Search Results (81)

Wind turbine blades feature complex geometries and operate under harsh conditions, including high curvature gradients, nonlinear deformations, elevated humidity, and particulate contamination. This study presents the design and kinematic analysis of a novel climbing robot based on a 10R folding metamorphic mechanism. The robot employs a hybrid wheel-leg drive and adaptively reconfigures between rectangular and hexagonal topologies to ensure precise adhesion and efficient locomotion along blade leading edges and windward surfaces. A high-order kinematic model, derived from a modified Grubler–Kutzbach criterion augmented by rotor theory, captures the mechanism’s intricate motion characteristics. We analyze the degrees of freedom (DOF) and motion branch transitions for three representative singular configurations, elucidating their evolution and constraint conditions. A scaled-down prototype, integrating servo actuators, vacuum adhesion, and multi-modal sensing on an MDOF control platform, was fabricated and tested. Experimental results demonstrate a configuration switching time of 6.3 s, a single joint response time of 0.4 s, and a maximum crawling speed of 125 mm/s, thereby validating stable adhesion and surface tracking performance. This work provides both theoretical insights and practical validation for the intelligent maintenance of wind turbine blades. Full article

With the rapid advancement of industrial intelligence, ensuring occupational safety has become an increasingly critical concern. Among the essential personal protective equipment (PPE), safety helmets play a vital role in preventing head injuries. There is a growing demand for real-time detection of helmet chinstrap wearing status during industrial operations. However, existing detection methods often encounter limitations such as user discomfort or potential privacy invasion. To overcome these challenges, this study proposes a non-intrusive approach for detecting the wearing state of helmet chinstraps, inspired by the mechanosensory hair arrays found on spider legs. The proposed method utilizes multiple MEMS inertial sensors to emulate the sensory functionality of spider leg hairs, thereby enabling efficient acquisition and analysis of helmet wearing states. Unlike conventional vibration-based detection techniques, posture signals reflect spatial structural characteristics; however, their integration from multiple sensors introduces increased signal complexity and background noise. To address this issue, an improved adaptive convolutional neural network (ICNN) integrated with a long short-term memory (LSTM) network is employed to classify the tightness levels of the helmet chinstrap using both single-sensor and multi-sensor data. Experimental validation was conducted based on data collected from 20 participants performing wall-climbing robot operation tasks. The results demonstrate that the proposed method achieves a high recognition accuracy of 96%. This research offers a practical, privacy-preserving, and highly effective solution for helmet-wearing status monitoring in industrial environments. Full article

To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article

Underwater propulsion systems are the fundamental functional modules of underwater robotics and are crucial in intricate underwater operational scenarios. This paper proposes a biomimetic underwater robot propulsion scheme that is motivated by the hindlimb movements of the bullfrog. A multi-linkage mechanism was developed to replicate the “kicking-and-retracting” motion of the bullfrog by employing motion capture systems to acquire biological data on their hindlimb movements. The FDM 3D printing and PC board engraving techniques were employed to construct the experimental prototype. The prototype’s biomimetic and motion characteristics were validated through motion capture experiments and comparisons with a real bullfrog. The biomimetic bullfrog hindlimb propulsion system was tested with six-degree-of-freedom force experiments to evaluate its propulsion capabilities. The system achieved an average thrust of 2.65 N. The effectiveness of motor drive parameter optimization was validated by voltage comparison experiments, which demonstrated a nonlinear increase in thrust as voltage increased. This design approach, which transforms biological kinematic characteristics into mechanical drive parameters, exhibits excellent feasibility and efficacy, offering a novel solution and quantitative reference for underwater robot design. Full article

