Underactuated Cable-Driven Parallel Robots (UACDPRs) typically rely on relative internal sensors to estimate the end-effector (EE) state. Therefore, at startup, the reference values of the quantities measured by these sensors are unknown, and so is the initial pose of the EE. The problem of determining the reference values of the internal sensors is called initial-pose self-calibration. The latter is often formulated as an overdetermined system of nonlinear equations and solved using nonlinear weighted least-squares methods, minimizing the error between modeled and measured variables, and its effectiveness is highly influenced by the choice of measurement configurations, as well as the motion planning and control strategy used to reach them. This work presents two practical data acquisition methods for initial-pose self-calibration of UACDPRs, aiming to reduce the overall time required by the procedure and enhance process automation. The first method is slower but richer in data, as it relies on equilibrium poses and, therefore, can leverage cable-tension data, whereas the second method is faster and is based on geometric constraints only. The performance of the methods is evaluated in terms of acquisition time, number of measurements, and calibration accuracy on a 4-cable UACDPR prototype. The results highlight the merits and shortcomings of both methods, namely, trade-offs between the velocity of data collection and the precision of pose estimation.

In firefighting missions, human firefighters are often exposed to high-risk environments such as intense heat and limited visibility. To address this, firefighting robots can serve as valuable agents for autonomous navigation and flame perception. This paper proposes a novel visual Simultaneous Localization and Mapping (SLAM) framework, Fire SLAM, tailored for firefighting scenarios. The system integrates a flame detection and tracking thread-based on the YOLOv8n network and Kalman filtering-to achieve real-time flame detection, tracking, and 3D localization. By leveraging the detection results, dynamic flame regions are excluded from the SLAM front-end, allowing static features to be used for robust pose estimation and loop closure. To validate the proposed system, multiple datasets were collected from real-world and simulated fire environments. Experimental results demonstrate that Fire SLAM improves localization accuracy and robustness in fire scenes with flame disturbances, showing promise for autonomous firefighting robot deployment.

In the fields of meal-assisting robotics and human–robot interaction (HRI), real-time and accurate mouth pose estimation is critical for ensuring interaction safety and improving user experience. The complexity arises from the diverse opening degrees of mouths, variations in orientation, and external factors such as lighting conditions and occlusions, which pose significant challenges for real-time and accurate posture estimation of mouths. In response to the above-mentioned issues, this paper proposes a novel method for point cloud fitting and posture estimation of mouth opening degrees (FP-MODs). The proposed method leverages both RGB and depth images captured from a single viewpoint, integrating geometric modeling with advanced point cloud processing techniques to achieve robust and accurate mouth posture estimation. The innovation of this work lies in the hypothesis that different states of mouth openings can be effectively described by distinct geometric shapes: closed mouths are modeled by spatial quadratic surfaces, half-open mouths by spatial ellipses, and fully open mouths by spatial circles. Then, based on these hypotheses, we developed algorithms for fitting geometric models to point clouds obtained from mouth regions, respectively. Specifically, for the closed mouth state, we employ an algorithm based on least squares optimization to fit a spatial quadratic surface to the point cloud data. For the half-open or fully open mouth states, we combine inverse projection methods with least squares fitting to model the contour as a spatial ellipse and circle, respectively. Finally, to evaluate the effectiveness of the proposed FP-MODs method, extensive actual experiments were conducted under varying conditions, including different orientations and various types of mouths. The results demonstrate that the proposed FP-MODs method achieves high accuracy and robustness. This study can provide a theoretical foundation and technical support for improving HRI and food delivery safety in the field of robotics.

In dynamic environments, moving objects pose a great challenge to the accuracy and robustness of visual simultaneous localization and mapping (VSLAM) systems. Traditional dynamic VSLAM methods rely on hand-designed feature frames, and these methods usually make it difficult to fully utilize feature information in dynamic regions. To this end, this paper proposes a SLAM system (GAF-SLAM) that combines gray area feature points, weighted static probabilities, and spatio-temporal constraints. This method realizes the efficient fusion of key point detection and target detection by introducing YOLO-Point to extract gray area feature points from dynamic regions. These feature points are located within the detection frame and have potentially static feature point properties. By combining the reprojection error and polar geometry constraints, potential static feature points are effectively screened out and the identification of these gray area feature points is further optimized. Subsequently, a novel static probabilistic computational framework is designed to assign weights to these gray area feature points and dynamically adjust their influence on the optimization results during the attitude estimation process. By combining static probability with temporal continuity and spatial smoothness constraints, the system achieves significantly improved localization accuracy and robustness in dynamic environments. Finally, the proposed method was evaluated on the TUM RGB-D dataset. The experimental results demonstrate that GAF-SLAM significantly improves pose estimation accuracy and exhibits strong robustness and stability in dynamic indoor environments.

