Search Results (37)

This paper presents an edge-deployable vision-based framework for human–robot interaction using a xArm collaborative robot and a single RGB camera mounted on the robot wrist, and lightweight AI-based perception modules. The system enables intuitive, contact-free control by combining hand understanding and object detection within a unified perception–decision–control pipeline. Hand landmarks are extracted using MediaPipe Hands, from which continuous hand trajectories, static gestures, and dynamic gestures are derived. Task objects are detected using a YOLO-based model, and both hand and object observations are mapped into the robot workspace using ArUco-based planar calibration. To ensure stable robot motion, the hand control signal is smoothed using low-pass and Kalman filtering, while dynamic gestures such as waving are recognized using a lightweight LSTM classifier. The complete pipeline runs locally on edge hardware, specifically NVIDIA Jetson Orin Nano and Raspberry Pi 5 with a Hailo AI accelerator. Experimental evaluation includes trajectory stability, gesture recognition reliability, and runtime performance on both platforms. Results show that filtering significantly reduces hand-tracking jitter, gesture recognition provides stable command states for control, and both edge devices support real-time operation, with Jetson achieving consistently lower runtime than Raspberry Pi. The proposed system demonstrates the feasibility of low-cost edge AI solutions for responsive and practical human–robot interaction in collaborative industrial environments. Full article

Biarticular actuators can enhance efficiency and stability in legged locomotion by transferring energy between joints. Their effectiveness depends strongly on the lever arm ratio—the ratio of the actuator’s moment arm at one joint to its moment arm at another—which governs how torque is distributed across joints during movement. Inspired by biomechanics, early robotic studies implemented biarticular actuators to improve energy efficiency, joint coordination, and positional control, primarily in planar or single-joint systems, leaving a gap in fully 3D robotic legs. Here, we present a geometry optimization framework for a robotic leg incorporating both biarticular and monoarticular actuators. Using human motion capture and joint torque data, we optimized the linkage mechanisms so that the system can maintain the required joint torques while keeping biarticular actuator moment arm ratios near their optimal values during walking and running. The optimized leg achieved a minimum achievable cost of transport of approximately $0.41 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for walking and $0.62 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for running. Full article

This study proposes a reproducible and accessible methodological framework for non-planar path generation to enable scaffold biofabrication on irregular anatomical surfaces replicating the native morphology of human tissue. By integrating a simulation-based trajectory optimization system with a robotic arm, lattice paths are generated using an intersection-based method with parallel planes. This method is processed by intersecting the anatomical object with orthogonal planes, allowing for the creation of paths that conform to complex geometries. The proposed approach relies on widely available and commonly used tools, such as MATLAB, avoiding the need for highly specialized software. Thus, a MATLAB-based kinematic model computes optimal end-effector trajectories, while a coaxial nozzle facilitates the simultaneous extrusion of an alginate-based biomaterial. The proposed method ensures smooth trajectory execution, achieving positional standard deviation within the reproducibility threshold of the robotic arm for an optimal path discretization density. Unlike conventional planar methods, the optimized approach achieves positional accuracy within the robotic arm’s reproducibility threshold while demonstrating superior geometric conformity on complex anatomical patches. The approach successfully fabricates scaffolds with controlled deposition on anatomical patches, demonstrating improved geometric conformity over traditional planar methods. This method provides a pathway for patient-specific scaffold fabrication, supporting advances in tissue engineering and regenerative medicine. Full article

Functional Electrical Stimulation (FES) can restore motor function; however, achieving precise multi-joint control remains challenging due to nonlinear muscle dynamics and fatigue. Reinforcement Learning (RL) offers a promising solution, but practical deployment is hindered by the need for patient-specific calibration. This study investigates offline RL approaches for controlling planar arm movements using heterogeneous datasets, aiming to enable zero-shot transfer to new users. We develop a biomechanical arm model in MuJoCo and evaluate four RL algorithms coupled with three offline techniques: conservative Q learning (SAC-CQL and QBR-CQL), Randomized Ensemble (QBR-REM), and distributional RL (IQNBR). Across all conditions, IQNBR demonstrates robust learning and superior control performance, achieving an average RMSE of $3.8\pm 0.6$ cm, even when trained on mixed-quality data. These results highlight the potential of distributional RL as a base learning method to build generic FES controllers that can operate without exhaustive calibration, with broader implications for controlling robots with human-like actuation systems. Full article