This paper proposes an adaptive human–robot concurrent control scheme that achieves the appropriate gait trajectory for a robotic leg prosthesis to improve the wearer’s comfort in various tasks. To accommodate different wearers, a neuromuscular-force-based impedance method was developed using muscle activation to reshape gait trajectory. To eliminate the use of sensors for torque measurement, a disturbance observer was established to estimate the interaction force between the human residual limb and the prosthetic receptacle. The cost function was combined with the interaction force and tracking errors of the joints. We aim to reduce the cost function by minimally changing the control weight of the gait trajectory generated by the Central Pattern Generator (CPG). The control scheme was primarily based on human-in-the-loop optimization to search for a suitable control weight to regenerate the appropriate gait trajectory. To handle the uncertainties and unknown coupling of the motors, an adaptive law was designed to estimate the unknown parameters of the system. Through a stability analysis, the control framework was verified by semi-globally uniformly ultimately bounded stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. In Case 1, the mean error (MEAN) of the tracking performance was $3.6°$ and $3.3°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance were $2.3°$ and $2.8°$, respectively. In Case 2, the mean error (MEAN) of the tracking performance is $2.7°$ and $3.1°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance are $1.8°$ and $2.4°$, respectively. In Case 3, the mean errors (MEANs) of the tracking performance for subject1 and 2 are $2.4°$, $2.9°$, $3.4°$, and $2.2°$, $2.8°$, $3.1°$, respectively. The minimized mean square errors (MSEs) of the tracking performance for subject1 and 2 were $1.6°$, $2.3°$, $2.6°$, and $1.3°$, $1.7°$, $2.2°$, respectively. Full article

The urgent need for complex terrain adaptability in industrial automation and disaster relief has highlighted the great potential of octopedal wheel-legged robots. However, their design complexity and motion control challenges must be addressed. In this study, an innovative design approach is employed to construct a highly adaptive robot architecture capable of intelligently adjusting the wheel-leg configuration to cope with changing environments. An advanced kinematic analysis and simulation techniques are combined with inverse kinematic algorithms and dynamic planning to achieve a typical ‘Step-Wise Octopedal Dynamic Coordination Gait’ and different gait planning and optimization. The effectiveness of the design and control strategy is verified through the construction of an experimental platform and field tests, significantly improving the robot’s adaptability and mobility in complex terrain. Additionally, an optional integrated quadrotor module with a compact folding mechanism is incorporated, enabling the robot to overcome otherwise impassable obstacles via short-distance flight when ground locomotion is impaired. This achievement not only enriches the theory and methodology of the multi-legged robot design but also establishes a solid foundation for its widespread application in disaster rescue, exploration, and industrial automation. 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

Legged robots face inherent challenges in energy efficiency and stability at high speeds due to the repetitive acceleration–deceleration cycles of swing-based locomotion. To address these limitations, this paper presents a motion strategy that uses rolling gait instead of swing gait to improve the energy efficiency and stability. First, a wheel-legged quadruped robot, R-Taichi, is developed, which is capable of switching to legged, wheeled, and RHex mobile modes. Second, the mechanical structure of the transformable two-degree-of-freedom leg is introduced, and the kinematics is analyzed. Finally, experiments are conducted to generate wheeled, legged, and RHex motion in both swing and rolling gaits, and the energy efficiency is further compared. The experimental results show that the rolling motion can ensure stable ground contact and mitigate cyclic collisions, reducing specific resistance by up to 30% compared with conventional swing gaits, achieving a top speed of 0.7 m/s with enhanced stability (root mean square error (RMSE) reduction of 22% over RHex mode). Furthermore, R-Taichi exhibits robust multi-terrain adaptability, successfully traversing gravel, grass, and obstacles up to 150 mm in height. Full article

