Search Results (142)

In this work, an innovative Schönflies motion generator manipulator is introduced, featuring a parallel architecture composed of serial chains with mixed degrees of freedom. Fundamental kinematic aspects essential to any manipulator such as displacement, velocity, acceleration, and singularity analyses are thoroughly addressed. Screw theory is employed to derive compact input–output expressions for velocity and acceleration, leveraging the properties of reciprocal screws and lines associated with the constrained degrees of freedom in the parallel manipulator. A key advantage of the proposed design is its near-complete avoidance of singular configurations, which significantly enhances its applicability in robotic manipulation. Numerical examples are provided to validate the theoretical results, with corroboration from specialized tools such as ADAMS™ software and data fitting algorithms. These results confirm the reliability and robustness of the developed kinematic analysis approach. 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

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

Although existing motion transfer methods for bionic robot arms are based on kinematic equivalence or simplified dynamic models, they frequently fail to tackle dynamic compliance and real-time adaptability in complex human-like motions. To address this shortcoming, this study presents a motion transfer method from the human arm to a bionic robot arm based on the hybrid PSO-RF (Particle Swarm Optimization-Random Forest) algorithm to improve joint space mapping accuracy and dynamic compliance. Initially, a high-precision optical motion capture (Mocap) system was utilized to record human arm trajectories, and Kalman filtering and a Rauch–Tung–Striebel (RTS) smoother were applied to reduce noise and phase lag. Subsequently, the joint angles of the human arm were computed through geometric vector analysis. Although geometric vector analysis offers an initial estimation of joint angles, its deterministic framework is subject to error accumulation caused by the occlusion of reflective markers and kinematic singularities. To surmount this limitation, this study designed five action sequences for the establishment of the training database for the PSO-RF model to predict joint angles when performing different actions. Ultimately, an experimental platform was built to validate the motion transfer method, and the experimental verification showed that the system attained high prediction accuracy (R² = 0.932 for the elbow joint angle) and real-time performance with a latency of 0.1097 s. This paper promotes compliant human–robot interaction by dealing with joint-level dynamic transfer challenges, presenting a framework for applications in intelligent manufacturing and rehabilitation robotics. Full article

This paper focuses on the design analysis of a novel 5-degrees of freedom (DOF) double-driven parallel mechanism (PM). By arranging two independent actuators on one branch chain, the mechanism can realize the five degrees of freedom of the moving platform only by relying on three branch chains, which have the characteristics of a compact structure and large workspace. Subsequently, the kinematic model of the mechanism is established, and the workspace, dexterity, and singularity characteristics are analyzed based on the derived model. Additionally, an explicit dynamic model of the mechanism is established based on the principle of virtual work. Finally, based on the dynamic model, the manipulability ellipsoid index and the inertial coupling strength index are proposed, and the distribution of these two kinds of dynamic performance indexes in the workspace is studied. Full article

The number of applications of parallel topology robots in industry is growing, and the interest of academics in finding new solutions and applications to implement such mechanisms is present all over the world. Industrywide, the most commonly used motion types need four- and six-degrees-of-freedom (DoF) robots. While there are commercial variants from different robot vendors, this study offers new alternatives to these. Based on Lie algebra synthesis, symmetrical parallel structures are identified, according to certain rules. Implementing 2-DoF actuation modules, the number of robot limbs is reduced compared to existing commercial robot structures. In terms of the applicability of a parallel mechanism (also concerning the control algorithm), it is important to determine singular configurations. Therefore, in addition to the kinematic schematics of the newly proposed mechanisms, their singular configurations are also discussed. Based on some dimensional simplifications (without a loss of generality), the conditions for the singular configurations are enumerated for the presented parallel topology robots with symmetrical kinematic chains. Finally, a comparison of the proposed mechanism is presented, considering its singular configurations. 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 planar parallel robots are widely employed in industrial applications due to simple geometry, few linkage interferences, and a large, reachable workspace. The symmetric geometry can bring significant convenience to parallel robots. The complexity of the mathematic models can be simplified since only one calculation method can be proposed to deal with various kinematic limbs in a parallel manipulator. The symmetric geometry can ease the assembly and maintenance procedures due to the modular design of linkages/joints. A novel 2-translation and 1-rotation (2T1R) parallel robot with symmetric geometry is proposed in this research. There is one closed loop in each kinematic limb, and 18 revolute joints are applied in its planar structure. Both the inverse and direct kinematic models are explored. The first-order relationship between robot inputs and outputs are constructed. Various singularity configurations are obtained based on the Jacobian matrix. The reachable workspace is resolved by the discrete spatial searching methodology, followed by the impacts originating from various linkages. The dexterity analysis of the parallel robot is conducted. Full article

