Search Results (56)

In this paper, an engineering model (EM) of a multi-joint space gripper for on-orbit servicing (OOS) is proposed. OOS missions demand robotic systems capable of reliable physical interactions under dynamic uncertainties and harsh space environments. While prior space-qualified grippers have demonstrated dexterous manipulation through anthropomorphic, high-DoF configurations, this work adopts a design direction widely established in industrial applications: a three-finger, lower-DoF configuration that balances grasp versatility, structural simplicity, and system integration for OOS missions. The developed gripper features a tendon-driven mechanism with a structural design optimized for space-environment compatibility and mechanical compliance. The kinematic characteristics of the mechanism are analyzed, while workspace and manipulability analyses are conducted to evaluate its operational limits. To verify the functional feasibility of the proposed design, representative grasping experiments were performed using a fabricated EM. The mechanical reliability and grasping performance were evaluated through a series of empirical experiments. The results indicate that the proposed design achieves a practical balance among grasp versatility, structural simplicity, and system integration for OOS missions, with a shielding-oriented structural configuration adopted as a design baseline. Its functional feasibility is supported by kinematic analysis, repeatability verification, and grasping experiments. This study provides a basis for the design and evaluation of three-finger robotic grippers in future OOS missions. Full article

Thumb degree-of-freedom (DOF) allocation in anthropomorphic robot hands involves a trade-off between functional mobility and mechanical-control complexity. This study presents a controlled multi-metric framework for comparing recurring thumb DOF configurations under common palm geometry, non-thumb finger structure, reference frames, Denavit–Hartenberg kinematics, and sampling assumptions. Five literature-derived thumb configurations, namely 3-1-1, 2-2-1, 2-1-1, 2-0-1, and 1-1-1, were evaluated to determine which thumb DOFs should be preserved when kinematic complexity is reduced. The theoretical evaluation included Kapandji Opposition Test reachability, opposition alignment, workspace volume, workspace compactness, cylindrical grasp opportunity, and Jacobian-based dexterity. A targeted experimental validation of the 2-1-1 and 2-0-1 prototypes was then performed on a tendon-driven test bench. The results showed that qualitatively similar thumb configurations are quantitatively unequal: several designs achieved identical Kapandji scores but differed substantially in workspace, alignment, dexterity, and grasp feasibility. Overall, 3-1-1 achieved the strongest overall capability, while 2-2-1 emerged as the strongest reduced-complexity alternative and achieved the best mean dexterity. Retaining two active carpometacarpal DOFs preserved a large share of dexterous function, whereas metacarpophalangeal fixation maintained selected cylindrical grasps but narrowed the feasible task boundary. Full article

The multi-finger robotic hand offers great potential for precise control due to its high degrees of freedom. Yet, manipulating objects forms a closed-chain kinematic system, which compounds the dimensionality and computational complexity of trajectory tracking. To tackle this challenge, and inspired by the widespread application of the zeroing neurodynamics (ZND) in robotic control, this study proposes a novel direct-discrete robust neurodynamics (DDRN) algorithm. The proposed algorithm advances the ZND methodology by employing a direct discretization design strategy. This strategy is crucial for two reasons. First, it fits naturally with the discrete-time nature of digital systems, enabling practical implementation. Second, it enhances precision by avoiding the integration errors inherent in continuous-to-discrete transformations. By simultaneously integrating this direct discretization with explicit noise suppression mechanisms, the DDRN algorithm efficiently solves the high-dimensional tracking problem formulated as a constrained time-varying quadratic programming (CTVQP) problem. Theoretical analyses demonstrate that under various noise environments, the steady-state residuals (SSRs) achieve global convergence, guaranteeing the algorithm’s strong robustness and high accuracy. Furthermore, comprehensive numerical simulations substantiate its superior performance. Practically, this DDRN algorithm enables more reliable and precise real-time control of dexterous robotic hands, with potential benefits for advanced manufacturing, prosthetic hands, and automated assembly where accurate trajectory tracking under sensor noise is critical. Full article