This study presents an innovative system for upper limb rehabilitation, combining a variable stiffness device, the ReHArm prototype, with a dynamic and engaging user interface, known as Arms Rehabilitation Management System. The proposed system offers a highly customisable approach to rehabilitation, ensuring real-time adaptability to patients’ specific needs while maintaining compactness and ease of use. Key features include a modular design allowing precise stiffness adjustments, a robust control architecture, and interactive rehabilitation phases designed to enhance user engagement. Extensive multidisciplinary analyses, including kinematic, dynamic, and structural evaluations, demonstrate the system’s ability to improve therapy effectiveness through tailored interaction and feedback. Validation tests demonstrated the prototype’s reliability and robustness, and initial usability assessments suggest its potential to improve rehabilitation outcomes. Further clinical studies involving patients will be necessary to fully evaluate its therapeutic effectiveness.

An improved identification algorithm is adopted to calibrate the kinematic parameters of the serial-parallel robot, which improves the motion accuracy of the end-effector. Firstly, a kinematic model of the serial-parallel robot is constructed based on the closed-loop vector method. Secondly, a kinematic error model is established by combining geometric error analysis with the vector differential method. Then, with the effective separation of compensable and non-compensable error sources, an identification model of kinematic parameters is constructed. Finally, an improved pivot element weighted iterative algorithm is used to identify the geometric error parameters. Through actual pose measurement, MATLAB is used to simulate the calibration process. The simulation and experimental results show that after kinematic calibration, compared with the traditional least squares method, the improved identification algorithm can significantly reduce the end-effector pose error of the serial-parallel robot, thus effectively improving the motion accuracy of the end-effector.

With increasing age, many elderly individuals will not be able to stand normally. To solve this problem, a knee exoskeleton is designed. The knee joint is designed as a variable stiffness structure. It can adjust its stiffness according to the body’s movement state, ensuring precise assistance while also enhancing human comfort. The variable stiffness mechanism consists of an elastic output actuator and a stiffness-adjusting actuator. The elastic output actuator is mainly responsible for the output of the joint torque. The stiffness-adjusting actuator is mainly responsible for adjusting the joint stiffness. These two mechanisms are analysed separately. Based on their relationship with the whole mechanism, a stiffness model of the entire knee joint is established. Experiments are subsequently conducted to evaluate the variable stiffness joint. The stiffness identification experiment indicates that the actual stiffness of the whole knee joint is essentially consistent with the theoretical value. The trajectory tracking experiment demonstrates that the joint exhibits excellent trajectory tracking capability, although stiffness has a certain effect. The exoskeleton assistive effect experiment demonstrates the ability of the exoskeleton to assist in standing. Additionally, the experiment on subjects with exoskeletons of different stiffnesses determines the impact of stiffness on human comfort.

A flexible power assistive exoskeleton is proposed in this study to overcome limitations in range of motion, assistance, and comfort existing in current exoskeletons. The flexible power assistive exoskeleton is made of three springs that store energy from shoulder movements to provide assistance. It uses biomechanical models to simulate muscle forces. It is highly portable and comfortable, with only 83.29 g weight. A theoretical model was established to address the relationship between body work and output force. An evaluation system is proposed to assess the comfort effect of the assistive exoskeleton. Results show that the assistive exoskeleton can support all ranges of motion for the human upper limbs. It can offer up to 14.2% assistance. It also has a mass-to-assistance value of 120. For a comforting evaluation, its satisfaction rate reaches 93.4%. In summary, we present a highly flexible power-assisted exoskeleton with a large motion range, noticeable assistance effect, and high comfortability. This work contributes to the development of flexible assistive exoskeletons and comforting evaluation strategies for wearable devices.