Robotic arm-based antenna measurement systems offer the flexibility needed for advanced antenna measurement and diagnostics techniques but are typically limited by reach and sampling time. This work integrates two complementary contributions to overcome these constraints. First, a composite-plane range extension is introduced for a medium-size robot mounted on a mobile platform and monitored by an optical tracking system (OTS). Independent planar scans are acquired after manual repositioning of the robot and then accurately aligned and blended into a single, larger measurement plane, with positioning errors mitigated through a calibration process. Second, a localized sparse sampling strategy is proposed to accelerate planar near-field (PNF) measurements when only selected angular regions of the radiation pattern are required. The approach relies on reduced-order modeling and singular value decomposition (SVD) analysis to design non-redundant grids that preserve the degrees of freedom relevant to the truncated angular sector, thereby reducing both the number of samples and the scan area. Numerical examples for a general case and experimental validation in X-band demonstrate that the combined methodology extends the effective measurement aperture while significantly shortening acquisition time for narrow or tilted beams, enabling accurate and portable in situ characterization of complex modern antennas by means of cost-effective acquisition systems. Full article

The design, control, simulation and animation of robotic systems heavily depend on dynamic modeling. A variety of studies have explored different dynamic modeling methodologies applied to diverse robotic mechanisms. Artificial neural networks (ANNs) have proven their value in engineering design in recent years, enhancing the understanding of complex mechanisms as well as shortening experimental periods and decreasing related expenses. This study investigates the application of various neural network algorithms for the analysis of a custom-designed three-link planar revolute–prismatic–revolute (RPR) robotic arm mechanism. Initially, the Euler–Lagrange equations of motion for the RPR mechanism are derived. Joint accelerations are then computed under different mass configurations of the robotic links, resulting in a dataset comprising 204 joint acceleration samples. Six distinct neural network models are subsequently employed to perform regression analysis on the collected data. The primary objective of this study is to analyze the relationship between joint accelerations and varying link masses under constant joint torques and forces, while its secondary aim is to present a representative application of neural networks as regression learners for the dynamic modeling of robotic mechanisms. The approach outlined in this study allows users to select appropriate neural network algorithms for use in specific applications, considering the wide range of available algorithms. Link mass variations and their effects on joint accelerations are investigated, establishing a basis for the modeling of robotic dynamics using regression-based neural networks. The results indicate that the optimizable neural network algorithm produces the best regression accuracy results, although the other models maintain similar performance levels. Full article

This research aims to investigate the practicality and feasibility of pneumatic muscle actuators (PMAs) applied in the pneumatic servo system. The mechanism consists of closed symmetrical planar robotic arms driven by two pairs of opposing PMAs, whose structure is similar to human arms. Importantly, the two distal links (or wrist parts) are combined into a collective end-effector, whose desired position is controlled only by the two shoulder angle joints. When two pairs of PMAs are attached to the upper arms, they actuate each shoulder and assist in the movement of the arms. However, the nonlinear behavior, high hysteresis, low damping, and time-varying characteristics of PMAs significantly limit their controllability. Therefore, to effectively address these challenges, a Fourier series-based adaptive sliding mode controller with $H_{\mathrm{\infty }}$ (FSB-ASMC + $H_{\mathrm{\infty }}$) is employed to achieve accurate path tracking of the PMAs. This control approach not only compensates for approximation errors, disturbances, and unmodeled dynamics but also ensures the desired $H_{\mathrm{\infty }}$ positioning performance of the overall system. The controller method can not only effectively prevent approximation errors, disturbances, and un-modeled dynamics but can also ensure the required $H_{\mathrm{\infty }}$ positioning performance of the whole system. Thus, the results of the experiment showed that the control strategy for the system collocating the FSB-ASMC + $H_{\mathrm{\infty }}$ can attain excellent control performance. Full article

