Search Results (1,044)

This research presents a reinforcement learning framework for stable quadruped locomotion using Proximal Policy Optimization (PPO). We address critical challenges in articulated robot control—including mechanical complexity and trajectory instability by implementing a 12-degree-of-freedom model in PyBullet simulation. Our approach features three key innovations: (1) a hybrid reward function ($R_t=0.72 · e^{-\Delta {\mathrm{CoG}}_t} + 0.25 · v_t - 0.11 · ‖{\tau }_t‖$) explicitly prioritizing center-of-gravity (CoG) stabilization; (2) rigorous benchmarking demonstrating Adam’s superiority over SGD for policy convergence (68% lower reward variance); and (3) a four-metric evaluation protocol quantifying locomotion quality through reward progression, CoG deviation, policy loss, and KL-divergence penalties. Experimental results confirm an 87.5% reduction in vertical CoG oscillation (from 2.0″ to 0.25″) across 1 million training steps. Policy optimization achieved −6.2 × 10⁻⁴ loss with KL penalties converging to 0.13, indicating stable gait generation. The framework’s efficacy is further validated by consistent CoG stabilization during deployment, demonstrating potential for real-world applications requiring robust terrain adaptation. Full article

Medium and large-sized birds exhibit remarkable agility and maneuverability in flight, with their flapping motion encompassing degrees of freedom in flapping, twist, and swing, which enables them to adapt effectively to harsh ecological environments. This study proposes a flapping–twist coupled driving mechanism for large-scale flapping-wing aircraft by mimicking the motion patterns of birds. The mechanism generates simultaneous twist and flapping motions based on the phase difference of double cranks, allowing for the adjustment of twist amplitude through modifications in crank radius and phase difference. The objective of this work is to optimize the lift and thrust of the flapping wing to enhance its flight performance. To achieve this, we first derived the kinematic model of the mechanism and conducted motion simulations. To mitigate the effects of the flapping wing’s flexibility, a rigid flapping wing was designed and manufactured. Through wind tunnel experiments, the flapping wing system was tested. The results demonstrated that, compared to the non-twist condition, there exists an optimal twist amplitude that slightly increases the lift of the flapping wing while significantly enhancing the thrust. It is hoped that this study will provide guidance for the design of multi-degree-of-freedom flapping wing mechanisms. Full article

With the application and rapid development of light industrial robots, it is vital to accelerate the prototype design to fulfill the demands of shortening the robot’s production cycle, owing to rapid update iterations. Since the traditional design method cannot intuitively and efficiently check the deficiencies in the design preparation, the secondary design iterations will result in higher equipment costs, longer design cycles, and lower development efficiency. The MBD (model-based design), a full 3D (three-dimensional) design and manufacturing method, is proposed to swiftly finish the prototype design for solving the above problems. Firstly, the robot design preparation is completed with the design requirements to generate a robot 3D model. Secondly, several design methods are used: (i) the rapid prototyping, which includes the joint component verification and selection to further optimize the 3D model; (ii) the robot kinematics algorithm, which provides a theoretical foundation for the 3D model design; (iii) the robot kinematics simulation, which verifies the correctness of the kinematics algorithm. Finally, the feasibility of the MBD is verified by the robot prototype and the motion control system test. Taking the MBD to design a 5-DoF (five-degrees-of-freedom) robot as an example, the joint verification and selection are finished quickly and accurately to build the robot prototype without the need for secondary design processing, and the kinematic algorithm verified by the co-simulation platform can be used directly in the actual motion control of the robot prototype, which accelerates the development of the robot motion control system. Full article

This study presents the design, fabrication, and performance assessment of a novel, small-scale (30–70 W), hybrid ocean energy system that captures energy from wave-induced heave motion using a point-absorber buoy and from ocean currents via a vertical axis water turbine (VAWT). Key innovations include a custom designed and built dual-rotor generator that accepts independent mechanical input from both subsystems without requiring complex mechanical coupling and a bi-directional mechanical motion rectifier with an overdrive. Numerical simulations using ANSYS AQWA (2024R2) and QBLADE(2.0.4) guided the design optimization of the buoy and turbine, respectively. Wave resource assessment for the Khobar coastline, Saudi Arabia, was conducted using both historical data and field measurements. The prototype was designed and built using readily available 3D-printed components, ensuring cost-effective construction. This mechanically simple system was tested in both laboratory and outdoor conditions. Results showed reliable operation and stable power generation under simultaneous wave and current input. The performance is comparable to that of existing hybrid ocean wave–current energy converters that employ more complex flywheel or dual degree-of-freedom systems. This work provides a validated pathway for low-cost, compact, and modular hybrid ocean energy systems suited for remote coastal applications or distributed marine sensing platforms. Full article