Workspace analysis is a crucial step in designing any robotic system and ensuring its safe operation. This article analyzes the workspace of a six degree-of-freedom (6-DOF) hybrid robot, which includes two separate modules with parallel architectures placed above each other. The upper module is a 4-DOF Delta-type parallel mechanism, and the lower module is a 2-DOF rotary mechanism with a circular rail. With this design, the hybrid robot represents a relative manipulation system, and workspace analysis is performed in the relative motion of the modules. This approach differs from other similar studies that combine the workspaces determined for each module independently, and we propose a method and derive results more suitable for practical use. To solve the workspace analysis problem, the paper develops a discretization-based approach, which considers all mechanical constraints. These constraints include joint constraints of each module and link interference between the modules. To analyze this interference, we apply the Gilbert–Johnson–Keerthi algorithm and represent the links as convex polytopes. Multiple numerical examples illustrate the developed techniques and show the translation and orientation workspaces of the robot for various relative configurations of its modules. Computer-aided-design simulations validate the proposed theoretical algorithms. The results demonstrate that the link interference between the modules, often ignored in other works, limits the workspace and should be considered for the proper workspace evaluation and design of similar hybrid robots.

Pipeline inspection robots play a crucial role in maintaining the integrity of pipeline systems across various industries. In this paper, a novel pipeline inspection robot is designed based on a four degrees-of-freedom (DOF) generalized parallel mechanism (GPM). First, a four DOF mechanism is introduced using numerical and graph synthesis. The design employs numerical and graph synthesis methods to achieve an ideal symmetric configuration, enhancing the robot’s adaptability and mobility. The coupling mid-platform, inspired by parallelogram mechanisms, enables synchronized contraction motion, allowing the robot to adjust to different pipe diameters. Then, the constraints of the pipeline inspection robot in the elbow are analyzed based on task requirements. Through kinematic and performance analyses using screw theory, the mechanism’s feasibility in practical applications is confirmed. Theoretical analysis, simulations, and experiments demonstrate the robot’s ability to achieve active steering in T-branches and elbows. Experimental validation in straight and bent pipes shows that the robot meets the expected speed targets and can successfully navigate complex pipeline environments. This research highlights the potential of GPMs in advancing the capabilities of pipeline inspection robots for real-world applications.

This work proposes an optimization approach for the time-consuming parts of Light Detection and Ranging (LiDAR) data processing and IMU-LiDAR data fusion in the LiDAR-inertial odometry (LIO) method. Two key novelties enable faster and more accurate navigation in complex, noisy environments. Firstly, to improve map update and point cloud registration efficiency, we employ a sparse voxel maps with a new update function to construct a local map around the mobile robot and utilize an improved Generalized Iterative Closest Point algorithm based on sparse voxels to achieve LiDAR point clouds association, thereby boosting both map updating and computational speed. Secondly, to enhance real-time accuracy, this paper analyzes the residuals and covariances of both IMU and LiDAR data in a tightly coupled manner, and achieves system state estimation by fusing sensor information through Gauss-Newton method, effectively mitigating localization deviations by appropriately weighting the LiDAR covariances. The performance of our method is evaluated against advanced LIO algorithms using eight open datasets and five self-collected campus datasets. Results show a 24.7–60.1% reduction in average processing time per point cloud frame, along with improved robustness and higher precision motion trajectory estimation in most cluttered and complex indoor and outdoor environments.

This paper presents a novel robust control method for a hip-assist exoskeleton robot’s joint module, addressing dynamic performance under variable loads. The proposed approach integrates traditional PID control with robust, model-based strategies, utilizing the system’s dynamic model and a Lyapunov-based robust controller to handle uncertainties. This method not only enhances traditional PID control but also offers practical advantages in implementation. Theoretical analysis confirms the system’s uniform boundedness and ultimate boundedness. A Matlab prototype was developed for simulation, demonstrating the control scheme’s feasibility and effectiveness. Numerical simulations show that the proposed fractional-order hybrid PD (FHPD) controller significantly reduces tracking error by 58.70% compared to the traditional PID controller, 55.41% compared to the MPD controller, and 32.32% compared to ADRC, highlighting its superior tracking performance and stability.

