Search Results (83)

This work systematically addresses the dual challenges of non-inertial dynamic coupling and kinematic constraint redundancy encountered in dynamic modeling of serial–parallel–serial hybrid robotic mechanisms, and proposes an improved Newton–Euler modeling method with constraint compensation. Taking the Skiing Simulation Platform with 6-DOF as the research mechanism, the inverse kinematic model of the closed-chain mechanism is established through $G_F$ set theory, with explicit analytical expressions derived for the motion parameters of limb mass centers. Introducing a principal inertial coordinate system into the dynamics equations, a recursive algorithm incorporating force/moment coupling terms is developed. Numerical simulations reveal a 9.25% periodic deviation in joint moments using conventional methods. Through analysis of the mechanism’s intrinsic properties, it is identified that the lack of angular momentum conservation constraints on the end-effector in non-inertial frames leads to system controllability degradation. Accordingly, a constraint compensation strategy is proposed: establishing linearly independent differential algebraic equations supplemented with momentum/angular momentum balance equations for the end platform. Co-Simulation results demonstrate that the optimized model reduces the maximum relative error of actuator joint moments to 0.98%, and maintains numerical stability across the entire configuration space. The constraint compensation framework provides a universal solution for dynamics modeling of complex closed-chain mechanisms, validated through applications in flight simulators and automotive driving simulators. Full article

The increasing adoption of industrial robots has significantly advanced manufacturing efficiency and flexibility. However, this expansion introduces new energy consumption challenges, especially as electricity has become the dominant energy source in automated systems. As the industrial sector faces rising energy costs and ambitious sustainability goals, understanding and minimizing the energy consumption of robotic systems is imperative. This review presents a structured analysis of energy consumption in industrial robots, linking mechanical design, actuation systems, and control strategies to their energetic effects. We first discuss different industrial robot types and their kinematic configurations, identifying how structural characteristics influence energy use. The article then categorizes energy consumption optimization strategies into software-based and hardware-based approaches. A comparative SWOT analysis highlights the strengths and limitations of each approach. The review also explores emerging trends such as DC microgrid integration. The future directions underline the need for standardized energy assessment frameworks and the development of hybrid optimization strategies that combine the reviewed approaches, suitable for being applied in real-world industrial robot applications. This work provides a comprehensive foundation for establishing best practices in energy consumption optimization for industrial robots. Full article

Accurate kinematic calibration of subreflector actuators is essential for pointing precision of large radio telescopes, particularly at high frequencies. Conventional least-squares methods are vulnerable to noise and outliers, and their accuracy may degrade when limited pose diversity leads to poor parameter excitation. To address these challenges, this paper proposes a novel robust self-calibration framework that integrates Huber loss and $L_2$ regularization into the Levenberg–Marquardt (LM) algorithm—yielding a hybrid optimization approach that combines residual robustness, numerical stability, and convergence reliability. A comprehensive simulation study was conducted under varying workspace sizes and sensor noise levels. The proposed method maintained stable performance even under reduced excitation and high-noise conditions, where traditional LM methods typically degrade, confirming its robustness and applicability to realistic calibration scenarios. The framework was further validated using a structured-light 6-DOF pose measurement system, the proposed method achieved over 90% improvement in both position and orientation accuracy compared to the traditional LM approach. These findings confirm the method’s effectiveness for high-precision 6-DOF calibration in parallel mechanisms, and its suitability for real-world applications in radio telescope subreflector alignment. Full article

The multiscale coupling characteristics of the kinematic interface behavior of mechanical transmission systems are the core factors affecting system accuracy and lifetime. In this paper, we propose an innovative framework to achieve multiscale modeling from surface topographic parameters to system-level dynamics response through four stages: microscopic topographic regulation, mesoscopic wear modeling, macroscopic gap evolution, and system vibration prediction. Through the active design of laser-textured surfaces and gradient coatings, the contact stress distribution can be regulated to keep the wear extension; combined with the multiscale physical model and joint simulation technology, the dynamic feedback mechanism of wear–gap–vibration is revealed. Aiming at the challenges of data scarcity and mechanism complexity, we integrate data enhancement and migration learning techniques to construct a hybrid mechanism–data-driven life prediction model. This paper breaks through the limitations of traditional isolated analysis and provides theoretical support for the design optimization and intelligent operation and maintenance of high-precision transmission systems. Full article

