Search Results (139)

Nowadays, grippers are extensively employed to interact with dynamic and variable objects. Therefore, enhancing the adaptability of grippers is crucial for improving production efficiency and product quality. To address the trade-off between load capacity and interaction safety in rigid and soft grippers, this paper proposes a rigid–flexible coupling gripper with high grasping adaptability based on an underactuated structure. We conduct static analysis on the underactuated mechanism, followed by dimensional optimization using a genetic algorithm. After optimization, the grasping force error at each knuckle is reduced to 2 N, and the total grasping force reaches 38 N. The soft actuators, integrated with a rigid framework, not only increase the contact area during grasping but also mitigate the excessive concentration of contact forces, significantly improving the compliance of the gripper. Additionally, to tackle the issue of weak interfacial bonding strength caused by rigidity mismatch between rigid components and soft materials, this paper proposes a novel method of applying embedded microstructures to enhance the interfacial toughness of rigid–flexible coupling. The elastic deformation of these microstructures ensures strong interfacial connection strength both under tensile and shear stresses. Lastly, a robotic grasping platform is developed to carry out diverse grasping experiments. Experimental results show that the underactuated linkage mechanism and the flexible structure can collaboratively adjust grasping strategies when handling objects of various types, enabling stable manipulation while preventing object damage. This design effectively expands the operational applicability of the gripper. 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

Several types of self-balancing vehicles have been successfully developed and commercialized in the past two decades, both as manned vehicles and as autonomous mobile robots. At the same time, due to their characteristic instability and underactuation, a large body of research has been devoted to their control. However, despite this practical and theoretical interest, the current publicly available literature does not cover their systematic design and development. In particular, overall processes that lead to a finished vehicle starting from a set of requirements and specifications have not been examined in the literature. Within this context, this paper contributes a comprehensive mechatronic, dynamics-based procedure for the design of this class of vehicles; to promote clarity of exposition, the procedure is systematically presented using Model-Based Systems Engineering tools and principles. In particular, the proposed design method is developed and formalized starting from an original description of the vehicle, which is treated as a complex system composed of several interconnected multi-domain components that exchange power and logical flows through suitable interfaces. A key focus of this work is the analysis of these exchanges, with the goal of defining a minimal set of quantities that should be necessarily considered to properly design the vehicle. As a salient result, the design process is organized in a logical sequence of steps, each having well-defined inputs and outputs. The procedure is also graphically outlined using standardized formalisms. The design method is shown to cover all the mechanical, electrical, actuation, measurement and control components of the system, and to allow the unified treatment of a large variety of different vehicle variants. The procedure is then applied to a specific case study, with the goal of developing the detailed design of a full-scale vehicle. The main strengths of the proposed approach are then widely highlighted and discussed. Full article

This paper delves into the potential of an optimized differential system within an underactuated tendon-driven soft robotic gripper, a crucial component that enhances the grasping abilities by allowing fingers to secure objects adapting to different shapes and geometries. The original version of the differential system exhibited a certain degree of deformability, which introduced some functional advantages. In particular, its flexibility allowed for more delicate grasping operations by acting as a force reducer and enabling a more gradual application of contact forces, an essential feature when handling fragile objects. Nonetheless, while these benefits are noteworthy, a rigid differential remains more effective for achieving firm and secure grasps. The primary goal of this study is to analyze the differential’s performance through FEM simulations and deformation experiments, assessing its structural behavior under various conditions. Additionally, the research explores an innovative differential geometry aimed at striking the ideal balance, ensuring a robust grasp while retaining a controlled degree of deformability. By refining the differential’s design, this study seeks to enhance the efficiency of underactuated soft robotic grippers, ultimately enhancing their capabilities in handling diverse objects ensuring a compliant and secure grasp with optimized efficiency. Full article

Two-wheeled self-balancing robots (TWSBRs) are underactuated, inherently nonlinear systems that exhibit unstable dynamics. Due to their structural simplicity and rich control challenges, TWSBRs have become a standard platform for validating and benchmarking various control algorithms. This paper presents a comprehensive and structured review of control strategies applied to TWSBRs, encompassing classical linear approaches such as PID and LQR, modern nonlinear methods including sliding mode control (SMC), model predictive control (MPC), and intelligent techniques such as fuzzy logic, neural networks, and reinforcement learning. Additionally, supporting techniques such as state estimation, observer design, and filtering are discussed in the context of their importance to control implementation. The evolution of control theory is analyzed, and a detailed taxonomy is proposed to classify existing works. Notably, a comparative analysis section is included, offering practical guidelines for selecting suitable control strategies based on system complexity, computational resources, and robustness requirements. This review aims to support both academic research and real-world applications by summarizing key methodologies, identifying open challenges, and highlighting promising directions for future development. Full article