Based on the characteristics of the variable pivot gait during the human load-carrying, this paper proposes a double-leg coordination assistance principle for load-carrying: assisting support of the guiding leg at the heel-pivot stage by the spring to reduce the collision, which can reduce the ankle moment of the following leg that is performing the push-off at the toe-pivot stage. A novel unpowered load-carrying exoskeleton (ULE) with a double-support closed-chain configuration is designed, and the theoretical verification is carried out. Five subjects participate in the load-carrying and metabolic cost experiments for assisting and energy-saving effect evaluation, and the angle and moment of human joints, plantar pressure, spring compression and human net metabolic rate are analyzed. Compared with carrying load by the human alone, wearing the novel ULE with spring reduces the human peak ankle moment performing the push-off by up to 11.9 ± 1.6% (Mean±SE, 10 kg), average ankle moment over the support phase by up to 36.8 ± 9.1% (Mean±SE, 5 kg) and the average vertical plantar pressure by up to 8.1 ± 1%% (Mean±SE, 15 kg). Meanwhile, wearing the novel ULE reduces the human net metabolic rate by 5.6 ± 0.5% (Mean±SE, 10 kg), 4.1 ± 0.7% (Mean±SE, 15 kg) and 5.9 ± 1.6% (Mean±SE, 20 kg). The results show that the novel ULE can provide support and joint moment assistance over the whole support phase while reducing human net metabolic rate. This study can also be applied to the powered load-carrying exoskeleton, providing a new avenue.

Three-translation (3T) redundant actuated Delta/Par4-like manipulators with closed-loop units (CLU) are widely used in logistics sorting, industrial packaging, and other applications due to their superior load-carrying capacity and dynamic performance. However, compared to conventional 3T parallel mechanisms (PMs), there are fewer configurations of 3T redundant actuated PMs with CLU, and the existing CLU are of a single configuration. This paper introduces a method for synthesizing a 3T redundant actuated PM with CLU based on the atlas method. First, the degrees of freedom (DOF) line diagrams and constraint line diagrams of the moving platform are determined sequentially. By applying the dual rule, the constraint diagrams are decomposed, yielding six dual DOF spaces. The equivalent DOF line diagrams are expanded using the principle of line equivalence, which synthesizes a variety of novel 4- and 5-DOF branched chains with CLU. Second, considering the geometrically symmetric distribution criterion of the branched chains, two assembly schemes for redundant actuated 3T PMs with CLU are proposed, yielding at least 432 and 324 novel configurations, respectively. Finally, two representative novel mechanisms are selected, and their DOF properties are verified using screw theory, which demonstrates the feasibility and applicability of the synthesized method. This method systematically synthesizes novel redundant actuated 3T PMs with CLU, laying the foundation for further research into innovative CLU configurations.

Flapping-wing robots, inspired by natural flyers, have gained significant attention for surveillance and environmental monitoring applications. This study presents the design and analysis of a bat-inspired flapping-wing robot with foldable wings, aiming to enhance flight efficiency and maneuverability. The robot features silicone-based, stretchable membrane wings, with a wingspan of 1.4 m and a total mass of 620 g. A one-degree-of-freedom (DOF) revolute-spherical-spherical-revolute mechanism is used to reproduce the flapping motion, while a one-DOF Watt six-bar linkage mechanism enables dynamic wing folding, allowing adaptive wing shape modulation during flight. Explicit solutions for joint angle of the wing were expressed through analytical method. Flight tests were conducted to validate the effectiveness of the flapping-folding mechanism. Results show that the robot successfully replicates bat wing kinematics, with folding during the upstroke and unfolding during the downstroke. This research offers insights into bio-inspired wing designs for next-generation flapping-wing robots.

The variable stiffness actuator (VSA) excels at tasks that are challenging for traditional rigid mechanisms to perform. A novel variable stiffness tensegrity-based compliant actuator is proposed, following an analysis of the cons and pros of existing VSAs. The proposed actuator leverages a tensegrity structure to eliminate direct contact between rigid elements, thereby reducing the internal mechanical friction. This leads to low damping and compliant behavior. Additionally, it enables a wide range of stiffness adjustments and decouples rotational stiffness from the rotation angle by utilizing different variants of the mechanically adjustable compliance and controllable equilibrium position actuator (MACCEPA). The stiffness analysis of the single-joint actuator is presented and experimentally validated. This design is then extended to multi-joint mechanism applications, including serial mechanism configuration, wire-driven mechanism configuration, and direct-drive mechanism configuration. An evaluation of the structural characteristics of these three configurations is provided, offering different options for implementing VSAs. The conducted works could provide fresh insights into the field of VSA.

