Search Results (134)

Humanoid robots have attracted considerable attention for their anthropomorphic structure, extended workspace, and versatile capabilities. This paper presents a novel humanoid upper-body robotic system comprising a pair of 8-degree-of-freedom (DOF) arms, a 3-DOF head, and a 3-DOF torso—yielding a 22-DOF architecture inspired by human biomechanics and implemented via standardized hollow joint modules. To overcome the critical reliance of zeroing neural network (ZNN)-based trajectory tracking on the Jacobian matrix derivative, we propose an integration-enhanced matrix derivative observer (IEMDO) that incorporates nonlinear feedback and integral correction. The observer is theoretically proven to ensure asymptotic convergence and enables accurate, real-time estimation of matrix derivatives, addressing a fundamental limitation in conventional ZNN solvers. Workspace analysis reveals that the proposed design achieves an 87.7% larger total workspace and a remarkable 3.683-fold expansion in common workspace compared to conventional dual-arm baselines. Furthermore, the observer demonstrates high estimation accuracy for high-dimensional matrices and strong robustness to noise. When integrated into the ZNN controller, the IEMDO achieves high-precision trajectory tracking in both simulation and real-world experiments. The proposed framework provides a practical and theoretically grounded approach for redundant humanoid arm control. Full article

Robots with multiple degrees of freedom (DOFs), such as underwater vehicle–manipulator systems (UVMSs), are expected to optimize system performance by exploiting redundancy with various basic tasks while still fulfilling the primary objective. Multiple tasks for robots, which are expected to be carried out simultaneously with prescribed priorities, can be referred to as sets of tasks (SOTs). In this work, a hybrid vision/force control method with continuous task transitions is proposed for a UVMS to simultaneously track the reference vision and force trajectory during manipulation. Several tasks with expected objectives and specific priorities are established and combined as SOTs in hybrid vision/force tracking. At different stages, various SOTs are carried out with different emphases. A hierarchical optimization-based whole-body control framework is constructed to obtain the solution in a strictly hierarchical fashion. A continuous transition method is employed to mitigate oscillations during the task switching phase. Finally, comparative simulation experiments are conducted and the results verify the improved convergence of the proposed tracking controller for UVMSs. Full article

At present, the redundant structures are one of the most effective methods for solving magnetic levitation bearing coil failure. Coil failure causes residual effective magnetic poles to form different support structures and even asymmetrical structures. For the magnetic bearing with redundant structures, how to construct the electromagnetic force (EMF) that occurs under different support structures to achieve support reconstruction is the key to realizing fault tolerance control. To reveal the support reconstruction mechanism of magnetic bearing with a redundant structure, firstly, this paper takes a single-degree-of-freedom magnetic suspension body as an example to conduct a linearization theory analysis of the offset current, clarifying the concept of the current distribution matrix (CDM) and its function; then, the nonlinear EMF mode of magnetic bearing with an eight-pole is constructed, and it is linearized by using the theory of bias current linearization. Furthermore, the conditions of no coils fail, the 8th coil fails, and the 6–8th coils fail are considered, and, with the maximum principle function of EMF, the corresponding current matrices are obtained. Meanwhile, based on the CDM, the corresponding magnetic flux densities were calculated, proving that EMF reconstruction can be achieved under the three support structures. Finally, with the CDM and position control law, a fault-tolerant control system was constructed, and the simulation of the magnetic bearing with a redundant structure was carried out. The simulation results reveal the mechanism of support reconstruction with three aspects of rotor displacement, the value and direction of currents that occur in each coil. The simulation results show that, in the 8-pole magnetic bearing, this study can achieve support reconstruction in the case of faults in up to two coils. Under the three working conditions of wireless no coil failure, the 8th coil fails and the 6–8th coils fail, the current distribution strategy was adjusted through the CDM. The instantaneous displacement disturbance during the support reconstruction process was less than 0.28 μm, and the EMF after reconstruction was basically consistent with the expected value. Full article

The new generation of robots (Industry 5.0 and beyond) is expected to be accompanied by the massive introduction of autonomous and cooperative agents in our society, both in the industrial and service sectors. Cooperation with humans will be simplified by humanoid robots with a similar kinematic outline and a similar kinematic redundancy, which is required by the diversity of tasks that will be performed. A bio-inspired approach is proposed for coordinating the redundant DOFs of such agents. This approach is based on the ideomotor theory of action, combined with the passive motion paradigm, to implicitly address the degrees of freedom problem, without any kinematic inversion, while producing coordinated motor patterns structured according to the typical features of biological motion. At the same time, since the approach is force-field-based, it allows us to integrate the computational loop parallel modules that exploit the redundancy of the system for satisfying geometric or kinematic constraints implemented by appropriate repulsive force fields. Moreover, the model is expanded to include dynamic constraints associated with the Lagrangian dynamics of the humanoid robot to improve the energetic efficiency of the generated actions. Full article

