Search Results (822)

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

This paper focuses on a class of 5-degrees-of-freedom (5-DOF) aerial platforms, particularly the Passively Articulated Dual UAVs (PADUAVs). These platforms have the potential to achieve omnidirectional motion, as their joints are free from position constraints. However, PADUAVs encounter singularity issues in certain configurations. To address this challenge, we propose a novel singularity-avoidance control strategy. The approach begins with an analysis of the flat outputs of the 5-DOF aerial system. Based on this analysis, we design a careful allocation strategy that maps position control to attitude control via the flat outputs. A variable intermediate attitude is introduced to ensure that this allocation remains singularity-free across all configurations of the 5-DOF aerial vehicle. The stability of the proposed controller is rigorously proven. We then apply the proposed control method to the PADUAV platform, providing detailed modeling, analysis, and dynamic decoupling of the system. Due to the presence of additional sub-vehicle dynamics in the PADUAV, an auxiliary attitude allocation module is also developed. The proposed position and attitude control allocation strategies enable the controller to maintain singularity-free stability across all configurations. Finally, we implement a 5-DOF tracking control strategy specifically tailored for the PADUAV. Numerical simulations validate the effectiveness of the proposed approach, demonstrating its robustness and reliability in aerial manipulation tasks. Full article

Loco-manipulation tasks using humanoid robots have great practical value in various scenarios. While reinforcement learning (RL) has become a powerful tool for versatile and robust whole-body humanoid control, visuomotor control in loco-manipulation tasks with RL remains a great challenge due to their high dimensionality and long-horizon exploration issues. In this paper, we propose a loco-manipulation control framework for humanoid robots that utilizes model-free RL upon model-based control in the robot’s tasks space. It implements a visuomotor policy with depth-image input, and uses mid-way initialization and prioritized experience sampling to accelerate policy convergence. The proposed method is validated on typical loco-manipulation tasks of load carrying and door opening resulting in an overall success rate of 83%, where our framework automatically adjusts the robot motion in reaction to changes in the environment. Full article

Background/Objectives: Manual therapy techniques, including mobilization and manipulation, are commonly used for chronic low back pain (CLBP), with clinical guidelines recommending their use. This study aimed to compare the immediate hypoalgesic effects of mobilization and manipulation in patients with non-specific CLBP, evaluating their impact on pain sensitivity and range of motion. Methods: A cross-over randomized controlled trial was conducted with 27 participants with non-specific CLBP. Participants received either mobilization or manipulation on two different intervention days. Outcome measures included pressure pain thresholds (PPTs) assessed with a digital algometer, pain intensity using a numeric rating scale, and lumbar range of motion (ROM) measured with a digital inclinometer. Results: The results indicated no statistically significant differences between mobilization and manipulation for any outcome measures (all p > 0.05). However, significant within-intervention improvements were observed, including pain reduction, increased PPTs, and enhanced ROM of the lower back. Conclusions: Our findings suggest that both mobilization and manipulation provide similar immediate benefits for patients with CLBP. The choice between these techniques should be based on therapists’ clinical reasoning and individualized risk stratification, considering the potential benefits and risks of each approach for a specific patient. 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

The fixed-time trajectory tracking control problem of the uncertain nonlinear Euler–Lagrange system is studied. To ensure the fast, high-precision trajectory tracking performance of this system, a non-singular terminal sliding-mode controller based on Gaussian process regression is proposed. The control algorithm proposed in this paper is applicable to periodic motion scenarios, such as spacecraft autonomous orbital rendezvous and repetitive motions of robotic manipulators. Gaussian process regression is employed to establish an offline data-driven model, which is utilized for compensating parametric uncertainties and external disturbances. The non-singular terminal sliding-mode control strategy is used to avoid singularity and ensure fast convergence of tracking errors. In addition, under the Lyapunov framework, the fixed-time convergence stability of the closed-loop system is rigorously demonstrated. The effectiveness of the proposed control scheme is verified through simulations on a spacecraft rendezvous mission and periodic joint trajectory tracking for a robotic manipulator. Full article

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. 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

The ability to autonomously generate up-to-date 3D models of robotic workcells is critical for advancing smart manufacturing, yet existing Next-Best-View (NBV) methods often rely on paradigms ill-suited for the fixed-base manipulators found in dynamic industrial environments. To address this gap, this paper proposes a novel NBV method for the complete exploration of a 6-DOF robotic arm’s workspace. Our approach integrates collision-based information gain metric, a potential field technique to generate candidate views from exploration frontiers, and a tunable fitness function to balance information gain with motion cost. The method was rigorously tested in three simulated scenarios and validated on a physical industrial robot. Results demonstrate that our approach successfully maps the majority of the workspace in all setups, with a balanced weighting strategy proving most effective for combining exploration speed and path efficiency, a finding confirmed in the real-world experiment. We conclude that our method provides a practical and robust solution for autonomous workspace mapping, offering a flexible, training-free approach that advances the state-of-the-art for on-demand 3D model generation in industrial robotics. Full article

