Search Results (1,226)

The design of dexterous robotic hands has long been constrained by empirical paradigms that struggle to balance anthropomorphic fidelity with dynamic performance. This study aims to establish a systematic methodology that bridges this gap through data-driven inverse design. We construct a quantitative association map between design variables and performance metrics using a comprehensive dataset of existing dexterous hands, then apply this map to translate explicit high-frequency dynamic targets into an optimized hardware configuration. The analysis reveals that the dominant principles for high-speed performance—tendon-driven transmission, proximal actuation, and lightweight rigid structures—closely mirror the biomechanical architecture of the human hand. Guided by this convergence, we develop the Beyond Hand, a 20-degree-of-freedom (DoF) anthropomorphic hand that preserves human-scale dimensions. Standardized frequency-response tests across all 15 joints show magnitude attenuation below 3 dB at 14 Hz and cutoff frequencies clustered around 10 Hz. In rhythm-game and Tetris-style manipulation tasks, the hand maintains over 90% accuracy at actuation frequencies up to 12 Hz. These results demonstrate that a performance-driven pathway can systematically elevate the dynamic capabilities of humanoid dexterous hands, offering a scalable framework for biomimetic robotic design. Full article

Long-horizon mobile manipulation requires a robot to execute a sequence of heterogeneous subtasks such as navigation, picking, and articulated-object manipulation in indoor environments. Standard reinforcement learning suffers from reward sparsity and inefficient exploration in this setting, and hierarchical methods often fail at the hand-off between consecutive subtasks when the terminal state of one subtask is kinematically infeasible for the next. We propose a motion planning-augmented hierarchical reinforcement learning architecture to resolve the fundamental trade-offs between sample efficiency and hand-off reliability in long-horizon mobile manipulation. The mission is decomposed into subtasks via a Semi-Markov Decision Process; within each subtask, a collision-free reference trajectory generated by RRT* in the full joint configuration space is embedded into the reward as a per-step shaping signal; and a region-goal mechanism, defined analytically from inverse kinematics feasibility, replaces rigid coordinate hand-offs with a continuous feasible region. The architecture is evaluated in the ManiSkill-HAB simulation under teleport-free sequential execution and challenging initialization. The proposed method improves subtask success rate and sample efficiency over the baseline across all six evaluated subtasks, and the advantage compounds along the long-horizon task chain. Full article

Handover control in human–robot collaboration remains a significant challenge. This paper proposes a three-step vision-based human–robot handover system (VHS). Vision inputs are used to perceive the environment and enable adaptive control of the robotic arm. Moreover, a three-step behavior cloning learning strategy is designed. Furthermore, a modified Temporal Difference (TD) loss function based on transfer models is proposed to train the algorithm to improve policy exploration and convergence. The proposed method results in substantial enhancements in comparative experimental validation in a simulation environment with a realistic dynamic hand model. Full article

Stable three-dimensional hand landmark reconstruction using low-cost RGB-D sensors is important for human–computer interaction, robot teleoperation, and vision-based motion analysis. RGB-based hand landmark detectors provide stable semantic 2D landmarks, but their depth output is not a metric measurement in the physical camera coordinate system. Stereo cameras can provide metric depth, but direct landmark-level back-projection is sensitive to invalid pixels, local depth holes, boundary noise, and partial occlusion. To address these problems, this paper presents a lightweight RGB-D sensing front-end that combines MediaPipe semantic hand landmarks with ZED2 stereo depth. The proposed pipeline detects 21 semantic hand landmarks in the RGB image, obtains landmark-level metric depth from the aligned ZED2 depth map using local median sampling, reconstructs 3D landmarks by camera back-projection, and further applies exponential moving average filtering and a bone-length consistency constraint. Experiments were conducted on a self-collected SVO dataset containing 13 hand actions and 26 recorded sequences, and an additional checkerboard-based reference-distance validation was performed to evaluate the metric depth sampling and 3D back-projection component. Compared with single-pixel sampling, the $5\times 5$ local median strategy slightly increased the valid-depth ratio from 0.9731 to 0.9738 and reduced the temporal smoothness metric from 1.7163 mm to 1.6902 mm. To further justify the temporal filtering choice, an additional comparison with the 1 Euro Filter was conducted using the reconstructed win5 trajectories. The 1 Euro Filter produced stronger smoothing, reducing the temporal smoothness metric to 0.196 mm, but also reduced the path-length ratio to 0.484, indicating substantial motion attenuation. EMA0.7 was therefore retained as a more balanced setting, reducing the temporal smoothness metric to 0.826 mm while maintaining a path-length ratio of 0.803. The BL0.5 bone-length constraint reduced the bone-length standard deviation from 2.0727 mm to 1.1995 mm with limited trajectory modification. The final configuration provides a practical low-cost RGB-D front-end for stable 3D hand landmark reconstruction under controlled indoor conditions. Full article

