Search Results (853)

Residual vibration of industrial manipulators can limit positioning efficiency and dynamic accuracy during high-speed motion. This study develops an integrated vibration-suppression framework for a rigid-link manipulator with flexible-joint dynamics. A controller-oriented rigid–flexible model with lumped disturbances is established, and a disturbance observer (DOB) is employed as the inner-loop compensation layer under a small-gain robustness constraint. On the compensated nominal model, partial eigenstructure assignment (PESA) selectively increases the damping of the retained flexible modes while preserving the rigid-body eigenstructure associated with trajectory tracking. A pose-dependent gain-scheduling mechanism further updates the PESA feedback gain to accommodate configuration-dependent modal-frequency variation. Numerical comparisons with conventional PID, standalone DOB, and standalone PESA demonstrate improved residual-vibration attenuation and settling behavior. Hardware tests on an Aubo i5 manipulator, with 16-channel responses directly acquired under the respective control configurations, further show an approximately 80% reduction in the representative low-frequency vibration amplitude relative to the PID baseline under the considered operating condition. Full article

Architectured soft structures have unlocked new possibilities for designing continuum manipulators with tailored mechanical performance. This work introduces a bioinspired soft manipulator based on modular wave spring units, leveraging the high elasticity of wave spring structures to enable compliant deformation and axial extensibility of the soft manipulator. To achieve precise control of the soft manipulator, a neural network-based inverse kinematics framework is developed to establish an efficient mapping from desired end poses to tendon actuation lengths. An iterative learning control strategy is further integrated to compensate for material hysteresis, friction, and external disturbances. A prototype is fabricated using flexible 3D-printable material (TPU), and comprehensive experiments are conducted to validate extensible performance, point positioning accuracy, trajectory-tracking accuracy, and compliant interaction capability. The results demonstrate that the proposed wave spring-based soft manipulator achieves a high extension ratio and reliable control precision. Full article

Electric discharge machining (EDM) has a unique ability to accurately cut exotic, hard-to-cut materials such as titanium without physical contact, with negligible force and vibration. Such a characteristic makes it a promising machining technique to be combined with robot manipulators to maximise the flexibility of the working envelope. Such a combination enabled robots to make a more sustainable cut of large, monolithic, and complex workpieces used in relevant defence and aerospace industries. The concept has been proven through a feasibility study prototype using wire EDM, followed by rotational milling EDM configurations. The wire EDM configuration was challenging due to instability in the wire control system and tension. The milling EDM configuration has been proven successful for intricate geometries. However, it involves removing large amounts of material and is thus not ideal for large geometries or deep cuts. Thus, to further explore sustainable robotic EDM for large workpieces without vaporising significant amounts of scarce, exotic, hard-to-cut materials, new inventive tools are needed. Therefore, this research aims to present a new knife EDM (KEDM) end-effector concept as a pure simulation capable of making large, deep cuts on a titanium workpiece without interruption. Using the TRIZ algorithm, engineering constraints are overcome to propose a KEDM design that vibrates and operates like a large WEDM, without frequent wire breakage, setup, or restarts. This research further explores the proposed end-effector through a digital twin kinematic simulation to find and demonstrate the extent of the machined workpiece and the robot’s enlarged workspace. Full article

The present work is aimed at developing and researching a robotic arm with five degrees of freedom, manufactured using 3D-printing technology and controlled by an ESP32 microcontroller. This technology is increasingly used in robotics, especially in the educational process, due to the possibilities for rapid prototyping, modification and restoration of individual components. In the development process, the mechanical, hardware and software parts of the system were implemented, and a basic kinematic analysis of the manipulator was performed. A control program was created, allowing the performance of “pick and place” tasks, as well as visualization and manual control through a developed application. The results obtained show that the developed system provides sufficient functionality and flexibility for use in robotics training, while at the same time allowing expansion and upgrading with additional functionalities. The main contribution of the work lies in the implementation of an accessible and adaptable robotic platform, suitable for educational and experimental purposes. Full article

