patchified water semantic masks, offering more interpretable and data-efficient short-term prediction of future observations compared to latent vision dynamics models. Third, we present the Constrained Actor Dynamics Estimator (CADE) architecture, which integrates the actor, cost estimator, and SDM for cost advantage estimation to form a model-based SafeRL framework capable of solving partially observable Constrained Submodular Markov Decision Processes.</div><div>The simulation results demonstrate that MGAE achieves faster convergence and superior performance compared to critic-based methods like Generalized Advantage Estimation. SDM provides more accurate short-term state predictions, enabling the cost estimator to better predict potential violations. Overall, CADE effectively integrates safety regulation into model-based RL, with the Lagrangian approach providing a “soft” balance between reward and safety during training, while the safety layer enhances inference by imposing a “hard” action overlay. Our code is publicly available on Github (<span><span>https://github.com/EdisonPricehan/omnisafe-cade/tree/cade</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105357"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037691","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":"The rapid rise of soft robotics in surgical operations: Trends, challenges, and future directions","authors":"Babatunde Olamide Omiyale ,&nbsp;Olamide Femi Akinsola ,&nbsp;Muhammad Awais Ashraf ,&nbsp;Niyi Gideon Olaiya ,&nbsp;Akinola Ogbeyemi ,&nbsp;Wenjun Chris Zhang","doi":"10.1016/j.robot.2025.105324","DOIUrl":"10.1016/j.robot.2025.105324","url":null,"abstract":"<div><div>This paper investigates the transformative impact of soft robotics on surgical operations, particularly in the development of next-generation minimally invasive techniques. Conventional surgical procedures are often influenced by various factors, such as patient positioning, the precision of surgical instruments, the surgeon’s experience, and physical conditions. These factors can make it challenging to accurately execute predetermined surgical plans, which can inevitably reduce surgical precision and safety. To address these challenges, soft robotic systems that mimic the flexibility and adaptability of biological tissues provide significant advantages over conventional rigid tools. These advantages include enhanced dexterity, reduced tissue trauma, and improved patient outcomes. Soft robots are made from compliant materials (e.g., silicone, hydrogels), which make them gentler on delicate tissues and organs. They can navigate tight or sensitive areas (e.g., the brain, heart, abdomen), allow for smaller incisions, minimize blood loss, reduce the risk of infection, and minimize recovery time, scarring, and human error caused by tremors or physical strain. This review examines recent advancements in soft robotics, clinical applications, addresses technological challenges, and identifies future directions for integrating soft robotics into mainstream surgical practice.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105324"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978235","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":"2026-04-01","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":"Enhanced flexibility and dexterity in robotic endoscopy via a 6-DOF parallel mechanism and eye-gaze-assisted field-of-view control","authors":"Mengtang Li ,&nbsp;Shen Zhao ,&nbsp;Shuai Wang ,&nbsp;Fanmao Liu","doi":"10.1016/j.robot.2025.105322","DOIUrl":"10.1016/j.robot.2025.105322","url":null,"abstract":"<div><div>In conventional minimally invasive surgery, an assistant manually steers the endoscope based on the surgeon’s verbal commands, but fatigue and tremor can degrade field-of-view (FOV) stability and efficiency. Robotic endoscopes address this limitation through automated FOV adjustment via image-based visual servoing, ensuring smooth and stable visualization. However, most robotic implementations mount rigid straight-rod endoscopes on external serial arms, limiting dexterity and complicating remote-center-of-motion (RCM) control. Moreover, many automated FOV methods track surgical-tool tips without representing the surgeon’s intention. This work therefore presents a compact 6-DOF parallel endoscopic mechanism that improves flexibility and dexterity while simplifying RCM constraint satisfaction, together with an eye-gaze-assisted multi-tool tracking controller that dynamically weights tools according to surgeon attention. Simulations and experiments across diverse scenarios demonstrate FOV stabilization within 2 s, mean image-space tracking error <span><math><mo>&lt;</mo></math></span> 20 pixels, eye hand error <span><math><mo>&lt;</mo></math></span> 3°, and at least a 30% reduction in unnecessary FOV adjustments. Supplementary video is available.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105322"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885484","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":"2026-04-01","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":"Modeling and solving UAV 3D trajectory planning using an efficient multi-mechanism enhanced chaos game optimization algorithm","authors":"Jingsen Liu ,&nbsp;Fei Li ,&nbsp;Yu Li ,&nbsp;Huan Zhou","doi":"10.1016/j.robot.2026.105361","DOIUrl":"10.1016/j.robot.2026.105361","url":null,"abstract":"<div><div>Three-dimensional (3D) trajectory planning for unmanned aerial vehicles (UAVs) is one of the critical technologies for achieving Self-guided flying, targeting computing a safe, efficient, and energy-optimal flight path. To satisfy the realistic flight needs of unmanned aerial vehicles, this paper establishes a mathematical model for 3D trajectory planning with optimization objectives including flight energy consumption, path length, flight altitude, flight threats, and trajectory smoothness. Simultaneously, the spherical coordinate system is utilized to optimize the UAV’s flight attitude and path, and cubic quasi-uniform B-spline curves are employed to smooth the flight trajectory. To better address and optimize the UAV 3D trajectory planning problem, a boosted chaos game optimization algorithm (SACGO) is put forward in this research, which effectively equilibrates the algorithm’s worldwide and localized search functionalities through a phased multi-mechanism mutation strategy. Additionally, an adaptive convergence factor based on dual random perturbations and cyclic boundary handling is introduced, enhancing the quality of candidate solutions while accelerating convergence speed. Regarding the analysis of theoretical algorithms, the improved algorithm has the exact same complexity in time as the original CGO, according to a time complexity study. Furthermore, probability measure methods are used to prove the global convergence of SACGO. For algorithm performance testing, we first performed a coupled sensitivity analysis on the two perturbation parameters within the improvement mechanism to identify their optimal values. Subsequently, SACGO is contrasted with 7 representative high-performance algorithms across multiple dimensions and evaluation methods on the CEC2022 and CEC2017 benchmark suites. The findings show that SACGO's convergence speed, solution correctness, and stability are all markedly improved by the suggested modifications. Finally, SACGO is used for UAV trajectory planning and tested against other algorithms in six scenarios with varying complexities. The solving results of 8 algorithms, including SACGO, are shown. SACGO efficiently plans flight paths under all constraints, comprehensively optimizes all objectives, and generates trajectories that are not only safe and feasible but also achieve the lowest trajectory cost.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"198 ","pages":"Article 105361"},"PeriodicalIF":5.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078414","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}