The adoption of neoadjuvant immune checkpoint inhibitor (ICI)-based chemoimmunotherapy has fundamentally transformed the operative landscape of resectable non-small cell lung cancer (NSCLC). Surgeons are now routinely confronted with ICI-altered tissue planes characterized by hilar fibrosis, vascular friability, and disrupted lymph node architecture. Simultaneously, robotic-assisted thoracic surgery (RATS) has consolidated its position as the dominant minimally invasive platform for pulmonary resection, accounting for the majority of lobectomies and segmentectomies performed at high-volume centers in 2023. Whether RATS confers specific technical advantages in this increasingly complex operative context remains incompletely characterized. We conducted a structured narrative review of published evidence, synthesizing data from randomized controlled trials, prospective cohorts, national registry analyses, and emerging technology reports addressing RATS in the setting of neoadjuvant ICI-based therapy for NSCLC. A systematic literature search was conducted across PubMed and EMBASE using predefined search terms. Available evidence, though largely retrospective and limited by small sample sizes, consistently demonstrates that RATS after neoadjuvant chemoimmunotherapy is technically feasible and oncologically sound, with R0 resection achievable in virtually all cases. The enhanced three-dimensional visualization, tremor filtration, and instrument degrees of freedom afforded by robotic platforms appear particularly advantageous in the setting of dense hilar adhesions and fragile pulmonary vasculature. Lymph node yield, a recognized robotic advantage, is preserved or enhanced despite post-ICI fibrosis. Pooled conversion rates to thoracotomy, derived from post hoc surgical analyses of ICI trial populations rather than trials designed to measure conversion, are higher than for upfront resection; available retrospective single-center data, including one direct RATS-versus-VATS comparison, suggest lower conversion rates with RATS in experienced hands, though this conclusion requires prospective validation. Emerging platform integrations, including combined robotic bronchoscopy and thoracoscopic surgery, single-port systems, and artificial intelligence-assisted anatomical navigation, are poised to further extend the reach of minimally invasive surgery in this challenging clinical scenario. In experienced centers, RATS appears to offer a technically favorable minimally invasive platform for pulmonary resection after neoadjuvant ICI-based therapy, with potential advantages over VATS in managing immunotherapy-altered anatomy; however, this conclusion is derived from retrospective series and should be interpreted cautiously pending prospective comparative data. Prospective multicenter trials with standardized surgical endpoints are urgently needed. Full article

Robot control based on visual information perception has been a hot topic in the field of industrial robots, and the use of visual servoing technology to guide robots for high-precision spatial localization of machined workpieces has a wide range of application value. Aiming at the camera hand–eye calibration error and robot repositioning error, which have a large impact on the spatial localization and navigation accuracy, and when the binocular camera Z-direction accuracy is not high enough and the viewing angle is limited, etc., we propose a spatial visual servoing algorithm based on an orthogonal vision system that combines an eye-in-hand camera and an eye-to-hand camera in a hybrid configuration. By extracting sub-pixel image features in real time and deriving directionally decoupled interaction matrices, a linear controller is designed to guide the robot in the XY-plane and Z-direction separately. This decoupling strategy enlarges the convergence domain, avoids local minima caused by coupled degrees of freedom, and enhances system stability. To this end, the intrinsic calibration and hand–eye calibration of two cameras placed orthogonally are carried out firstly, and the accuracy of hand–eye calibration is not too demanding; then the sub-pixel level image position of the target is extracted in real time and the interaction matrix is derived and a linear controller is designed to control the robot’s motion; finally, the experiments of spatial localization accuracy are completed on the KUKA iiwa to validate the effectiveness of the method. Full article