Soft robots have emerged as a transformative technology with widespread applications across diverse fields. Among various actuation mechanisms, fluid-based actuation remains predominant in soft robotics, where precise fluid regulation is fundamental to system performance. This review aims to provide a comprehensive reference for researchers interested in fluid regulation strategies in soft robots by outlining the current state of research in this field and discussing innovations in valve designs to inspire future advancements. The fluid regulation strategies discussed in this review are systematically categorized into three main approaches: valve-based, smart fluid-based, and pressure source-based strategies, with each type systematically classified and discussed in detail. Building upon this analysis, a Task-to-Fluidic Regulation System mapping framework is proposed, integrating the V-model principles from systems engineering to provide a structured, requirements-driven methodology that links task objectives to concrete regulation system configurations through sequential design and multi-level verification. Finally, the latest advancements in fluid regulation methods in soft robotics are summarized, along with emerging trends and directions for future development.

The study presents a novel cable-driven serial robot based on flexible joints and tensegrity structures, which features a rapid response capability in complex dynamic environments. This makes it particularly suitable for human–robot interaction scenarios. Compared to traditional rigid serial robots, the design’s compliance demonstrates significant advantages in addressing complex demands. The study delves into kinematic and dynamic modeling methods and verifies their effectiveness through simulations. The kinematic model transforms the local coordinate system to the global one using general kinematic equations. First, the static and dynamic model of the robot is derived based on the torque balance equation, and then the dynamic model of the robot is constructed. By simplifying the robot model, the relationship between tension values from driving cables and the robot’s workspace is analyzed under the constraints of tensegrity structures and flexible joints. Additionally, trajectory simulations validate the kinematic and dynamic models. The kinetic energy variation curves based on the trajectories confirm the accuracy of the theoretical analysis. This method demonstrates broad applicability and can be applied to other serial robots with flexible structures, offering effective solutions for use in complex dynamic environments.

Sit-to-stand (STS) motion is an essential daily activity. However, this motion becomes increasingly difficult for older adults as their muscle strength declines with age. To assist individuals in standing up while maximizing their muscle strength based on the assist-as-needed (AAN) strategy, assistive devices must detect early STS intent, specifically before the buttocks leave the chair, to ensure timely assistance. This study proposes a novel method for detecting STS intent by applying external mechanical stimuli to the toes and analyzing the resulting changes in heel and toe-reaction forces. Moreover, a structured detection framework was developed by utilizing predefined thresholds for the change rate and magnitude of the heel and toe-reaction forces to detect STS intent. Offline tests for threshold setting of STS-intent detection were established in the offline tests: change rate and magnitude of the reaction forces on the heel and toes. The thresholds for each criterion were determined using the Pareto optimization method. Using the determined thresholds, these criteria were then applied in online tests to evaluate the performance of the proposed intent detection method. The results demonstrated that mechanical stimuli improved the performance of STS-intent detection, providing accurate and stable detection. This method can be applied to STS-assistive devices to effectively implement AAN functionality for standing assistance.

Aiming at the issues of traditional ant colony algorithm (ACO) in mobile robot path planning, including initial search blindness, susceptibility to local optima, and slow convergence, this paper proposes a multi-strategy improved ant colony algorithm (MS-ACO). Firstly, dynamic non-uniform distribution of initial pheromones is implemented by integrating the repulsive field from artificial the potential field method. Secondly, the heuristic information is enhanced to improve global search capability while constraining unnecessary path turns. Thirdly, an improved pheromone update strategy is developed by adopting distinct updating mechanisms for different evolutionary phases. Finally, dynamic parameter adaptation is achieved through optimized weight coefficients and volatility coefficients that coordinate with the pheromone update strategy, better aligning with the iterative characteristics of ant colony optimization. Experimental results demonstrate that MS-ACO effectively addresses the limitations of traditional ACO. Under identical experimental conditions, it achieves a 30.4% reduction in path length, 37.8% decrease in pathfinding time, and 71% fewer turns compared to conventional methods, verifying the feasibility and superiority of the proposed algorithm.