This paper explores the application of Deep Neural Networks (DNNs) to solve the Inverse Kinematics (IK) problem in robotic manipulators. The IK problem, crucial for ensuring precision in robotic movements, involves determining joint configurations for a manipulator to reach a desired position or orientation. Traditional methods, such as analytical and numerical approaches, have limitations, especially for redundant manipulators, or involve high computational costs. Recent advances in machine learning, particularly with DNNs, have shown promising results and seem fit for addressing these challenges. This study investigates several DNN architectures, namely Feed-Forward Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), for solving the IK problem, using the TIAGo robotic arm with seven Degrees of Freedom (DOFs). Different training datasets, normalization techniques, and orientation representations are tested, and custom metrics are introduced to evaluate position and orientation errors. The performance of these models is compared, with a focus on curriculum learning to optimize training. The results demonstrate the potential of DNNs to efficiently solve the IK problem while avoiding issues such as singularities, competing with traditional methods in precision and speed. Full article

Hydraulic legged robots, with advantages such as high load capacity and power density, have become a strategic driving force in advancing intelligent mobile platform technologies. However, their high energy consumption significantly limits long-duration endurance and efficient operational performance. In this paper, inspired by the excellent autonomous energy-efficient consciousness of mammals endowed by natural evolution, a bionic energy-efficient inverse kinematics method based on neural networks (EIKNN) is proposed for the energy-efficient motion planning of hydraulic legged robots with redundant degrees of freedom (RDOFs). Firstly, the dynamic programming (DP) algorithm is used to solve the optimal joint configuration with minimum energy loss as the goal, and the training data set is generated. Subsequently, the inverse kinematic model of the leg with minimum energy loss is learned based on neural network (NN) simulation of the autonomous energy-efficient consciousness endowed to mammals by natural evolution. Finally, extensive comparative experiments validate the effectiveness and superiority of the proposed method. This method not only significantly reduces energy dissipation in hydraulic legged robots but also lays a crucial foundation for advancing hydraulic legged robot technology toward high efficiency, environmental sustainability, and long-term developmental viability. Full article

The use of multi-joint arms in a high-spindle environment can solve complex problems, but the singularity problem of the manipulator related to the structure of the serial manipulator is prominent. Therefore, based on the general mathematical model of fruit spatial distribution in high-spindle apple orchards, this study proposes two harvesting system architecture schemes that can meet the constraints of fruit spatial distribution and reduce the singularity of harvesting robot operation, which are four-arm dual-module independent moving scheme (Scheme A) and four-arm single-module parallel moving scheme (Scheme B). Based on the link-joint method, the analytical expression of the singular configuration of the redundant degree of freedom arm group system under the two schemes is obtained. Then, the inverse kinematics solution method of the redundant arm group and the singularity avoidance picking trajectory planning strategy are proposed to realize the judgment and solution of the singular configuration in the complex working environment of the high-spindle. The singularity rate of Scheme A in the simulation environment is 17.098%, and the singularity rate of Scheme B is only 6.74%. In the field experiment, the singularity rate of Scheme A is 26.18%, while the singularity rate of Scheme B is 13.22%. The success rate of Schemes A and B are 80.49% and 72.33%, respectively. Through experimental comparison and analysis, Scheme B is more prominent in solving singular problems but still needs to improve the success rate in future research. This paper can provide a reference for solving the singular problems in the complex working environment of high spindles. Full article

This paper presents a secure control framework enhanced with an event-triggered mechanism to ensure resilient and resource-efficient operation under false data injection (FDI) attacks on sensor measurements. The proposed method integrates a Kalman filter and a neural network (NN) to construct a hybrid observer capable of detecting and compensating for malicious anomalies in sensor measurements in real time. Lyapunov-based update laws are developed for the neural network weights to ensure closed-loop system stability. To efficiently manage system resources and minimize unnecessary control actions, an event-triggered control (ETC) strategy is incorporated, updating the control input only when a predefined triggering condition is violated. A Lyapunov-based stability analysis is conducted, and linear matrix inequality (LMI) conditions are formulated to guarantee the boundedness of estimation and system errors, as well as to determine the triggering threshold used in the event-triggered mechanism. Simulation studies on a two-degree-of-freedom (2-DOF) robot manipulator validate the effectiveness of the proposed scheme in mitigating various FDI attack scenarios while reducing control redundancy and computational overhead. The results demonstrate the framework’s suitability for secure and resource-aware control in safety-critical applications. Full article