To overcome the inherent limitations of conventional offline programming in adapting to dimensional deviations and assembly-induced errors during robotic welding of ship structures, this paper proposes a point-cloud-enhanced visual scanning paradigm that enables automatic weld seam identification and collision-free trajectory planning. A dedicated monochromatic vision system is rigidly integrated onto a six-axis industrial robot, enabling high-fidelity feature extraction and geometric contour reconstruction for the precise localization of multi-configuration weld seams. The proposed approach substantially reduces manual teaching operations, enhances environmental adaptability in unstructured shipbuilding workshops, and improves global positioning accuracy. The core technical contributions are threefold: (1) systematic design and precision calibration of the integrated robotic vision system, including a hand–eye calibration procedure; (2) development of a hybrid 2D image-3D point cloud processing pipeline that combines SURF and FLANN for image stitching with RANSAC-based plane segmentation and PCA-driven contour reconstruction; and (3) extensive experimental validation across five distinct workpiece configurations. These results confirm the system’s strong applicability for intelligent and efficient shipbuilding welding, significantly outperforming conventional offline programming, which exhibits deviations exceeding 5 mm under identical conditions. Quantitative error analysis demonstrates that the online recognition method achieves a weld localization root mean square error (RMSE)of 0.82 mm, a standard deviation of 0.45 mm, and a verified maximum absolute deviation of 1.5 mm. Full article

Disabilities of the lower extremities significantly affect a person’s ability to perform activities of daily living. Many people have been affected by this type of disability due to birth disease or injury from accidents, strokes or even old age. The technical aids used in this type of disability are very basic, and rehabilitation is mainly performed by therapists. Rehabilitation consists of repetitions of exercises with normal movements that must be performed for prolonged periods of time. On the other hand, therapists, having to support the weight of the patient, tend to get injured. This paper introduces the design and modeling a robotic device intended to assist the therapist in the rehabilitation of sit-to-stand (STS) and walking movements, focusing primarily on the technical aspects of the system. The robot is designed to safely support the user’s weight and guide the user with appropriate movements according to the usual biomechanics of STS. This paper presents the solution of the inverse kinematic modeling of both the position and velocity of the robot mechanism, as well as the dynamic analysis. A series of simulations is conducted to evaluate the performance of the proposed mechanical architecture during the STS task, providing quantitative information on the system dynamics and the interaction forces between the user and the robot. The mathematical model was employed in the design of a prototype intended for children aged 8–12 years, capable of supporting up to 50 kg and providing a vertical motion range of 20–90 cm. The main structural elements of the robot, its control architecture, and its operation during the execution of the STS task are described. Finally, the conclusions of this work are discussed, and future work derived from this research is outlined. Full article

Inspired by the human hand, this work presents a hybrid rigid–soft gripper that achieves passive adaptability through a self-resetting granular jamming pouch integrated onto a 3-DOF cable-driven rigid skeleton. Seven fingertip configurations (rigid tip, different jamming particles, and TPU-only) were evaluated across five object geometries. The jamming pouch configurations showed a clear advantage over rigid fingertips and a modest improvement over TPU-only fingertips when grasping flat or smoothly curved surfaces, while demonstrating substantially superior performance for objects with sharp protrusions, lips, undercuts, or deformable edges, where enhanced conformability and geometric interlocking markedly improved payload capacity and lateral offset tolerance. The passive self-reset mechanism remained reliable over 1000 cycles. These results demonstrate that the hybrid design effectively combines the advantages of rigid and soft grippers, achieving superior overall grasping performance while balancing adaptability and payload without pneumatic actuation, with strong potential for applications in logistics, food handling, and mobile robotics. Full article