Accurate and rapid prediction of structural responses to seismic loading is critical for ensuring structural safety. Recently, there has been active research focusing on the application of deep learning techniques, including Physics-Informed Neural Networks (PINNs) and Long Short-Term Memory (LSTM) networks, to predict the dynamic behavior of structures. While these methods have shown promise, each comes with distinct limitations. PINNs offer physical consistency but struggle with capturing long-term temporal dependencies in nonlinear systems, while LSTMs excel in learning sequential data but lack physical interpretability. To address these complementary limitations, this study proposes a hybrid LSTM-PINN model, combining the temporal learning ability of LSTMs with the physics-based constraints of PINNs. This hybrid approach allows the model to capture both nonlinear, time-dependent behaviors and maintain physical consistency. The proposed model is evaluated on both single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structural systems subjected to the El-Centro ground motion. For validation, the 1940 El-Centro NS earthquake record was used, and the ground acceleration data were normalized and discretized for numerical simulation. The proposed LSTM-PINN is trained under the same conditions as the conventional PINN models (e.g., same optimizer, learning rate, and loss structure), but with fewer training epochs, to evaluate learning efficiency. Prediction accuracy is quantitatively assessed using mean error and mean squared error (MSE) for displacement, velocity, and acceleration, and results are compared with PINN-only models (PINN-1, PINN-2). The results show that LSTM-PINN consistently achieves the most stable and precise predictions across the entire time domain. Notably, it outperforms the baseline PINNs even with fewer training epochs. Specifically, it achieved up to 50% lower MSE with only 10,000 epochs, compared to the PINN’s 50,000 epochs, demonstrating improved generalization through temporal sequence learning. This study empirically validates the potential of physics-guided time-series AI models for dynamic structural response prediction. The proposed approach is expected to contribute to future applications such as real-time response estimation, structural health monitoring, and seismic performance evaluation. Full article

A reduced representation of a dynamical system helps us to understand what the true degrees of freedom of that system are and thus what the possible instabilities are. Here we extend previous work on barotropic flows to the more general non-barotropic flow case and study the implications for variational analysis and conserved quantities of topological significance such as circulation and helicity. In particular we introduce a four-function Eulerian variational principle of non-barotropic flows, which has not been described before. Also new conserved quantities of non-barotropic flows related to the topological velocity field, topological circulation and topological helicity, including a local version of topological helicity, are introduced. The variational formalism given in terms of a Lagrangian density allows us to introduce canonical momenta and hence a Hamiltonian formalism. Full article

This study investigates the nonlinear aeroelastic behavior and energy harvesting performance of a two-degrees-of-freedom NACA 0012 airfoil under varying reduced velocities and electrical load resistances. The system exhibits a range of dynamic responses, including periodic and chaotic states, governed by strong fluid–structure interactions. Nonlinear oscillations first appear near the critical reduced velocity $U_r^{*}=6$, with large-amplitude limit-cycle oscillations emerging around $U_r^{*}=8$ in the absence of the electrical loading. As the load resistance increases, this transition shifts to higher $U_r^{*}$, reflecting the damping effect of the electrical load. Fourier spectra reveal the presence of odd and even superharmonics in the lift coefficient, indicating nonlinearities induced by fluid–structure coupling, which diminishes at higher resistances. Phase portraits and Poincaré maps capture transitions across dynamical regimes, from periodic to chaotic behavior, particularly at a low resistance. The voltage output correlates with variations in the lift force, reaching its maximum at an intermediate resistance before declining due to a suppressing nonlinearity. Flow visualizations identify various vortex shedding patterns, including single (S), paired (P), triplet (T), multiple-pair (mP) and pair with single (P + S) that weaken at higher resistances and reduced velocities. The results demonstrate that nonlinearity plays a critical role in efficient voltage generation but remains effective only within specific parameter ranges. 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

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

Additive manufacturing is one of the most common technologies used in prototyping and manufacturing usable parts. Currently, industrial robots are also increasingly being used to carry out this process. This is due to a robot’s capability to fabricate components with structural configurations that are unattainable using conventional 3D printers. The number of degrees of freedom of the robot, combined with its working range and precision, allows the construction of parts with greater dimensions and better strength in comparison to conventional 3D printing. However, the implementation of a robot into the 3D printing process requires the development of novel solutions to streamline and facilitate the prototyping and manufacturing processes. This work focuses on the need to develop new slicing methods for robotic additive manufacturing. A solution for alternative control code generation without external slicer utilization is presented. The implementation of the proposed method enables a reduction of over 80% in the time required to generate new G-code, significantly outperforming traditional approaches. The paper presents a novel approach to the slicing process in robotic additive manufacturing that is adopted for the fused granular fabrication process using thermoplastic polymers. Full article