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing. Full article

When a robot performs a grinding operation, the steady-state force/position tracking accuracy of its end-effector is critical to achieving high grinding quality. To solve this problem, a fuzzy adaptive impedance method is incorporated into the robot’s compliant control framework. Firstly, the robot dynamics model is established based on the Newton–Euler method. To describe the robot dynamics more comprehensively, a linear friction compensation model is also introduced. Secondly, a dynamic feedforward trajectory-tracking controller is proposed based on the dynamic model, and its stability is verified using a Lyapunov function. The impedance parameters are adjusted in real time according to the feedback contact force and its rate of change, thereby enabling dynamic equilibrium between the end contact force and end position. This allows the robot end-effector to exhibit compliance during external environmental interactions. Finally, a control platform of a force/position compliance controller was constructed, and two grinding conditions of plane and arc were designed to validate the effectiveness of force/position compliance control based on impedance control. Compared with the fixed impedance approach, the proposed method reduces overshoot by 11.6% (plane) and 12.45% (arc), improves surface roughness from Ra 0.042 μm to Ra 0.021 μm, and achieves faster force tracking with fewer oscillations. Full article

The contradiction between high compliance and low load-bearing capacity of flexible manipulators limits their engineering applications. Meanwhile, the theoretical modeling of the deformation and variable stiffness characteristics of flexible joints still faces considerable challenges. This paper proposes a positive-pressure double-airbag gap-constrained particle-jamming variable stiffness method and develops a novel actively variable stiffness pneumatic flexible bending joint with an integrated configuration of actuator, variable stiffness device (VSD), and primary structure. Based on classical elasticity theory and Coulomb–Amontons’ law of friction, theoretical models for the bending angle and tangential stiffness are established and verified through prototype experiments. With VSD activation, the joint reaches a bending angle of 56.35° at 0.4 MPa. At 40° forward bending, VSD activation increases the tangential stiffness from 0.167 N/mm to 0.832 N/mm, with the stiffness ratio between 40° and 0° increasing from 1.56 without VSD to 4.80 with VSD activation. Model predictions agree well with experimental data, yielding mean relative errors of 6.77% for the bending-angle model with VSD and 6.76% for the forward tangential-stiffness model with VSD activation. A coupling effect between bending deformation and stiffness is observed. The results demonstrate that the proposed joint achieves substantial stiffness regulation, providing a basis for its application in flexible robotic systems. Full article

Conventional cooled infrared optical systems employ a cold stop, which disrupts optical-path symmetry and constrains exit-pupil matching. Consequently, reducing the refractive lens count increases the residual broadband-aberration burden, motivating the introduction of an ultrathin phase-compensation element near the exit pupil. Conventional solutions therefore tend to use complex optical configurations with large volume and high weight, making it challenging to meet the demands of modern lightweight and compact detection systems. Metalenses offer a new approach for aberration correction through the flexible phase manipulation enabled by their unit cells. However, severe chromatic dispersion of metalenses under broadband conditions remains a major obstacle to their practical application. To address this issue, a hybrid refractive–metalens design method for cooled infrared optical systems is proposed. Based on the distinctive phase distribution characteristics of metalenses, an achromatic theoretical formulation applicable to broadband infrared wavelengths is derived. Guided by this theory, a cooled mid-wave infrared refractive–metalens hybrid optical system is designed, featuring a full field of view of 126°, an F-number of 2, and an operating wavelength band of 3.3–5 μm. In comparison with a conventional eight-element refractive system of identical specifications, the proposed hybrid system reduces the total optical-element count from eight to four and achieves reductions of 21% in total track length and 79% in system weight, while maintaining a full-field polychromatic MTF above 0.4 at 33 lp/mm. In addition, the narcissus effect is effectively mitigated under the modeled conditions. This approach enables high-performance aberration correction using metalenses while offering a new design paradigm for simplified infrared optical systems. Full article