To address the challenges of manual apple harvesting and the limitations of existing devices—such as constrained workspace, low efficiency, and limited flexibility—a two-stage, sub-region harvesting device was developed. The design, informed by the fruit distribution characteristics in densely planted dwarf apple orchards, integrates a positioning mechanism and a fruit-picking mechanism, enabling multiple pickings within a single positioning operation to enhance workspace coverage. A forward kinematics model was established using the Denavit–Hartenberg (D–H) parameter method. An improved Monte Carlo simulation based on a hybrid Beta distribution estimated the maximum reachable distances of the fruit-picking reference point in the X, Y, and Z directions as 2146 mm, 2169 mm, and 2165 mm, respectively—adequately covering the target harvesting domain. Incorporating a translational axis structure further expanded the harvesting volume by 1.165 m³, a 42.40% improvement over the conventional 3R configuration. To support adaptive control, a random point–geometry fusion method was proposed to solve for joint variables based on harvesting postures, and an automatic fruit-picking control system was implemented. Experimental validation, including reference point tracking and harvesting tests, demonstrated maximum positioning errors of 1.5 mm and 2.2 mm, a fruit-picking success rate of 76.53%, and an average picking time of 7.24 s per fruit—marking a 4.6% improvement compared to existing devices reported in previous studies. This study provides a comprehensive technical framework and practical reference for advancing mechanized apple harvesting. Full article

Aircraft trajectory prediction is challenging because of the flight process with uncertain kinematic motion and varying dynamics, which is characterized by intricate temporal dependencies of the flight surveillance data. To address these challenges, this study proposes a novel hybrid prediction framework, the IMM-Informer, which integrates an interacting multiple model (IMM) approach with the deep learning-based Informer model. The IMM processes flight tracking with multiple typical motion models to produce the initial state predictions. Within the Informer framework, the encoder captures the temporal features with the ProbSparse self-attention mechanism, and the decoder generates trajectory deviation predictions. A final fusion combines the IMM estimators with Informer correction outputs and leverages their respective strengths to achieve accurate and robust predictions. The experiments are conducted using real flight surveillance data received from automatic dependent surveillance-broadcast (ADS-B) sensors to validate the effectiveness of the proposed method. The results demonstrate that the IMM-Informer framework has notable prediction error reductions and significantly outperforms the prediction accuracies of the standalone sequence prediction network models. Full article

This study presents a topological design and modeling framework for 3D-printed robotic grippers, tailored for combined precision and coarse robotics assembly. The proposed methodology leverages topology optimization to develop multi-scale-compliant mechanisms, comprising a symmetrical continuum structure of five beams. The proposed methodology centers on the hybrid kinematics for precision and coarse operations of the gripper, parametrizing beam deformations in response to a defined set of boundary conditions and varying input loads. The research employs topology analysis to draw a clear correlation between input load and resultant motion, with a particular emphasis on the mechanism’s capacity to integrate both fine and coarse movements efficiently. Additionally, the paper pioneers an innovative solution to the ubiquitous point-contact problem encountered in grasping, intricately weaving it with the stiffness matrix. The overarching aim remains to provide a streamlined design methodology, optimized for manufacturability, by harnessing the capabilities of contemporary 3D fabrication techniques. This multifaceted approach, underpinned by the multiscale grasping method, promises to significantly advance the domain of robotic gripping and manipulation across applications such as micro-assembly, biomedical manipulation, and industrial robotics. Full article

Performing long-duration navigation without the global navigation satellite system (GNSS) network is a challenging task, particularly for small unmanned aerial vehicles (UAVs) equipped with low-cost micro-electro-mechanical sensors. This study proposes a hybrid neural network that integrates self-attention mechanisms with long short-term memory (SALSTM) to enhance GNSS-denied navigation performance. The estimation task of GNSS-denied navigation is first modeled based on UAV aerodynamics and kinematics, enabling a precise definition of the inputs and outputs that SALSTM needs to map. A self-attention layer is inserted in multiple LSTM layers to capture long-range dependencies in subtle dynamic changes. The output layer is designed to generate state sequences, leveraging the recursive nature of LSTM to enforce state continuity constraints. The outputs of SALSTM are fused to enhance integrated navigation within an extended Kalman filter framework. The performance of the proposed method is evaluated using flight data obtained from field tests. The results demonstrate that SALSTM-enhanced integrated navigation achieves superior long-term stability and improves velocity and position estimation accuracy by more than 50% compared to the best existing methods. Full article

To address the challenges of confined workspaces and high-precision requirements in thyroid surgery, this paper proposes a modular cable-driven robotic system with a hybrid rigid–continuum structure. By integrating rigid mechanisms and continuum joints within a closed-loop cable-driven framework, the system achieves a balance between flexibility in narrow spaces and operational stiffness. To tackle kinematic model inaccuracies caused by manufacturing errors, an innovative joint decoupling strategy combined with the Particle Swarm Optimization (PSO) algorithm is developed to dynamically identify and correct 19 critical parameters. Experimental results demonstrate a 37.74% average improvement in repetitive positioning accuracy and a 52% reduction in maximum absolute error. However, residual positioning errors (up to 4.53 mm) at motion boundaries highlight the need for integrating nonlinear friction compensation. The feasibility of a safety-zone-based force feedback master–slave control strategy is validated through Gazebo simulations, and a ring-grasping experiment on a surgical training platform confirms its clinical applicability. Full article