This paper presents a novel redundant-actuated 4-PSS&S compliant parallel micro-rotation mechanism (P represents the actuated prismatic joint and S denotes the spherical pair) with three rotational degrees of freedom. First, compliance models of the flexure spherical hinge, each branch and the whole mechanism are established using the compliance matrix method. Then, the mechanism is simplified as an equivalent spring system to establish two kinetostatic models, with their correctness validated through finite element simulations. Finally, a comparative analysis is conducted on the performance of the 3-PSS&S mechanism, non-redundant-actuated 4-PSS&S mechanism and redundant-actuated 4-PSS&S mechanism. The results show the following: ① For the 4-PSS&S mechanism, redundant actuation with optimized actuating force distribution effectively reduces the peak actuating force by up to 50% (average 40.95%), achieving an average 10.79% reduction compared to the 3-PSS&S mechanism. ② The 4-PSS&S mechanism’s output stiffness increases by 26.68% in the θ_x and θ_y directions and by 33.31% in the θ_z direction compared to the 3-PSS&S mechanism. ③ Optimal force distribution significantly reduces the parasitic axis drift of the redundant-actuated 4-PSS&S mechanism at the constrained flexure spherical hinge S3, indicating higher motion accuracy. ④ The workspace volume of the redundant-actuated 4-PSS&S mechanism expands by 94.32% compared to the 3-PSS&S mechanism and by 372.89% compared to the non-redundant-actuated 4-PSS&S mechanism. Full article

Cable-driven redundant manipulators (CDRMs) are widely applied in various fields due to their notable advantages. Stiffness regulation capability is essential for CDRMs, as it enhances their adaptability and stability in diverse task scenarios. However, their stiffness regulation still faces two main challenges. First, stiffness regulation methods that involve physical structural modifications increase system complexity and reduce flexibility. Second, methods that rely solely on cable tension are constrained by the inherent stiffness of the cables, limiting the achievable regulation range. To address these challenges, this paper proposes a novel stiffness regulation method for CDRMs through the combined optimization of configuration and cable tension. A stiffness model is established to analyze the influence of the configuration and cable tension on stiffness. Due to the redundancy in degrees of freedom (DOFs) and actuation cables, there exist infinitely many configuration solutions for a specific pose and infinitely many cable tension solutions for a specific configuration. This paper proposes a dual-level stiffness regulation strategy that combines configuration and cable tension optimization. Motion-level and tension-level factors are introduced as control variables into the respective optimization models, enabling effective manipulation of configuration and tension solutions for stiffness regulation. An improved differential evolution algorithm is employed to generate adjustable configuration solutions based on motion-level factors, while a modified gradient projection method is adopted to derive adjustable cable tension solutions based on tension-level factors. Finally, a planar CDRM is used to validate the feasibility and effectiveness of the proposed method. Simulation results demonstrate that stiffness can be flexibly regulated by modifying motion-level and tension-level factors. The combined optimization method achieves a maximum RSR of 17.78 and an average RSR of 12.60 compared to configuration optimization, and a maximum RSR of 1.37 and an average RSR of 1.10 compared to tension optimization, demonstrating a broader stiffness regulation range. Full article

Task prioritization for inverse kinematics can be a powerful tool for realizing objectives in robot manipulation. This is particularly true for robots with redundant degrees of freedom, but it can also help address a debilitating singularity in six-axis robots. A roll-pitch-roll wrist is especially problematic for any six-axis robot because it produces a “gimbal-lock” singularity in the middle of the wrist workspace when the roll axes align. A task priority methodology can be used to realize only the achievable components of the commanded motion in the reduced operational space of a manipulator near singularities while phasing out the uncontrollable direction. In addition, this approach allows the operator to prioritize translation and rotation in the region of singularities. This methodology overcomes a significant drawback to the damped least-squares method, which can produce tool motion that deviates significantly from the desired path even in directions that are controllable. The approach used here reduces the operational space near the wrist singularity while maintaining full command authority over tool translation. The methodology is demonstrated in simulations conducted on a six degree-of-freedom Motoman MH250 manipulator. Full article