This paper pioneers the integration of hyperspectral imaging and robotics for the automated analysis of cultural heritage, representing a measurable advancement over existing manually operated systems. For the first time in the cultural heritage domain, a compact push-broom hyperspectral camera working in the VNIR range has been successfully mounted on a robotic arm, enabling precise and repeatable acquisition trajectories without the need for manual intervention. Unlike traditional approaches that rely on fixed paths or manual repositioning, the proposed approach allows dynamic and programmable imaging of both planar and volumetric objects, greatly improving adaptability to complex geometries. The integrated system achieves spectral reliability comparable to established manual methods, while offering superior flexibility and scalability. Current limitations, particularly regarding the illumination setup, are discussed alongside planned optimisation strategies. Full article

In this article, a novel procedure is developed to properly handle the 3-D mispositioning of the scanning probe in the near-field to far-field (NFtFF) transformations with spherical scanning for quasi-planar antennas under test, which make use of a non-redundant (NR) number of samples. It proceeds through two stages. In the former, a phase correction technique, named spherical wave correction, is applied to compensate for the phase shifts of the collected NF samples, which do not belong to the measurement sphere, due to mechanical defects of the arc, or inaccuracy of the robotic arm employed in the considered NF facility driving the probe. Once the phase shifts have been compensated, the recovered NF samples belong to the set spherical surface, but their positions differ from those prescribed by the adopted NR representation, because of an imprecise control and/or inaccuracy of the positioning system. Thus, the resulting sampling arrangement is affected by 2-D mispositioning errors. Accordingly, an iterative procedure is used in the latter step to restore the NF samples at their exact locations from those determined at the first step. Once the correct sampling arrangement has been retrieved from the 3-D mispositioned one, an optimal sampling interpolation formula is employed to obtain the massive input NF data necessary for the classical spherical NFtFF transformation technique. Numerical results, showing the precision of the NF and FF reconstructions, assessed the efficacy of the developed procedure. Full article

Variations in the drive motor’s load inertia during wafer transfer robot arm motion critically degrade end-effector trajectory accuracy. To address this challenge, this study proposes an anti-disturbance control strategy integrating Radial Basis Function Neural Network (RBFNN) and event-triggered mechanisms. Firstly, dynamic simulations reveal that nonlinear load inertia growth increases joint reaction forces and diminishes trajectory precision. The RBFNN dynamically approximates system nonlinearities, while an adaptive law updates its weights online to compensate for load variations and external disturbances. Secondly, an event-triggered mechanism is introduced, updating the controller only when specific conditions are met, thereby reducing communication burden and actuator wear. Subsequently, Lyapunov stability analysis proves the closed-loop system is Uniformly Ultimately Bounded (UUB) and prevents Zeno behavior. Finally, simulations on a planar 2-DOF manipulator demonstrate significantly enhanced trajectory tracking accuracy under variable loads. Critically, the adaptive neural network control method reduces trajectory tracking error by 50% and decreases controller update frequency by 84.7%. This work thus provides both theoretical foundations and engineering references for high-precision wafer transfer robot control. Full article

In Aerial Manipulation, the motion of the robotic arm can cause unwanted movements of the flying base affecting the trajectory tracking capability. A possible solution to reduce these disturbances is to use a free revolute joint between the flying base and the manipulator, thus reducing the torque applied to the base from the manipulator. In this paper, a novel approach to solve the inverse kinematics of an aerial manipulator with a free revolute joint is presented. The approach exploits the Generalized Jacobian to deal with the presence of a mobile base, and the dynamics of the system is considered to predict the motion of the non-actuated joint; external forces acting on the system are also included. The method is implemented in MATLAB for a planar case considering the parameters of a real manipulator attached to a real octocopter. The tracking of a trajectory with the end-effector and a load picking task are simulated for a non-redundant and for a redundant manipulator. Simulation results demonstrate the capability of this approach in following the desired trajectories and reducing rotation and horizontal translation of the base. Full article