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

Soft continuum robots are constructed from soft and compliant materials and can provide high flexibility and adaptability to various applications. They have theoretically infinite degrees of freedom (DOFs) and can generate highly nonlinear behaviors, which leads to challenges in accurately modeling and controlling their deformation, compliance, and behaviors. Inspired by animals, embodied intelligence utilizes physical bodies as an intelligent resource for information processing and task completion and offloads the computational cost of central control, which provides a unique approach to understanding and modeling soft robotics. In this study, we propose a theoretical framework to explain and guide distributed sensing enabled embodied intelligence for soft finger manipulation from a physics-based perspective. Specifically, we aim to provide a theoretical foundation to guide future sensor design and placement by addressing two key questions: (1) whether and why the state of a specific material point such as the tip trajectory of a soft finger can be predicted using distributed sensing, and, (2) how many sensors are sufficient for accurate prediction. These questions are critical for the design of soft and compliant robotic systems with embedded sensing for embodied intelligence. In addition to theoretical analysis, the study presents a feasible approach for real-time trajectory prediction through optimized sensor placement, with results validated through both simulation and experiment. The results showed that the tip trajectory of a soft finger can be predicted with a finite number of sensors with proper placement. While the proposed method is demonstrated in the context of soft finger manipulation, the framework is theoretically generalizable to other compliant soft robotic systems. Full article

Central pattern generators (CPGs) have been extensively researched and validated as a well-established methodology for bionic control, particularly within the field of legged robotics. However, investigations concerning octopod robots remain relatively sparse. This study presents the design of an octopod robotic system inspired by the biological characteristics of lobsters. The machine lobster utilizes remote sensing technology to execute designated tasks in subterranean and mining environments, with its motion regulated by CPGs, accompanied by a comprehensive simulation analysis. The research commenced with the modeling of a biomimetic lobster robot, which features a three-degree-of-freedom leg structure and torso, interconnected by shape memory alloys (SMAs) that serve as muscle actuators. Mathematically, both forward and inverse kinematics were formulated for the robot’s legs, and a 24-degree-of-freedom (DOF) gait pattern was designed and validated through MATLAB 2020a simulations. Subsequently, a multi-layer mesh CPG neural network model was developed utilizing the Kuramoto model, which incorporated frustration effects as the rhythm generator. The control model was constructed and evaluated in Simulink, while dynamic simulations were conducted using Adams 2022 software. The findings demonstrate the feasibility, robustness, and efficiency of the proposed CPG network in facilitating the forward locomotion of the lobster robot, thereby broadening the range of control methodologies applicable to octopod biomimetic robots. Full article

This paper presents the development and analysis of a novel 6-DOF Cartesian parallel mechanism intended for use as a haptic device for initial sonography training. The system integrates a manipulator designed for delivering force feedback in five degrees of freedom; however, in the current stage, only mechanical architecture and kinematic validation have been conducted. Future enhancements will focus on implementing and evaluating closed-loop force control to enable complete haptic feedback. To assess the kinematic performance of the mechanism, a detailed kinematic model was developed, and both the Kinematic Conditioning Index (KCI) and Global Conditioning Index (GCI) were computed to evaluate the system’s dexterity. A trajectory simulation was conducted to validate the mechanism’s movement, using motion patterns typical in sonography procedures. Quasi-static analysis was performed to study the transmission of force and torque for generating realistic haptic feedback, critical for simulating real-life sonography. The simulation results showed consistent performance, with dexterity and torque distribution confirming the suitability of the mechanism for haptic applications in sonography training. Additionally, structural analysis verified the robustness of key components under expected loads. In order to validate the proposed design, the prototype was constructed using a combination of aluminum components and 3D-printed ABS parts, with Igus^® linear guides for precise motion. The outcomes of this study provide a foundation for the further development of a low-cost, effective sonography training system. Full article

This study presents a computational approach to optimize the stiffness distribution of large deformable elastic braces (LDEBs), which possess a high elastic deformation capacity and are designed to enhance the seismic performance of building structures. An optimization problem was formulated to minimize the seismic response of two-story buildings modeled as multi-degree-of-freedom systems, in which both the building frame and the LDEBs were represented by spring elements. Seismic responses under earthquake excitations were evaluated through time-history analyses. Particle swarm optimization (PSO) was employed to determine the optimal stiffness ratios of LDEBs that minimize the maximum story drift. Extensive round-robin analyses were conducted to verify the validity of the PSO results, generating response surfaces that mapped the maximum story drift against the LDEBs’ stiffness under three different earthquake records. The analysis revealed that the optimal solutions obtained from the PSO coincided with the global minimum identified in the round-robin response surfaces. These results confirm the effectiveness of the proposed optimization framework and demonstrate the potential of LDEBs for enhancing seismic resilience in structural designs. Full article