The working accuracy of space optical payloads, sensitive components, greatly depends on the pointing accuracy and stability of the platform. This article establishes a mathematical model for relative position and attitude measurement based on PSD and eddy current and analyzes the failure modes under the measurement models. Through model derivation, it can be concluded that the position and attitude measurement system has high redundancy; in the event of sensor failure in the horizontal or vertical direction, relative position and attitude measurement and resolution can still be completed, which solves the relative measurement problem of position and attitude measurement of the space Maglev vibration isolation control system, providing high-precision closed-loop control for the control system to achieve high-precision pointing and stability. In response to the requirements of high-precision non-contact displacement and attitude measurement, eddy current sensors were selected, and a sensor circuit box was designed. The testing and calibration system adopts an eight-bar Maglev layout, and the actuator has unidirectional dual-mode output. The actuator adopts a double closed magnetic circuit structure, and the coil adopts a winding single-coil structure. The system includes a multi-degree-of-freedom high-precision coil spatial pose automatic positioning platform, a strong magnetic structure, strong uniform magnetic field magnetization, an integrated assembly testing platform, etc. According to the test data, the driver has strong linearity in both low- and high-current ranges. The relative output error in the low-current range does not exceed 0.1 mA, and the relative output error in the high-current range does not exceed 2 mA. After fitting and calibration, it can meet the design requirements. Within redundant designing, fault mode analyzing, and system testing, the relative measurement system can ensure the working accuracy of the optical payload of the spacecraft, which reaches the advanced level in the field. Full article

This paper proposes an enhanced RRT* algorithm (XN-RRT*) to address the challenges of low path planning efficiency and suboptimal picking success rates in complex pitaya harvesting environments. The algorithm generates sampling points based on normal distribution and dynamically adjusts the center and range of the sampling distribution according to the target distance and tree density, thus reducing redundant sampling. An improved artificial potential field method is employed during tree expansion, incorporating adjustment factors and target points to refine the guidance of sampling points and overcome local optima and infeasible targets. A greedy algorithm is then used to remove redundant nodes, shorten the path, and apply cubic B-spline curves to smooth the path, improving the stability and continuity of the robotic arm. Simulations in both two-dimensional and three-dimensional environments demonstrate that the XN-RRT* algorithm performs effectively, with fewer iterations, high convergence efficiency, and superior path quality. The simulation of a six-degree-of-freedom robotic arm in a pitaya orchard environment using the ROS2 platform shows that the XN-RRT* algorithm achieves a 98% picking path planning success rate, outperforming the RRT* algorithm by 90.32%, with a 27.12% reduction in path length and a 14% increase in planning success rate. The experimental results confirm that the proposed algorithm exhibits excellent overall performance in complex harvesting environments, offering a valuable reference for robotic arm path planning. Full article

Full-body movement involving multi-segmental coordination has been essential to our evolution as a species, but its study has been focused mostly on the analysis of one-dimensional data. The field is poised for a change by the availability of high-density recording and data sharing. New ideas are needed to revive classical theoretical questions such as the organization of the highly redundant biomechanical degrees of freedom and the optimal distribution of variability for efficiency and adaptiveness. In movement science, there are popular methods that up-dimensionalize: they start with one or a few recorded dimensions and make inferences about the properties of a higher-dimensional system. The opposite problem, dimensionality reduction, arises when making inferences about the properties of a low-dimensional manifold embedded inside a large number of kinematic degrees of freedom. We present an approach to quantify the smoothness and degree to which the kinematic manifold of full-body movement is distributed among embedding dimensions. The principal components of embedding dimensions are rank-ordered by variance. The power law scaling exponent of this variance spectrum is a function of the smoothness and dimensionality of the embedded manifold. It defines a threshold value below which the manifold becomes non-differentiable. We verified this approach by showing that the Kuramoto model obeys the threshold when approaching global synchronization. Next, we tested whether the scaling exponent was sensitive to participants’ gait impairment in a full-body motion capture dataset containing short gait trials. Variance scaling was highest in healthy individuals, followed by osteoarthritis patients after hip replacement, and lastly, the same patients before surgery. Interestingly, in the same order of groups, the intrinsic dimensionality increased but the fractal dimension decreased, suggesting a more compact but complex manifold in the healthy group. Thinking about manifold dimensionality and smoothness could inform classic problems in movement science and the exploration of the biomechanics of full-body action. Full article

The integration of redundant structures into robotic systems enhances the degrees of freedom (DOFs), flexibility, and capability to perform complex tasks. This study evaluates the mechanical performance of a 9-DOF series-parallel hybrid redundant device designed for rehabilitation training of patients with balance disorders. The redundant structural design improves the robot’s movement flexibility, optimizes load distribution, and mitigates stress concentration in local joints or components. To optimize the robot’s overall structural parameters and reduce joint driving forces, a genetic algorithm (GA) was employed. A custom dataset was created by collecting motion-related data, including foot posture and position. The robot’s mechanical characteristics were comprehensively analyzed, followed by simulation experiments. The results demonstrate that incorporating the redundant structure, along with the optimization of structural parameters, significantly enhances the robot’s mechanical performance. This study provides a solid foundation for the functional development and control system design of rehabilitation robots, extending the capabilities of existing systems and offering a novel, reliable, and efficient therapeutic tool for patients with balance disorders. Full article