Rapeseed oil transesterification reaction with ethanol under supercritical fluid conditions was performed either in the presence of catalysts or without them. The catalysts were Al₂O₃ and AlOOH, obtained after Al₂O₃ hydrothermal processing, and CaO/Al₂O₃ and CaO/AlOOH, obtained after permeation. The obtained product was measured for dynamic viscosity and density. Based on these data, kinematic viscosity was calculated. Biodiesel fuel was separated via centrifugation to extract more viscous ethyl esters of saturated fatty acids and unreacted triglycerides in order to comply with the standards for biodiesel fuel. Analyses have found that the maximum content of obtained ethyl esters of fatty acids in a reaction product before separation is reached, in the case of using the CaO/AlOOH catalyst, is in the amount of 93.34% by mass; and none of the samples’ kinematic viscosity values comply with the standards for biodiesel fuel. Performing centrifugation allowed us to reduce viscosity and increase biodiesel fuel concentration to reach the EN14214 standard requirements. Also, a significant deterioration of the initial catalysts’ strength after the singular experiment has been observed: Al₂O₃ by 22.4%, AlOOH by 13.89%, CaO/Al₂O₃ by 25.13%, and CaO/AlOOH by 17.27%. Full article

Unmanned surface vehicles (USVs) are increasingly critical in modern maritime operations, where reliable cooperative formation control under actuator failure is essential for safe navigation and efficient mission execution. Thus, this study presents an innovative fault-tolerant control strategy for USV formations, specifically addressing the challenges posed by actuator degradation, compound uncertainties, and input saturation. Concretely, the main contribution of this study is as follows. First, a detailed analysis of the USV kinematics and dynamics is conducted, and a novel position constraint model is developed through a formation transformation approach. To mitigate internal and external disturbances, a new non-singular terminal sliding mode surface is designed in conjunction with a dynamically regulated convergence law, ensuring finite-time convergence while reducing chattering. An adaptive terminal sliding mode controller is then formulated, integrating an event-triggered mechanism and an RBF neural network to compensate for model uncertainties and input saturation effects. Simulation results demonstrate that the proposed method not only achieves robust cooperative formation control under partial actuator failure but also significantly enhances the tracking accuracy and reduces the communication load compared to conventional sliding mode approaches. Full article

The forward kinematics of the Stewart platform is crucial for precise control and reliable operation in six-degree-of-freedom motion. However, there are some shortcomings in practical applications, such as calculation precision, computational efficiency, the capacity to resolve singular Jacobian matrix and real-time predictive performance. To overcome those deficiencies, this work proposes a hybrid strategy for forward kinematics in the Stewart platform based on dual quaternion neural network and ARMA time series prediction. This method initially employs a dual-quaternion-based back-propagation neural network (DQ-BPNN). The DQ-BPNN is partitioned into real and dual parts, composed of parameters such as driving-rod lengths, maximum and minimum lengths, to extract more features. In DQ-BPNN, a residual network (ResNet) is employed, endowing DQ-BPNN with the capacity to capture deeper-level system characteristics and enabling DQ-BPNN to achieve a better fitting effect. Furthermore, the combined modified multi-step-size factor Newton downhill method and the Newton–Raphson method (C-MSFND-NR) are employed. This combination not only enhances computational efficiency and ensures global convergence, but also endows the method with the capability to resolve a singular matrix. Finally, a traversal method is adopted to determine the order of the autoregressive moving average (ARMA) model according to the Bayesian information criterion (BIC). This approach efficiently balances computational efficiency and fitting accuracy during real-time motion. The simulations and experiments demonstrate that, compared with BPNN, the R² value in DQ-BPNN increases by 0.1%. Meanwhile, the MAE, MAPE, RMSE, and MSE values in DQ-BPNN decrease by 8.89%, 21.85%, 6.90%, and 3.3%, respectively. Compared with five Newtonian methods, the average computing time of C-MSFND-NR decreases by 59.82%, 83.81%, 15.09%, 79.82%, and 78.77%. Compared with the linear method, the prediction accuracy of the ARMA method increases by 14.63%, 14.63%, 14.63%, 14.46%, 16.67%, and 13.41%, respectively. Full article