In response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight approach is established. This approach takes into account the driver’s state, traffic environment risks, and the vehicle’s global control deviation to adjust the driving weights between humans and machines. Secondly, the human–machine cooperative relationship with unconscious competition is characterized as a master–slave game interaction. The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. Coordination of the manipulation actions of the driver and the intelligent driving system is achieved by balancing the master–slave game. Finally, different types of drivers are simulated by varying the parameters of the driver models. The effectiveness of the proposed driving weight allocation scheme was validated on the constructed simulation test platform. The results indicate that the human–machine collaborative control strategy can effectively mitigate conflicts between humans and machines. By giving due consideration to the driver’s operational intentions, this strategy reduces the driver’s workload. Under high-risk scenarios, while ensuring driving safety and providing the driver with a satisfactory experience, this strategy significantly enhances the stability of vehicle motion. Full article

In response to the limitations of vibration suppression performance caused by the difficulty in accurately modeling nonlinear friction during robotic manipulator dynamics parameter identification, this paper proposes a hybrid identification method based on a Broad Learning System (BLS) optimized by Particle Swarm Optimization (PSO). First, a joint excitation trajectory is designed using a fifth-order Fourier series with zero boundary conditions to ensure sufficient excitation of system dynamics. Then, a linear regression formulation of the manipulator’s structural dynamics is established, and the BLS network is employed to model the unstructured residuals—primarily arising from nonlinear friction—with high precision. Finally, the PSO algorithm is applied to optimize the hyperparameters of the BLS network, achieving global model optimality. Simulation results demonstrate that under typical motion conditions of the manipulator, the proposed method exhibits excellent capability in capturing nonlinear disturbances, maintaining joint prediction errors below 6 × 10⁻¹² N·m. This significantly improves the accuracy and robustness of the feedforward vibration suppression control. Moreover, by integrating PSO-based hyperparameter optimization and trajectory design with sufficient excitation, the proposed method enhances data efficiency during the identification process, offering a novel and practical identification strategy for precise modeling and control of complex mechanical systems. Full article

This paper presents the design and implementation of an autonomous robotic facility for textile sorting and recycling, leveraging advanced computer vision and machine learning technologies. The system enables real-time textile classification, localization, and sorting on a dynamically moving conveyor belt. A custom-designed pneumatic gripper is developed for versatile textile handling, optimizing autonomous picking and placing operations. Additionally, digital simulation techniques are utilized to refine robotic motion and enhance overall system reliability before real-world deployment. The multi-threaded architecture facilitates the concurrent and efficient execution of textile classification, robotic manipulation, and conveyor belt operations. Key contributions include (a) dynamic and real-time textile detection and localization, (b) the development and integration of a specialized robotic gripper, (c) real-time autonomous robotic picking from a moving conveyor, and (d) scalability in sorting operations for recycling automation across various industry scales. The system progressively incorporates enhancements, such as queuing management for continuous operation and multi-thread optimization. Advanced material detection techniques are also integrated to ensure compliance with the stringent performance requirements of industrial recycling applications. Full article

An X-rudder underwater vehicle’s hydrodynamic force acting on its rudder will display asymmetrical characteristics during vertical movement that are absent from a cross-rudder vehicle. This paper presents a novel hydrodynamic expression method based on rotational hydrodynamic transformation through a detailed analysis of the local flow characteristics around the tail attachment during the vertical plane maneuvering of the X-rudder vehicle, given that the conventional Taylor expansion-based hydrodynamic expression method is unable to characterize this asymmetric characteristic. With the help of this technique, a novel expression that can precisely describe the asymmetric hydrodynamic properties during the X-rudder vehicle’s underwater vertical plane maneuvering is created. This paper next concentrates on common vertical plane maneuvering motion situations and performs simulation predictions using both new and conventional expressions based on Taylor expansion. The asymmetric characteristics of the X-rudder underwater vehicle in vertical plane maneuvering have been experimentally confirmed, and the asymmetric characteristics become more pronounced as the speed increases, according to the results, which are compared with those of tests using self-driving models. Overall, the new model accurately describes the asymmetric features of the X-rudder vehicle’s vertical maneuvering motion and correlates well with the experimental findings. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article