The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the disadvantage of heavy labor intensity and a high risk factor. The robots that install two-split and four-split spacer bars cannot be applied to the complex operating conditions of six-split transmission lines. In order to improve the installation efficiency of spacers and reduce operating costs and risks, a new type of spacer-installing robot was researched based on the six-split transmission lines in this paper. Through the theoretical analysis of the wire’s arc sag, the moving device of the robot was designed. In order to improve the operating efficiency of the robot, the storage and feeding device of the six spacers was designed. A planar arm with the ability to assemble the spacer was designed. The overall design of the robot was completed by integrating the design of each unit. Through the experimental test, the results indicated that the robot was capable of installing six spacers at once, the maximum moving slope was 15 degrees, and the error rate in the spacer installation was 2.33%, which matched the manual installation of the spacers. The robot provided new ideas for the design of new transmission line engineering equipment and expanded the scope of the application of robots in the power industry. Full article

While prior studies have explored developing mycelium paste for EAM of this material, this research streamlined the EAM workflow for preparing living, extrudable mycelium mixtures, involving alterations in the preparation sequence and adjustments in the admixture ratios. The resultant mycelium mixture was employed in a series of experiments to optimize the parameters of robotic EAM using Artificial Neural Networks. Next, a performance-based acoustic wall was designed informed by simulation in Pachyderm. Building upon previous research by authors, two adjacent panels with high complex geometric features were selected for fabrication, presenting a challenging test scenario, as conventional planar slicing introduces stair-stepping phenomena, while non-planar slicing introduces irregularities in layer height. To address these, a hybrid slicing strategy was used by integrating both slicing techniques. Next, an experimental framework was established to assess the influence of EAM toolpath planning factors on the acoustic properties of the designed acoustic panels. Lastly, two panels were fabricated using an ABB IRB 2400 robotic arm. The alignment of the toolpath planning factors and EAM parameters resulted in a uniform material deposition in the final fabricated panels. This study underscores the transformative capacity of robotic EAM and conformal toolpath planning, presenting the development of biodegradable building materials and advanced acoustic solutions. Full article

To address issues such as leaf occlusion, misalignment of the harvesting robotic arm, and limited harvesting range in hillside chili fields, this paper designs an intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling. The system integrates real-time data from a LiDAR and IMU inertial navigation system to reconstruct the chili point cloud occluded by leaves from multiple perspectives. To address issues such as misalignment of the robotic arm caused by terrain undulations, the system integrates an adaptive leveling platform and an H-shaped planar slide, combined with a gyroscope to dynamically adjust the arm’s posture in real time, ensuring arm stability while expanding its workspace. In addition, to ensure harvesting efficiency and pepper integrity, an integrated cutting–gripping flexible end effector is designed to achieve synchronized cutting and collection operations. The experiment shows that the system achieves recognition accuracy of 81.95% for occluded chili peppers and 89.04% for non-occluded chili peppers. The harvesting success rate is 86.33%, with a single harvesting operation taking 13.17 s. During prolonged operation, the harvesting success rate can be maintained at approximately 85.1%. In summary, the intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling provides a feasible solution for automated pepper harvesting. Full article

Control remains a challenge in precision applications in robotics, particularly when combined with execution in small time intervals. This study employed a two-degree-of-freedom (2-DoF) planar robotic arm driven by a detailed human musculoskeletal model for actuation, incorporating nonlinear control techniques to execute a precision task through simulation. Then, we compared these simulations with real experimental data from healthy subjects performing the same task. Our results show that the Feedback Linearization Control (FLC) applied performed satisfactorily within the task execution constraints compared to a robust nonlinear control technique, i.e., Sliding Mode Control (SMC). On the other hand, differences can be observed between the behavior of the simulated model and the real experimental data, where discrepancies in terms of errors were found. The model errors increased with the amplitude and remained unchanged with any increase in the task execution frequency. However, in human trials, the errors increased both with the amplitude and, notably, with a drastic rise in frequency. Full article