This study employs four metaheuristic optimization methods to optimize the dimensional synthesis of Ackermann steering mechanisms for three-axle, six-wheeled vehicles with front-axle steering mode and reverse-phase steering mode. The employed optimization methods include Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO), Differential Evolution with golden ratio (DE-gr), and Linearly Ensemble of Parameters and Mutation Strategies in Differential Evolution (L-EPSDE). With a front-wheel steering angle range of 70 degrees, two hundred optimization experiments were conducted for each method, and statistical analyses revealed that DE-gr and L-EPSDE methods outperformed PSO and HPSO methods in terms of standard deviation, mean value, and minimum error. These two methods exhibited superior convergence stability, faster convergence, and higher accuracy compared to PSO and HPSO. Reverse-phase (K = 1) steering mode outperformed front-axle steering mode, delivering reduced steering errors and turning radii. Considering the transmission ratio of front to rear axle (K) as a design variable in reverse-phase steering mode increased design flexibility and significantly lowered steering errors for the front and rear axle steering mechanisms. However, this comes with a slight increase in the turning radius of the vehicle’s front part compared to when K = 1. The optimized mechanism, designed using the DE-gr method, was validated through kinematic simulations and steering analyses using MSC-ADAMS v2015 software, further confirming the effectiveness and reliability of the proposed design. Full article

The prediction of debris flows is essential for safeguarding infrastructure and minimizing the economic losses associated with the hazards. Traditional empirical and theoretical models, while providing foundational insights, often struggle to capture the complex and nonlinear behaviors inherent in debris flows. This study aims to enhance debris flow prediction by integrating theoretical modeling with data-driven approaches. We model debris flow as a viscoplastic fluid, employing the Herschel–Bulkley rheological model to describe its behavior. By combining the kinematic wave model with lubrication theory, we develop a comprehensive theoretical framework that encapsulates the mechanical physics of debris flows and identifies key governing parameters. Numerical solutions of this theoretical model are utilized to generate an extensive training dataset, which is subsequently used to train a support vector regression (SVR) model. The SVR model targets slide depth and velocity upon impact, using explanatory variables including yield stress, material density, source area depth and length, and slope length. The model demonstrates high predictive accuracy, achieving coefficients of determination $R^2$ of 0.956 for slide depth and 0.911 for slide velocity at impact. Additionally, the relative residuals $\sigma$ are primarily distributed within the range of −0.05 to 0.05 for both slide depth and slide velocity upon impact. These results indicate that the proposed hybrid model not only incorporates the fundamental physical mechanisms governing debris flows but also significantly enhances predictive performance through data-driven optimization. This study underscores the critical advantage of merging physical models with machine learning techniques, offering a robust tool for improved debris flow prediction and risk assessment, which can inform the development of more effective early warning systems and mitigation measures. Full article

Parallel–serial (hybrid) manipulators represent robotic systems composed of kinematic chains with parallel and serial structures. These manipulators combine the benefits of both parallel and serial mechanisms, such as increased stiffness, high positioning accuracy, and a large workspace. This study discusses the existing architectures and applications of parallel–serial robots and the methods of their design and analysis. The paper reviews around 500 articles and presents over 150 architectures of manipulators used in machining, medicine, and pick-and-place tasks, humanoids and legged systems, haptic devices, simulators, and other applications, covering both lower mobility and kinematically redundant robots. After that, the paper considers how researchers have developed and analyzed these manipulators. In particular, it examines methods of type synthesis, mobility, kinematic, and dynamic analysis, workspace and singularity determination, performance evaluation, optimal design, control, and calibration. The review concludes with a discussion of current trends in the field of parallel–serial manipulators and potential directions for future studies. Full article