Underactuated robotic systems are appealing for industrial use due to their reduced actuator number, which lowers energy consumption and system complexity. Underactuated systems are, however, often affected by residual vibrations. This paper addresses the challenge of generating energy-optimal trajectories while imposing theoretical null residual (and yet practical low) vibration in underactuated systems. The trajectory planning problem is cast as a constrained optimal control problem (OCP) for a two-degree-of-freedom revolute–revolute planar manipulator. The proposed method produces energy-efficient motion while limiting residual vibrations under motor torque limitations. Experiments compare the proposed trajectories to input shaping techniques (ZV, ZVD, NZV, NZVD). Results show energy savings that range from 12% to 69% with comparable and negligible residual oscillations. Full article

A modified dual hierarchical terminal sliding mode control (MDHTSMC) strategy is developed in this study for the control of a two-wheeled self-balancing robot (TWSBR). The control framework incorporates individually designed sliding surfaces within a structured dual-layer hierarchy, enabling explicit prediction of convergence time. To overcome the system’s underactuation characteristics, a hierarchical structure is embedded into the dual terminal sliding mode control law. Additionally, the proposed approach mitigates the chattering effect and enhances the system’s self-balancing capabilities. Numerical simulations were conducted to verify the controller’s effectiveness and to confirm the theoretical results. Full article

This paper introduces a robust neural adaptive MIMO control strategy to improve the stability and adaptability of bipedal locomotion amid uncertainties and external disturbances. The control combines nonlinear dynamic inversion, finite-time convergence, and radial basis function (RBF) neural networks for fast, accurate trajectory tracking. The main novelty of the presented control strategy lies in unifying instantaneous feedback, real-time learning, and dynamic adaptation within a multivariable feedback framework, delivering superior robustness, precision, and real-time performance under extreme conditions. The control scheme is implemented on a 5-DOF underactuated $\mathrm{R}\mathrm{A}\mathrm{B}\mathrm{B}\mathrm{I}\mathrm{T}$ robot using a $\mathrm{d}\mathrm{S}\mathrm{P}\mathrm{A}\mathrm{C}\mathrm{E}\mathrm{D}\mathrm{S}1103$ platform with a sampling rate of $∆t=1.5 \mathrm{m}\mathrm{s} (667 \mathrm{H}\mathrm{z})$. The experimental results show excellent performance with the following: The robot achieved stable cyclic gaits while keeping the tracking error within $e=\pm 0.04 \mathrm{r}\mathrm{a}\mathrm{d}$ under nominal conditions. Under severe uncertainties of trunk mass variations ${∆m}_{\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{n}\mathrm{k}}=+100\%$, limb inertia changes ${∆I}_{\mathrm{l}\mathrm{i}\mathrm{m}\mathrm{b}}=\pm 30\%$, and actuator torque saturation at $\tau =\pm 150 \mathrm{N}\mathrm{m}$, the robot maintains stable limit cycles with smooth control. The performance of the proposed controller is compared with classical nonlinear decoupling, non-adaptive finite-time, neural-fuzzy learning, and deep learning controls. The results demonstrate that the proposed method outperforms the four benchmark strategies, achieving the lowest errors and fastest convergence with the following: $\mathrm{I}\mathrm{A}\mathrm{E}=1.36$, $\mathrm{I}\mathrm{T}\mathrm{A}\mathrm{E}=2.43$, $\mathrm{I}\mathrm{S}\mathrm{E}=0.68$, $t_{ss}=1.24 \mathrm{s}$, and $M_p=2.21\%$. These results demonstrate evidence of high stability, rapid convergence, and robustness to disturbances and foot-slip. Full article

Planar underactuated robots are mainly applied in the microgravity field, such as deep sea and deep space. The system modeling and stability control of planar underactuated robots are prerequisites to ensure successful task completion. The planar prismatic–prismatic–rotational (PPR) underactuated robot is one type of planar underactuated robot structure. In this paper, the dynamic system model of planar PPR underactuated robots is built, and the switching-control strategy is designed. In the first phase, an improved PD controller based on the linkage coupling of the PPR model is designed to adjust the state quantities of the three linkages and stabilize the first two linkages to the target state. This controller has certain robustness and rapidity. In the second phase, the PPR model is downgraded to the PR model, an open-loop iterative controller is designed, and the third underactuated link is stabilized to the target state through oscillation convergence. Finally, the effectiveness and applicability of the proposed strategy were verified through the comparison of setting torque interference and the simulation of the initial velocity of the link. Full article

Soft anthropomorphic robotic grippers are attractive because of their inherent compliance, allowing them to adapt to the shape of grasped objects and the overload protection needed for safe human–robot interaction or gripping delicate objects with sophisticated control. The anthropomorphic design allows the gripper to benefit from the biological evolution of the human hand to create a multi-functional robotic end effector. Entirely soft grippers could be more efficient because they yield under high loads. A trending solution is a hybrid gripper combining soft and rigid elements. This work describes a prototype of an anthropomorphic, underactuated five-finger gripper with a direct pneumatic drive from soft bending actuators and an integrated resistive tactile sensor array. It is a hybrid construction with soft robotic structures and rigid skeletal elements, which reinforce the body, focus the direction of the actuator’s movement, and make the finger joints follow the forward kinematics. The hand is equipped with a resistive tactile dielectric elastomer sensor array that directly triggers the hand’s actuation in the sense of reflexes. The hand can execute precision grips with two and three fingers, as well as lateral grip and strong grip types. The softness of the actuation allows the finger to adapt to the shape of the objects. Full article