As critical end-effectors enabling the practical deployment of marine robotic systems, soft hands face persistent challenges including multi-finger asynchronization, unbalanced force distribution, and insufficient anti-disturbance robustness, compounded by constraints from soft material nonlinearity and harsh marine environmental disturbances. To address these limitations, this paper proposes a dexterous grasping method integrating coupled dynamical systems and adaptive force planning control, designed to enhance operational reliability in complex marine environments. An intermediate dynamic layer is embedded to ensure precise multi-finger synchronization, a hybrid force planning algorithm balances force uniformity and constraint satisfaction, and an adaptive controller synergizes with a Neo-Hookean model to compensate for nonlinear deviations. Simulations and physical experiments demonstrate that the method delivers excellent grasping stability and accuracy for uneven mass distribution targets such as cylinders and spheres, while balancing synchronization precision, constraint compliance, and anti-disturbance capability. Compared with the traditional coupled dynamical systems (DSs), the constraint violation is reduced by up to 18.2%, the friction force is increased by 4.0%, and the force distribution uniformity is improved by approximately 5.1%.Compared with the particle swarm optimization (PSO) strategy, the constraint violation is reduced by up to 50.5%, the friction force is increased by 40.9%, and the force distribution uniformity is also improved by about 5.1%. This work fills a key gap in balancing multiple performance metrics for marine soft hands, providing a reliable technical solution to accelerate the real-world deployment of marine robotic systems. Full article

Traditional dexterous hands can readily grasp objects but face limitations in dexterous manipulation due to complex control systems and high actuation demands. This paper presents a novel dexterous hand designed to address these challenges. The hand consists of four fingers, each equipped with two mecanum wheels at the fingertips to allow for the omnidirectional manipulation of objects. Continuous rotation of the mecanum wheels enables unbounded motion of grasped objects without the need for finger gaiting. Object pose adjustment is achieved by controlling the rotation of mecanum wheels, thus significantly reducing operational complexity and enhancing manipulative agility. Furthermore, to address the control difficulty of multi-finger coordinated motion, a four-finger coupled mechanism is implemented, resulting in a dexterous hand with three degrees of freedom. Kinematic models of omnidirectional manipulation are established for typical geometric objects, including a flat plate, a cuboid, a sphere, and a cylinder. Simulations confirm the correctness of the kinematic models. Experimental results show that the hand can achieve omnidirectional manipulation of objects. Finally, the extended functionality of the dexterous hand is briefly presented, which allows it to be reconfigured into an omnidirectional mobile robot. Full article

Dexterous hands are the core end-effectors of humanoid robots, and their design is a key research focus in this field. With multiple independent finger units, the units’ dexterity directly determines the hand’s operational performance, yet achieving three-degree-of-freedom (3-DOF) anthropomorphic motion remains a key design challenge. To address this, this paper proposes a hybrid-driven index finger unit: combining linkage and tendon–cable drive advantages to realize 3-DOF anthropomorphic motion, and adopting independent drive/transmission modules to simplify manufacturing and boost parameter optimization flexibility. Validated via motion dynamics, DOF, and operational force assessments, this design offers key unit tech for dexterous hand development and serves as a reference for optimizing multi-DOF anthropomorphic finger designs. Full article

Designing a mechanical gripper, achieving the combined capabilities of high loading capacity, flexible environmental adaptability, and dexterous kinematic performance, is highly desired in human–machine interaction and industrial production efficiency improvement, yet this combination of grasping encounters irreconcilable challenges. Although rigid–flexible coupled mechanical grippers exhibit promising advantages compared with conventional rigid mechanical grippers and pure soft grippers, they still get stuck in problems of grasping stability owing to the mechanical mismatch between rigid and flexible materials. Inspired by the hybrid structure of the human finger, we designed an underactuated rigid–flexible coupled mechanical gripper (U-RFCG) to expand the grasping range of existing mechanical grippers. We utilized an embedded flexible microcolumn array to couple the rigid underactuated fingers with a flexible silicone rubber finger segment and integrated a flexible silicone rubber cavity into each rigid–flexible coupling finger segment, thereby addressing issues such as slippage and fracture at the coupling interface of the rigid–flexible structure. This design enables the mechanical gripper to possess the superior characteristics of both rigid and flexible grippers, along with simple execution control. We established mathematical models to analyze the static and kinematic properties of the fingers. Based on these models, we optimized the dimensional parameters of the underactuated links to ensure reasonable contact force distribution and stable motion. Repeated experiments demonstrated that the contact force exerted by each phalanx consistently stabilized at approximately 3.58 N during operation. Lastly, we integrated the U-RFCG into a 3D motion platform. Our mechanical gripper demonstrates significant adaptability and high load capacity for grasping various objects, including irregular cauliflowers, fragile fried instant noodles, and heavy cabbages. It successfully handled objects spanning a weight range of 30–1500 g without causing damage to them. These results confirm that our design balances load capacity and grasping safety through the synergy of rigid and flexible properties, providing a new solution for robotic grasping in complex scenarios. Full article