Modern manufacturing faces increasing demands for flexibility, customization, and productivity under dynamic conditions. Multi-robot systems offer a promising solution by enabling cooperative execution of complex tasks, such as assembly and cooperative manipulation. In this context, Multi-Agent Reinforcement Learning (MARL) has emerged as a promising paradigm to enhance coordination and adaptability in industrial settings. MARL enables multiple agents to learn and interact in shared environments to achieve common goals within complex and dynamic industrial processes. In this paper, a deep analysis of MARL applied to industrial multi-robot systems based on a systematic review is presented, with particular focus on cooperative manipulation tasks. Following PRISMA guidelines, we analyze a total of 30 articles published between 2016 and 2026, selected independently by two of the authors from an initial pool of 102 records retrieved from Scopus and Web of Science. These articles were used to address five key questions regarding MARL algorithms, control architectures, industrial applications and validation practices. These research questions seek to examine gaps and trends at the research level which are important for the development of multi-agent control technologies. This review shows a clear prevalence of model-free algorithms under Centralized Training with Decentralized Execution (CTDE) architectures, with validation mainly performed in simulation. Despite promising results and high potential for impact, critical gaps remain in scalability, reproducibility, and sim-to-real transfer, limiting real deployment in manufacturing environments. To address these challenges and fill current gaps, we outline actionable research directions, such as hybrid MARL approaches, standardized industrial benchmarks, digital twin pipelines, and safety-aware deployment strategies, to accelerate MARL adoption in industrial environments. Full article

Using cable transmission in underwater manipulators helps to reduce the mass and rotational inertia of distal moving components, but the control performance of cable-driven joints is affected by flexible cable transmission, equivalent joint-side friction, hydrodynamic effects, and external disturbances. This paper proposes a control method combining time-delay estimation (TDE) with gain-scheduled sliding mode control (GSMC) for a cable-driven joint of an underwater manipulator. TDE uses delayed control-input and joint-acceleration data to estimate and compensate for the lumped dynamic term in the equivalent joint model online. GSMC employs a composite sliding surface and an error-dependent gain-scheduling mechanism to suppress trajectory-tracking errors in the presence of the TDE estimation residual. In joint-level MATLAB/Simulink R2024b simulations, smooth-step, sinusoidal-trajectory-tracking, and ablation results under predefined combined-uncertainty conditions, together with the results of 50 paired Monte Carlo runs, show that TDE-GSMC achieves the lowest major tracking-error indices among the four methods for the smooth-step and $0.35\mathrm{Hz}$ sinusoidal trajectories and also yields the lowest mean tracking error and 95th percentile of the disturbance peak in the Monte Carlo simulations; the ablation results further characterize the performance differences among the tested controller configurations. Full article

ε-phase indium selenide (ε-InSe), a non-centrosymmetric van der Waals layered semiconductor, exhibits broken inversion symmetry in all layer numbers, giving rise to exceptional second-order nonlinear optical responses and holding great promise for nonlinear optoelectronic applications. The dynamic control of the nonlinear efficiency of ε-InSe is crucial for its engineering applications. However, the quantitative manipulation of second-harmonic generation (SHG) intensity and crystal symmetry in few-layer ε-InSe via strain engineering is still lacking. In this work, we systematically investigate the modulation of SHG intensity and angle-resolved SHG patterns in few-layer ε-InSe under uniaxial tensile strain. Using a home-built straining apparatus, we apply controlled tensile strain and measure the strain-dependent SHG responses. The experimental results demonstrate that the SHG intensity of few-layer ε-InSe shows a non-monotonic response to increasing tensile strain, first increasing and then decreasing. Concurrently, the sixfold symmetry of the SHG pattern is broken, confirming the significant strain-induced modulation of the lattice symmetry. This study provides a viable route for the design of flexible and tunable nonlinear optoelectronic devices based on ε-InSe. Full article