In this paper, we study $H_{\infty }$ control for systems with explicit mechanical constraints and a lack of state information, such as walking robots. This paper proposes an $H_{\infty }$ control design scheme based on solving an optimization problem with linear matrix inequality constraints. Our method is based on the orthogonal decomposition of the state variables and the use of two linear controllers and a Luenberger observer, tuned to achieve the desired properties of the closed-loop system. The method takes into account static linear additive disturbance, which appears due to the uncertainties associated with the mechanical constraints. We propose a dynamics linearization procedure for systems with mechanical constraints, taking into account the inevitable lack of information about the environment; this procedure allows a nonlinear system to be transformed into a form suitable for the application of the proposed control design method. The method is tested on a constrained underactuated three-link robot and a flat quadruped robot, showing the desired behavior in both cases. Full article

The globally robust control of a four-link manipulator arm (FLMA) is an important subject for a wide range of industrial applications such as COVID-19 prevention robotics, lower limb rehabilitation robotics and underwater robotics. This article uses the feedback linearized approach to stabilize the complex nonlinear FLMA without applying a nonlinear approximator that includes the fuzzy approach and neural network optimal approach. This article proposes a new approach based on the “first” derived nonlinear convergence rate formula of the FLMA to control highly nonlinear dynamics. The linear quadratic regulator (LQR) method is often applied in the balance controlling space of the underactuated manipulator. This proposed approach takes the place of the LQR approach without the necessary trial and error operations. The implications of the proposed approach are “globally” effective, whereas the Jacobian linearized approach is “locally” valid. In addition, the main innovation of the proposed approach is to perform “simultaneously” additional performances including almost disturbance decoupling performance, which takes the place of the traditional posture–energy approach and avoids some torque chattering behaviour in the swing-up space, and globally exponential stable performance, without the need to solve the Hamilton–Jacobin equation. Simulations of comparative examples show that the proposed controller is superior to the singular perturbation and fuzzy approaches. Full article

Maintenance of the exteriors of buildings with convex façades, such as skyscrapers, is in high demand in urban centers. However, manual maintenance is inherently dangerous due to the possibility of accidental falls. Therefore, research has been conducted on cleaning robots as a replacement for human workers, e.g., the dual ascension robot (DAR), which is an underactuated rope-driven robot, and the rope-riding mobile anchor (RMA), which is a rope-riding robot. These robots are equipped with a convex-façade-cleaning system. The DAR and RMA are connected to each other by a rope that enables vibration transmission between them. It also increases the instability of the residual vibration that occurs during the operation of the DAR. This study focused on reducing the residual vibrations of a DAR to improve the stability of the overall system. Because it is a rope-on-rope (ROR) system, we assumed it to be a simplified serial spring–damper system and analyzed its kinematics and dynamics. An input-shaping technique was applied to control the residual vibrations in the DAR. We also applied a disturbance observer to mitigate factors contributing to the system uncertainty, such as rope deformation, slip, and external forces. We experimentally validated the system and assessed the effectiveness of the control method, which consisted of the input shaper and disturbance observer. Consequently, the residual vibrations were reduced. Full article

Dynamic locomotion capabilities on complex terrains constitute a critical requirement for humanoid robots in industrial manufacturing and emergency response applications. To address the fundamental challenges of terrain perception, underactuated dynamics planning, and foothold control in unstructured environments, this paper proposes a hierarchical planning and control framework that integrates terrain perception. The framework first segments the terrain to generate convex polygon constraints that characterize the terrain features. Subsequently, an optimization model is constructed based on nonlinear model predictive control, integrating underactuated dynamics and terrain constraints into a multi-objective optimal control problem. This problem is solved in real time using sequential quadratic programming. Furthermore, a hierarchical whole-body control approach is employed, which achieves coordinated whole-body control under multiple tasks and constraints through priority task allocation and quadratic programming. We validate the proposed methods through simulated experiments on forward walking, external force disturbance, and complex terrain walking conducted on the MuJoCo simulation platform. The simulation demonstrates that the robot can achieve a stable walking speed of 1 m/s and possess forward and lateral anti-disturbance capabilities of 60 Ns and 30 Ns, respectively. The robot can also stably traverse stairs with a height difference of 0.16 m and random terrains. These results validate the advantages of the proposed method in terms of dynamic performance, robustness, and terrain adaptability. Full article

In various fields, planar 2R underactuated robots have garnered significant attention due to their numerous applications. To guarantee the stable control of these robots, a simple control strategy is presented in this paper, and we utilize the intelligent optimization algorithm to enhance the controller parameters. Initially, a comprehensive dynamic model is developed for the robot with its control properties described. Subsequently, we design a PD controller to control the movement of the planar 2R underactuated robot. The differential evolution algorithm (DEA) is used to optimize the parameters of the PD controller to obtain the best control effect and make each link reach the target state. The findings from the simulation demonstrate the efficacy of the approach, and the designed strategy shows a higher stability and convergence rate, highlighting its important contribution to the field of underactuated robots. Full article