The control of tendon-driven robotic fingers presents significant challenges due to their inherent underactuation, coupled with complex non-linear dynamics arising from tendon elasticity, friction, and external disturbances. Therefore, achieving precise control of finger motion and contact interactions necessitates advanced modeling, estimation, and control strategies capable of addressing uncertainties in tendon tension, routing, and elasticity. This paper presents a comprehensive comparative study of three distinct control paradigms: feedback linearization with Proportional-Derivative (FBL-PD) control, feedback linearization with super-twisting sliding-mode algorithm (FBL-STA), and deep-deterministic reinforcement learning (DDPG-RL), for the precise trajectory tracking of a three-link tendon-driven robotic finger. Through extensive simulations, the performance of each controller is rigorously evaluated based on trajectory-tracking accuracy and robustness to varying disturbances. The results indicate that under disturbance-free conditions, the FBL-PD and FBL-STA controllers, when properly tuned, achieve precise tracking of the reference trajectory; however, they produce noticeably noisy control signals. When subjected to external disturbances, these controllers exhibit increased sensitivity, producing even noisier responses. In contrast, the DDPG-RL maintains smooth control dynamics and achieves sufficiently accurate tracking in both scenarios. This comparative analysis elucidates the strengths and weaknesses of each control strategy, offering critical insights and practical guidelines for the design and implementation of advanced control systems for dexterous tendon-driven robotic fingers. Full article

This work examines the robotic technique of ungrasping, in which an object held by a gripper is intentionally released into the environment. The proposed method achieves controlled object release through non-static contact interactions that permit rolling and sliding. This form of dexterous manipulation is especially important for thin or slender objects, as demonstrated through physical experiments. This study first addresses how three-dimensional stability can be established using a minimal number of contacts. It then introduces a planning framework for three-dimensional ungrasping that extends our prior planar formulation. Experimental results obtained with a two-fingered gripper—the most common gripper type—validate the feasibility, effectiveness, and practicality of the presented approach. Full article

This paper presents tilt-and-pivot manipulation, a robotic technique for picking large, thin objects resting on hard supporting surfaces. The method employs in-hand dexterous manipulation by reorienting the gripper around the object’s contact point, allowing a finger to enter the gap between the object and the surface, without requiring relative sliding at the contact. This finally facilitates reliable pinch grasps on the object’s faces. We investigate the kinematic principles and planning strategies underlying tilt-and-pivot, discuss effector design considerations, and highlight the practical advantages of the strategy, which is applicable to a variety of low-profile objects. Experimental results, incorporating vision and force–torque sensing, demonstrate its effectiveness in bin-picking scenarios and its applicability to more complex object-handling tasks. Full article

This study presents the design and evaluation of a dexterous prosthetic hand featuring five fingers, ten independently actuated joints, and four passively driven joints. The hand’s dexterity is enabled by a novel rigid–flexible coupled finger mechanism that incorporates a 1-active–1-passive joint configuration, which can enhance the dexterity of traditional rigid actuators while achieving a human-like workspace. Each finger is designed with a specific degree of rotational freedom to mimic natural opening and closing motions. This study also elaborates on the mapping of eight-channel electromyography to finger grasping force through improved TCN, as well as the control algorithm for grasping flexible objects. A functional prototype of the prosthetic hand was fabricated, and a series of experiments involving adaptive grasping and handheld manipulation tasks were conducted to validate the effectiveness of the proposed mechanical structure and control strategy. The results demonstrate that the hand can stably grasp flexible objects of various shapes and sizes. This work provides a practical solution for prosthetic hand design, offering promising potential for developing lightweight, dexterous, and highly anthropomorphic robotic hands suitable for real-world applications. Full article