Finding realistic motions for redundant manipulators is essential for complex jobs such as home care and industrial assembly. Motion planning is complex when a task requires standing upright or moving through restricted spaces. This work provides an effective motion-planning strategy for 7-DOF manipulators that improves connections via redundancy. The analytic Cartesian-space-to-joint-space kinematic mapping models for 7-DOF redundant manipulators with diverse configurations are constructed first, and the feasible nodes are determined by sampling the Cartesian space without barriers to satisfy the task requirements. Each Cartesian-space sampling node can provide numerous feasible joint-space nodes because of the redundancy of the robot manipulators. To remove additional valid nodes from a singular position, joint configurations with the same end-effector position orientation are modified iteratively. Finally, we find the nearest nodes in the joint-space constraint manifold and build collision-free smooth pathways. The task constraint levels were varied for a 7-DOF manipulator in simulations and experiments. The proposed planner finds more viable nodes at the same end-position attitude than one-to-one projection. It does not require numerical iterations and achieves high planning efficiency and a high motion-planning success rate. Full article

Inspired by changing the operation mode via branch-chain switching, a symmetrical multi-arm robot is proposed to meet the demand of continuous high-performance output. The kinematics and Jacobian matrix of the mechanism are established and solved, and the parameter expression when singularity occurs is obtained. As Type-I singularity is the key limiting factor of continuous motion, a branch-chain switching and motion planning method is proposed. Numerical simulation and joint interpolation control are explained according to the pseudo-inverse matrix. The mechanism completes the switching between the executing branch chain and the branch chain to be executed to realize continuous rotation with a large angle. The results prove the feasibility of the design and the correctness of the model, proving that this method can be a reference method for the design of this kind of robot. Full article

This paper proposes a dual-loop control strategy to address the trajectory tracking problem of differential wheeled mobile robots (WMRs). First, the kinematic model of the WMR is established, and the dynamic model including the actuators is derived. To tackle the issue of y-axis direction divergence in existing methods, a predefined-time velocity control law based on intermediate variables is proposed. By introducing the y-axis error term into the angular velocity control, the ability to rapidly track the target trajectory is enhanced, providing a reliable velocity tracking target for the dynamic controller. Furthermore, a predefined-time nonsingular fast terminal sliding mode controller is designed, which combines a nonsingular fast terminal sliding surface with predefined-time stability theory to overcome the singularity problem in existing approaches, achieving fast and accurate tracking of velocity errors. Additionally, to improve the system’s disturbance rejection capability, a nonlinear extended state observer (NESO) is proposed to estimate external disturbances and provide feedforward compensation to the dynamic controller. Experimental results demonstrate that the proposed strategy outperforms existing methods in terms of trajectory tracking accuracy and robustness, providing an effective solution for the high-performance control of WMRs. Full article

Traditional fracture reduction relies heavily on the surgeon’s experience, which hinders the transmission of skills. This specialization bottleneck, coupled with the high demands on physical strength, significantly limits the efficiency of daily treatments in trauma orthopedics. Currently, most fracture surgery robots focus on open or minimally invasive reduction techniques, which inherently carry the risk of iatrogenic damage due to surgical incisions or bone pin insertions. However, research in closed reduction-oriented robotic systems is remarkably limited. Addressing this gap, our study introduces a novel bone-setting robot for the closed reduction of ankle fractures designed with a redundant parallel platform. The parallel robot’s design incorporates three sliding redundancy actuators that enhance its tilt flexibility while maintaining load performance. Moreover, a singularity-free redundant kinematic solver has been developed, optimizing the robot’s operational efficacy. Building upon the demonstrations from professional closed reduction techniques, we propose the use of a multivariate Student-t process as a multi-output regression model within dynamic movement primitive for accurately learning stable reduction maneuvers. Additionally, we develop an anthropomorphic variable impedance controller based on inverse dynamics. The simulation results demonstrate convincingly that the developed ankle bone-setting robot is proficient in effectively replicating and learning the nuanced closed reduction techniques. Full article