In collaborative robot lead-through teaching, fixed admittance parameters impose an inherent trade-off between operational ease and motion stability. This paper proposes a SAC-optimized fuzzy variable admittance control method (SAC-FAC). A fuzzy variable admittance controller (FAC) quantifies the operator’s motion and turning intents using interaction-force and end-effector motion information, and modulates the damping coefficient online via interpretable fuzzy rules. Soft Actor-Critic (SAC) searches offline for a well-balanced membership-function configuration on an episode basis in simulation, and the optimized configuration is then fixed for deployment. A saturation mechanism in the reward function suppresses degeneration of the damping configuration toward its physical lower bound. To counter parameter degradation under high-disturbance training, potential-based reward shaping and performance-gated curriculum learning are jointly introduced to promote stable convergence. Ablation studies and comparisons with four alternative optimizers verify the training design and support the suitability of SAC in this framework. Experiments on a UR10 collaborative robot platform with three trajectory types show that, relative to the hand-tuned FAC, SAC-FAC reduces the mean trajectory tracking error, work per unit path, and root-mean-square interaction force by 19.5%, 11.6%, and 6.8%, respectively, with more evident advantages on the compound and 3D ramp trajectories while preserving the interpretability of the fuzzy rule structure. Full article

Defining and tracking trajectories in complex environments for nonholonomic mobile robots are challenging due to the underactuated dynamics and nonintegrable velocity constraints of these robots, which preclude smooth, time-invariant feedback stabilization and yield uncontrollable linearizations around equilibrium points. As a result, maintaining structured motions such as ring-shaped limit cycles becomes particularly difficult under large initial deviations or external disturbances. In this paper, a control framework based on a dynamically generated reference trajectory is proposed, where the desired motion is defined by a modified Van der Pol oscillator. Unlike conventional approaches relying on predefined geometric paths, the proposed method embeds the target orbit into a dynamic auxiliary nonlinear system whose trajectories converge to a stable limit cycle, enabling local asymptotic convergence to the desired motion. A discontinuous robust control law is designed for a perturbed wheeled mobile robot, and the resulting closed-loop system is analyzed within the framework of solutions of systems with discontinuous right-hand sides. It is shown that the tracking error dynamics are uniformly and ultimately bounded with respect to matched disturbances and that, in the disturbance-free case, the tracking errors converge asymptotically to the origin. As a consequence, the robot’s trajectory converges to the invariant limit cycle of the reference dynamics, therebydriving the robot’s trajectory toward the invariant limit cycle of the reference dynamics. The simulation results demonstrate an improvement in the transient response relative to standard circular reference tracking. The experimental results further corroborate these findings, showing that the modified Van der Pol reference keeps the position tracking errors tightly bounded, while mitigating the large initial overshoot associated with the circular reference. Full article

Industry 5.0 places humans at the center of production systems, requiring technologies that integrate operators as active components while adapting dynamically to their physical and cognitive needs. Within this context, facilitating the learning of complex concepts becomes essential, particularly through intuitive and accessible approaches. The objective of this work is to develop a hands-on educational platform for the introduction to human–robot interaction, aligned with Sustainable Development Goal 4 (SDG4). The platform is designed to support the experiential learning of key aspects of collaboration between human and robots while simultaneously familiarizing students with practical elements, including programming, hardware implementation with microcontrollers and sensors, and the use of the Robot Operating System (ROS). The developed system is based on the use of inertial measurement units (IMUs) to capture kinematic signals, enabling real-time interaction with a collaborative robot. The platform supports both translational and orientation control, with a maximum latency of 0.3 s, ensuring responsive and effective human–robot interaction. The hands-on approach will allow students to interact directly with the test bench, putting previously learned theoretical concepts into practice, according to the principle of learn-by-doing. Full article