The sensory glove (also known as data or instrumented glove) plays a key role in measuring and tracking hand dexterity. It has been adopted in a variety of different domains, including medical, robotics, virtual reality, and human–computer interaction, to assess hand motor skills and to improve control accuracy. However, no particular technology has been established as the most suitable for all domains, so that different sensory gloves have been developed, adopting different sensors mainly based on optic, electric, magnetic, or mechanical properties. This work investigates the performances of the MANUS Quantum sensory glove that sources an electromagnetic field and measures its changing value at the fingertips during fingers’ flexion. Its performance is determined in terms of measurement repeatability, reproducibility, and reliability during both quasi-static and dynamic hand motor tests. Full article

Anthropomorphic dexterous hands are crucial for robotic interaction in unstructured environments, yet their performance is often constrained by traditional actuation systems, which suffer from excessive weight, complexity, and limited compliance. Twisted String Actuators (TSAs) offer a promising alternative due to their high transmission ratio, lightweight design, and inherent compliance. However, their strong nonlinearity under variable loads poses significant challenges for high-precision control. This study presents an integrated approach combining data-driven modeling and biomimetic mechanism innovation to overcome these limitations. First, a data-driven modeling approach based on a dual hidden-layer Back Propagation Neural Network (BPNN) is proposed to predict TSA displacement under variable loads (0.1–4.2 kg) with high accuracy. Second, a lightweight, underactuated five-finger dexterous hand is developed, featuring a biomimetic three-phalanx structure and a tendon-spring transmission mechanism, achieving an ultra-lightweight design. Finally, a comprehensive experimental platform validates the system’s performance, demonstrating precise bending angle prediction (via integrated BPNN–kinematic modeling), versatile gesture replication, and robust grasping capabilities (with a maximum fingertip force of 7.4 N). This work not only advances TSA modeling for variable-load applications but also provides a new paradigm for designing high-performance, lightweight dexterous hands in robotics. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article

Tactile sensation across the whole hand, including the fingers and palm, is essential for manipulation and, therefore, is expected to be similarly useful for enabling dexterous robot manipulation. Tactile sensation would ideally be distributed (over large surface areas), have a high precision, and provide measurements in multiple axes, allowing for effective manipulation and interaction with objects of varying shapes, textures, friction, and compliance. Given the complex geometries and articulation of state-of-the-art robotic grippers and hands, they would benefit greatly from their surface being instrumented with a thin, curved, and/or flexible tactile sensor technology. However, the majority of current sensor technologies measure tactile information across a planar sensing surface or instrument-curved skin using relatively bulky camera-based approaches; proportionally in the literature, thin and flexible tactile sensor arrays are an under-explored topic. This paper, presents a thin, flexible, non-camera-based optical tactile sensor design as an investigation into the feasibility of adapting our novel LiVec sensing principle to curved and flexible surfaces. To implement the flexible sensor, flexible PCB technology is utilized in combination with other soft components. This proof-of-concept design eliminates rigid circuit boards, creating a sensor capable of providing localized 3D force and 3D displacement measurements across an array of sensing units in a small-thickness, non-camera-based optical tactile sensor skin covering a curved surface. The sensor consists of 16 sensing units arranged in a uniform 4 × 4 grid with an overall size of 30 mm × 30 mm × 7.2 mm in length, width, and depth, respectively. The sensor successfully estimated local XYZ forces and displacements in a curved configuration across all sixteen sensing units, the average force bias values ($\overline{\mu }$) were −1.04 mN, −0.32 mN, and −1.31 mN, and the average precision ($\overline{SD}$) was 54.49 mN, 55.16 mN and 97.15 mN, for the X, Y, Z axes, respectively, the average displacement bias values ($\overline{\mu }$) were 1.58 μm, 0.29 μm, and −1.99 μm, and the average precision values ($\overline{SD}$) were 221.61 μm, 247.74 μm, and 44.93 μm for the X, Y, and Z axes, respectively. This work provides crucial insights into the design and calibration of future curved LiVec sensors for robotic fingers and palms, making it highly suitable for enhancing dexterous robotic manipulation in complex, real-world environments. Full article