Wheel-legged robots combine the advantages of both wheeled robots and traditional quadruped robots, enhancing terrain adaptability but posing higher demands on the perception of foot–ground contact forces. However, existing approaches still suffer from limited accuracy in estimating contact positions and three-dimensional contact forces when dealing with flexible tire–ground interactions. To address this challenge, this study proposes a foot–ground contact state detection technique and optimization method based on multi-sensor fusion and intelligent modeling for wheel-legged robots. First, finite element analysis (FEA) is used to simulate strain distribution under various contact conditions. Combined with global sensitivity analysis (GSA), the optimal placement of PVDF sensors is determined and experimentally validated. Subsequently, under dynamic gait conditions, data collected from the PVDF sensor array are used to predict three-dimensional contact forces through Gaussian process regression (GPR) and artificial neural network (ANN) models. A custom experimental platform is developed to replicate variable gait frequencies and collect dynamic contact data for validation. The results demonstrate that both GPR and ANN models achieve high accuracy in predicting dynamic 3D contact forces, with normalized root mean square error (NRMSE) as low as 8.04%. The models exhibit reliable repeatability and generalization to novel inputs, providing robust technical support for stable contact perception and motion decision-making in complex environments. Full article

Underwater sensor deployment in military applications requires high precision, yet existing robotic solutions often lack the maneuverability and adaptability required for complex aquatic environments. To address this gap, this study proposes a bio-inspired underwater robot modeled after the marine iguana, which exhibits effective crawling and swimming capabilities. The research aims to develop a compact, multi-functional robot capable of precise sensor deployment and environmental detection. The methodology integrates a biomimetic mechanical design—featuring leg-based crawling, tail-driven swimming, a deployable head mechanism, and buoyancy control—with a multi-sensor control system for navigation and data acquisition. Gait and trajectory planning are optimized using kinematic modeling for both terrestrial and aquatic locomotion. Experimental results demonstrate the robot’s ability to perform accurate underwater sensor deployment, validating its potential for military applications. This work provides a novel approach to underwater deployment robotics, bridging the gap between biological inspiration and functional engineering. Full article

This paper presents a learning-assisted approach for state estimation of quadruped robots using observations of proprioceptive sensors, including multiple inertial measurement units (IMUs). Specifically, one body IMU and four additional IMUs attached to each calf link of the robot are used for sensing the dynamics of the body and legs, in addition to joint encoders. The extended Kalman filter (KF) is employed to fuse sensor data to estimate the robot’s states in the world frame and enhance the convergence of the extended KF (EKF). To circumvent the requirements for the measurements from the motion capture (mocap) system or other vision systems, the right-invariant EKF (RI-EKF) is extended to employ the foot IMU measurements for enhanced state estimation, and a learning-based approach is presented to estimate the vision system measurements for the EKF. One-dimensional convolutional neural networks (CNN) are leveraged to estimate required measurements using only the available proprioception data. Experiments on real data from a quadruped robot demonstrate that proprioception can be sufficient for state estimation. The proposed learning-assisted approach, which does not rely on data from vision systems, achieves competitive accuracy compared to EKF using mocap measurements and lower estimation errors than RI-EKF using multi-IMU measurements. Full article

In this article, the current state of the art in the area of autonomously working and mobile robots used for inspections in deep underground mining and exploration is described, and directions for future development are highlighted. The increasing demand for CRMs (critical raw materials) and deeper excavations pose a higher risk for people and require new solutions in the maintenance and inspection of both underground machines and excavations. Mitigation of risks and a reduction in accidents (fatal, serious and light) may be achieved by the implementation of mobile or partly autonomous solutions such as drones for exploration, robots for exploration or initial excavation, etc. This study examines various types of mobile unmanned robots such as ANYmal on legs, robots on a tracked chassis, or flying drones. The main scope of this review is the evaluation of the effectiveness and technological advancement in the aspect of improving safety and efficiency in deep underground and abandoned mines. Notable possibilities are multi-sensor systems or cooperative behaviors in systems which involve many robots. This study also highlights the challenges and difficulties of working and navigating (in an environment where we cannot use GNSS or GPS systems) in deep underground mines. Mobile inspection robots have a major role in transforming underground operations; nevertheless, there are still aspects that need to be developed. Further improvement might focus on increasing autonomy, improving sensor technology, and the integration of robots with existing mining infrastructure. This might lead to safer and more efficient extraction and the SmartMine of the future. Full article