The use of prosthetic devices can significantly improve the quality of life of individuals with limb amputations. However, existing prosthetic hands face multiple engineering and manufacturing challenges, making them economically inaccessible to a large portion of the population. This study focuses on the design and analysis of a cost-effective prosthetic hand capable of performing five fundamental grasp types: tripod, cylindrical, spherical, lateral, and pinch. The development process began with a biomechanical analysis of the human hand, followed by the derivation of a kinematic model. To ensure anthropomorphic fidelity, finger trajectories were synthesized using a computer vision-based algorithm that captured natural human motion. These trajectories were then mapped to the prosthetic control system. Experimental validation was conducted through rigorous goniometric analysis of the prototype’s execution. The results demonstrated the system’s effectiveness in replicating functional grasps, with a Root Mean Square Error (RMSE) within acceptable thresholds for assistive tasks. While the prototype achieved high motion correspondence, higher deviations were observed in distal joints due to mechanical transmission resistance and spring-return torque requirements. This work provides a scalable framework for tendon-driven prostheses, balancing advanced trajectory synthesis with a robust and accessible mechanical architecture. Full article

Conventional industrial manipulators are often costly and come with steep learning curves, which limits their scalability in hands-on robotics education. This paper presents a compact and modular vision-guided sorting platform based on a 4-DOF SCARA robot, designed for rapid assembly, reconfiguration, and beginner-friendly deployment in laboratory courses. A collaborative visual perception strategy is proposed, which introduces a lightweight YOLOv8 algorithm for robust material category recognition, while HSV-based color segmentation and Hough circle localization are utilized to extract sub-pixel centroid features. The pixel measurements are mapped to the robot base frame through an integrated nine-point hand–eye calibration model, and joint commands are generated via a joint-space quintic polynomial interpolation algorithm to ensure continuity and avoid kinematic singularities. The overall system adopts a hierarchical architecture in which the vision host communicates target commands to a motion controller via TCP/IP, while joint actuators are driven through a CAN bus. Feasibility is first verified in a Webots digital prototype with synchronized conveyor and manipulator control, and is then validated on a physical platform equipped with a compliant TPU-based soft gripper to improve grasp tolerance under localization noise. Experiments demonstrate that the system achieves an average recognition accuracy of 98.1% and a mean positioning error of 0.189 mm. The proposed platform provides an extensible testbed for teaching kinematics, perception-to-control integration, and modular robotic system development. Full article

Respiratory rate is one of the most important vital signs. It affects ventilation which relates to oxygen inhalation and carbon dioxide elimination. Currently, only a handful of prototypes are available for estimating the respiratory rate under the condition that users remain completely still. This research focuses on the development of a respiratory rate monitoring system that can detect human respiratory signals using force sensitive resistors (FSRs). The FSR sensors measure the forces from respiratory motion and then signal processing techniques are employed to minimize background noise and artifacts. Respiratory data are processed by a microcontroller and transmitted via Bluetooth to a mobile device for further processing and visualization. The system performance was evaluated in three stages. Firstly, for the proof by simulation, a mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (PCC) of 0.26, 0.37 breaths per minute (bpm), and 0.9998 are achieved, respectively, even when the noise level is very high, i.e., power signal-to-noise ratio is 0.25 or −6.02 decibel. Secondly, for the test on a robot, the MAEs are 0.25, 0.53, and 0.75 bpm; the RMSEs are 0.28, 0.64, and 0.92 bpm; the PCCs are approximately 1, 0.9993, and 0.9986, respectively, under sitting, walking, and jogging conditions. The system is further deployed on 14 human subjects yielding MAEs of 0.51, 1.24, and 1.92 bpm; RMSEs of 0.65, 1.63, and 2.22 bpm; and PCCs of 0.9893, 0.9831, and 0.9655, for human sitting, walking, and jogging, respectively. In the future, this respiratory rate monitoring system could be applied to patients, elderly individuals, or the general population who experience movement or locomotion during monitoring. Full article