Redundant manipulators offer greater flexibility in executing complex trajectory-tracking tasks in industrial applications, owing to their additional degrees of freedom (DOFs). However, their inverse kinematics (IK) remains computationally expensive, limiting their practical application. By combining the joint parameterization method (JPM) and the stationary point solver (SPS), the JPSP-IK framework is proposed to provide closed-form joint solutions while significantly reducing the computational burden. JPM treats the redundant variables as free parameters and analytically reconstructs the remaining joints in closed form from these variables and the target end-effector pose, thereby reducing the original full-space IK problem to a low-dimensional redundancy-resolution problem. On this basis, the SPS is developed to efficiently determine the redundant variables with respect to the secondary objective, considering joint-limit avoidance and motion smoothness, and the complete joint solution is analytically reconstructed through the analytical mapping derived by the JPM. Validation and comparative experiments demonstrate that JPSP-IK achieves substantially lower computation times than representative full-space and JPM-based IK methods. Full article

Optical bistability (OB) with low threshold and high tunability is crucial for advanced photonic devices. This work theoretically investigates low-threshold and tunable OB in a one-dimensional photonic crystal Fabry-Pérot (FP) cavity embedded with a Weyl semimetal (WSM) layer. By combining the local field enhancement of the cavity and the large third-order nonlinear refractive index of the WSM, we achieve OB in the terahertz regime with a low threshold of ~10⁵ V/m. Furthermore, the OB threshold and hysteresis width can be flexibly manipulated via the Fermi energy of WSM, incident angle, photonic crystal period, the refractive index of the dielectric inside the cavity, and the position of the WSM inside the FP cavity. Moreover, based on the high sensitivity of switching thresholds to refractive index and displacement variations, we also propose a dual-parameter sensing scheme. This exhibits excellent sensing performance for gas sensing (sensitivity up to 96.48 × 10⁵ V/m·RIU) and high sensitivity for displacement sensing (0.91 × 10⁶ V/m·μm). We believe that the above results can provide reference schemes for nonlinear photonic devices. Full article

To address the evaluation requirements for agricultural robotic harvesting, this work presented a fruit-grasping assessment system based on tactile fusion analysis. Four piezoresistive pressure sensors were symmetrically integrated into the inner surfaces of a flexible gripper. A signal-conditioning circuit and a data acquisition module transmitted tactile signals to a Transformer–Mamba fusion network for feature extraction and target classification. After being trained on a dataset comprising 300 samples, the model extracted deep tactile features to distinguish among three target categories: citrus fruits, branches, and leaves. Classification outputs generated control commands for a robotic manipulator, enabling obstacle-avoidance retraction and precise harvesting operations. Experimental evaluations, conducted in both indoor and outdoor environments, demonstrated a target recognition accuracy of 93.12%. The manipulator response time was below 0.5 s, and the operational success rate was 90%. The proposed sensing system and algorithmic framework showed strong adaptability and supported quantitative assessment of grasping performance. The fruit detachment, compression damage, and plant-collision risks were effectively reduced while operational stability and harvesting efficiency improved. Full article

The demand for robotic manipulators has increased because of their precision, speed, and cost-efficiency in complex or hazardous tasks. Flexible robotic manipulators, unlike rigid ones, offer lower mass and energy consumption, enabling advanced applications across various fields. Despite these advantages, the mass reduction of these manipulators can lead to undesired effects, including decreased precision, increased sensitivity to parametric uncertainties, coupled dynamics, and increased oscillations caused by their inherent flexibility. Moreover, the modeling complexity of such mechanical systems represents a significant challenge, since multiple degrees of freedom must be considered. In this study, a physics-informed neural network (PINN) is designed to estimate the dynamic behavior of a flexible-link manipulator. First, a dataset is created by executing different trajectories (i.e., different rotation angles) of the flexible manipulator. Based on the dataset, the PINN is then trained using time and strain signals as inputs to estimate the angular displacement, combining a data-driven loss with a physics-based loss derived from the system’s dynamic model. Finally, the PINN model is investigated experimentally to assess the closed-loop strategy and evaluate its efficiency and reproducibility in recognizing the mechanical system behavior. Therefore, the results show that the PINN and closed-loop experimental validations are consistent with the proposed method. Full article