Biological systems can adaptively navigate multi-terrain environments via morphological and behavioral flexibility. While robotic systems increasingly achieve locomotion versatility in one or two domains, integrating terrestrial, aquatic, and aerial mobility into a single platform remains an engineering challenge. This work tackles this by introducing a bipedal robot equipped with a reconfigurable locomotion framework, enabling seven adaptive policies: (1) thrust-assisted jumping, (2) legged crawling, (3) balanced wheeling, (4) tricycle wheeling, (5) paddling-based swimming, (6) air-propelled drifting, and (7) quadcopter flight. Field experiments and indoor statistical tests validated these capabilities. The robot achieved a 3.7-m vertical jump via thrust forces counteracting gravitational forces. A unified paddling mechanism enabled seamless transitions between crawling and swimming modes, allowing amphibious mobility in transitional environments such as riverbanks. The crawling mode demonstrated the traversal on uneven substrates (e.g., medium-density grassland, soft sand, and cobblestones) while generating sufficient push forces for object transport. In contrast, wheeling modes prioritize speed and efficiency on flat terrain. The aquatic locomotion was validated through trials in static water, an open river, and a narrow stream. The flight mode was investigated with the assistance of the jumping mechanism. By bridging terrestrial, aquatic, and aerial locomotion, this platform may have the potential for search-and-rescue and environmental monitoring applications. Full article

Legged robots often encounter the problem that the foot-end steps into empty spaces due to terrain collapse in complex environments such as mine tunnels and coal shafts, which in turn causes body instability. Aiming at this problem, this paper takes the hexapod robot as the research object and proposes a multi-segmented electrically driven single-leg compliance control strategy for robots with tripod and quadrupedal gaits, to reduce the impact when the foot-end touches the ground, and thus to improve the stability of the robot. First, this paper analyzes the kinematic and dynamic models of the multi-segmented electrically driven single leg of the hexapod robot. Then, the minimum tipping angle of the fuselage is obtained based on force-angle stability margin (FASM) and used as the index to design the single-leg pit-probing control algorithm based on position impedance control and the single-leg touchdown force adjustment control algorithm based on inverse dynamics control. Finally, this paper designs a finite state machine to switch between different control strategies of the multi-segmented electrically driven single leg of the hexapod robot, and the vertical dynamic impact characteristic index is applied to evaluate the effect of single-leg impedance control. The simulation and prototype test results show that the proposed method significantly reduces the foot-end touchdown force and improves the walking stability of the hexapod robot in complex environments compared with the multi-segmented electrically driven single leg without the compliance control strategy. Full article

Parallel-kinematic machines or parallel robots have only been established in a few applications where their advantage over serial kinematics due to their high payload capacity, stiffness, or dynamics with their limited workspace-to-installation-space ratio pays out. However, some applications still have not yet been sufficiently or satisfactorily automated in which parallel robots could be advantageous. As their performance is much more dependent on their complex dimensioning, an automated design tool—not existing yet—is required to optimize the parameterization of parallel robots for applications. Combined structural and dimensional synthesis considers all principally possible kinematic structures and performs a separate dimensioning for each to obtain the best task-specific structure. However, this makes the method computationally demanding. The proposed computationally efficient approach for dimensional synthesis extends multi-objective particle swarm optimization with hierarchical constraints. A cascaded (bilevel) optimization includes the design optimization of components and the redundancy resolution for tasks with rotational symmetry, like milling. Two case studies for different end-effector degrees of freedom demonstrate the broad applicability of the combined structural and dimensional synthesis for symmetric parallel robots with rigid links and serial-kinematic leg chains. The framework produces many possible task-optimal structures despite numerous constraints and can be applied to other problems as an open-source Matlab toolbox. Full article