To improve the workspace, linear displacement stiffness, and driving torque utilization of humanoid robot shoulder joint mechanisms, an offset-designed RBR-2RRR (R represents the revolute pair, and B represents the ball cage joint) spherical hybrid bionic shoulder joint configuration (SHBSJC) is proposed and its structural parameters are optimized. Firstly, the shoulder joint’s physiological structure is biomimetically designed, a prototype mechanism of RBR-2RRR SHBSJC is proposed, and its kinematics are solved. The deformation response of RBR-2RRR and 3-RRR under the same load is compared to verify the obtained configuration can improve the linear displacement stiffness. Considering the workspace and singularity, using the GCI and GDCI as optimization functions, the recommended and adopted values of structural parameters are obtained. The distribution diagrams of the LCI and LDCI demonstrate that the configuration meets performance expectations. To further increase the prototype mechanism’s workspace and match the human shoulder joint’s motion range, an offset-designed RBR-2RRR SHBSJC is proposed, and the offset angle, installation posture angle, and spatial mapping relationship of the mechanism are determined. The results of workspace comparison and virtual model machine action simulation indicate that the final configuration meets the workspace expectations. This work enriches the shoulder joint configuration types and has engineering application value. Full article

Modern mechanical engineering faces high competition in global markets, which requires manufacturers of process equipment to significantly reduce production costs while ensuring high product quality and maximum productivity. Metalworking occupies a significant part of industrial production and consumes a significant share of the world’s energy and natural resources. Improving the technology of manufacturing parts with an emphasis on more efficient use of metalworking machines is necessary to maintain the competitiveness of the domestic machine tool industry. Hybrid metalworking systems based on the principles of multi-purpose integration eliminate the disadvantages of monotechnologies and increase efficiency by reducing time losses and intermediate operations. The purpose of this work is to develop and implement a hybrid machine tool system and an appropriate combined technology for manufacturing machine parts. Theory and methods. Studies of the possible structural composition and layout of hybrid equipment at integration of mechanical and surface-thermal processes were carried out, taking into account the basic provisions of structural synthesis and componentization of metalworking systems. Theoretical studies were carried out using the basic provisions of system analysis, geometric theory of surface formation, design of metalworking machines, methods of finite elements, and mathematical and computer modeling. The mathematical modeling of thermal fields and structural-phase transformations during HEH HFC was carried out in ANSYS (version 19.1) and SYSWELD (version 2010) software packages using numerical methods of solving differential equations of unsteady heat conduction (Fourier equation), carbon diffusion (2nd Fick’s law) and elastic–plastic behavior of the material. The verification of the modeling results was carried out using in situ experiments employing the following: optical and scanning microscopy; and mechanical and X-ray methods of residual stress determination. Formtracer SV-C4500 profilograph profilometer was used in the study for simultaneous measurement of shape deviations and surface roughness. Surface topography was assessed using a Walter UHL VMM 150 V instrumental microscope. The microhardness of the hardened surface layer of the parts was evaluated on a Wolpert Group 402MVD. Results and discussion. The original methodology of structural and kinematic analysis for pre-design studies of hybrid metalworking equipment is presented. Methodological recommendations for the modernization of multi-purpose metal-cutting machine tool are developed, the implementation of which will make it possible to implement high-energy heating with high-frequency currents (HEH HFC) on a standard machine tool system and provide the formation of knowledge-intensive technological equipment with extended functionality. The innovative moment of this work is the development of hybrid metalworking equipment with numerical control and writing a unique postprocessor to it, which allows to realize all functional possibilities of this machine system and the technology of combined processing as a whole. Special tooling and tools providing all the necessary requirements for the process of surface hardening of HEH HFC were designed and manufactured. The conducted complex of works and approbation of the technology of integrated processing in real conditions in comparison with traditional methods of construction of technological process of parts manufacturing allowed to obtain the following results: increase in the productivity of processing by 1.9 times; exclusion of possibility of scrap occurrence at finishing grinding; reduction in auxiliary and preparatory-tasking time; and reduction in inter-operational parts backlogs. Full article

The field of bioinspired underwater robots aims to replicate the capabilities of marine animals in artificial systems. Stingrays have emerged as highly promising species to be mimicked because of their flat body morphology and size. Furthermore, they are considered high-performance species due to their maneuverability, propulsion mode, and sliding efficiency. Designing and developing mechanisms to imitate their pectoral fins is a challenge for underwater robotic researchers mainly because the locomotion characteristics depend on the coordinated movement of the fins. In the state of the art, several mechanisms were proposed with 2 active rotation degrees of freedom (DoFs) to replicate fin movement. In this paper, we propose adding an additional active DoF in order to improve the realism in the robotic manta ray movement. Therefore, in this article, we present the mechanical design, modeling, and kinematics analysis of a 3-active-and-rotational-DoF pectoral fin inspired by the Mobula Alfredi or reef manta ray. Additionally, by using the kinematics model, we were able to simulate and compare the behaviour of both mechanisms, that is, those with 2 and 3 DoFs. Our simulation results reveal an improvement in the locomotion, and we hypothesized that with the third DoF, some specific missions, such as hovering or fast emergence to the surface, will have a better performance. Full article