Search Results (141)

Line-driven soft robots possess inherent advantages in terms of cushioning and terrain adaptability, but the controllable deformation design of line-driven structures and its coordination mechanism with the overall robot motion remain insufficiently studied. To fill this gap, this paper designs a line-driven hexapod soft robot that achieves directional bending of flexible legs through unilateral line traction, combined with triangular gait co-motion and ROS-based multi-sensor perception. Integrating leg deformation as part of the motion mechanism enables the robot to achieve straight-line and turning movements while maintaining structural compliance. This paper establishes the mapping relationship between the leg actuation space, configuration space, and task space, constructs a kinematic model, and uses the finite element method to analyze leg deformation and stress distribution. Based on this, a robot prototype is built, and a ROS-based distributed control and perception system is constructed, utilizing LiDAR, camera, and attitude sensor data to achieve SLAM and state monitoring. Experimental results show that the robot can achieve continuous motion with an average speed of 15.32 mm/s and a turning angle of 4.75° in a single gait cycle. The feasibility of line-driven structure control based on unilateral traction was verified, and a reference was provided for the design of soft robots oriented towards environmental perception. Full article

Two-electron oxygen reduction reaction (2e⁻-ORR) represents a sustainable and energy-efficient approach for decentralized hydrogen peroxide (H₂O₂) production compared with the conventional anthraquinone process. Among various electrocatalysts, metal–nitrogen–carbon (M–N–C) materials have attracted extensive attention owing to their tunable electronic structures and favorable *OOH adsorption behavior. However, the uncontrolled pyrolysis process generally leads to structurally heterogeneous and ill-defined coordination environments, making it difficult to precisely regulate active sites and understand catalytic mechanisms. Herein, we report a single-atom catalyst (CoN@OCNT) featuring spatially confined pyridinic-N-coordinated Co single sites, synthesized by anchoring a well-defined hexapod terpyridine Co-precursor onto oxidized carbon nanotubes (OCNTs) to suppress metal aggregation during pyrolysis. Benefiting from the optimized coordination environment and enhanced mass/electron transfer, the CoN@OCNT catalyst exhibits nearly 100% H₂O₂ selectivity over a wide potential window from −1.0 to 0.66 V versus RHE in neutral electrolyte. In situ FT-IR and Raman spectroscopy reveal a rapid *OOH-mediated reaction pathway during the 2e⁻-ORR process. Furthermore, membrane electrode assembly (MEA) testing demonstrates an H₂O₂ production rate of 21.8 mol h⁻¹ g_cat⁻¹ with stable operation over 80 h at 60 mA cm⁻². Remarkably, at an industrially relevant current density of 300 mA cm⁻², the catalyst achieves a record H₂O₂ production rate of 70.3 mol h⁻¹ g_cat⁻¹ and a salt-free H₂O₂ concentration of 9.4 mM, highlighting its great potential for practical large-scale H₂O₂ electrosynthesis in neutral media. Full article

Hexapod robots are attractive for operation in cluttered and uneven environments, but their walking stability is strongly affected by the coupled effects of leg morphology and foot-end trajectory planning. In many existing designs, leg-segment proportions, reachable workspace, and swing-phase trajectory smoothness are considered separately, which makes it difficult to clarify how structural parameters and motion planning jointly influence locomotion stability. To address this issue, this study presents a spider-leg-inspired hexapod robot with a simplified three-degree-of-freedom leg configuration. Selected functional characteristics of spider legs, including segmented limb structure and compliant distal contact, were abstracted into an engineering-feasible hexapod platform rather than directly reproducing spider anatomy. A parametric workspace analysis was conducted under a fixed total leg length to compare six candidate femur-to-tibia ratios. Based on forward reach, vertical foot-lifting capability, stride potential, and structural compactness, a 4:6 femur-to-tibia ratio was selected. In addition, an eleventh-order Bézier curve was developed for swing-phase foot trajectory planning and compared with a conventional composite cycloid trajectory under identical tripod-gait conditions. Simulation and straight-line walking experiments showed that the Bézier-based trajectory reduced body-attitude fluctuation and produced smoother angular-velocity variation than the composite cycloid trajectory. The results indicate that the proposed structural design and Bézier-based trajectory can improve flat-ground walking stability of the hexapod robot. This work provides a practical reference for biomimetic structural design and gait-trajectory optimization of multi-legged robots, while further validation on more complex terrain remains necessary. Full article

It has long been hypothesized that some arthropod lineages transitioned to land by following an interstitial pathway through the spaces between sand grains. In recent years, various molecular phylogenetic analyses suggest a greater number of terrestrialization events within Arthropoda than previously hypothesized. The relative importance of an interstitial route to land is likely to have been underestimated because of biases in the fossil record and the choice of techniques used for collecting extant arthropods from sands and other types of mineral regolith (sediment with low organic content). A number of early-branching taxa are microarthropods that are common in mineral regolith, providing phyloecological evidence for an interstitial pathway onto land. Following interstitial terrestrialization, hexapods and early-branching arachnids may have remained minute and soft-bodied within mineral regolith until the Early Devonian, when organically rich soils developed on much of the land surface, resulting in increased food resources but also increased rates of predation. This led to defensive modifications and increases in surface abundance and body size, which would have all elevated the probability of fossilization. Full article

This manuscript presents the development of an HEX platform inverse kinematics model, its numerical implementation, and experimental validation. A complete inverse-kinematics formulation is established from the geometric definition of the base and mobile joint coordinates and a z–y–x Euler rotation sequence, allowing actuator-length computation for arbitrary 6-DOF poses. The model is implemented to map the operational workspace under actuator stroke and joint-angle constraints via a two-stage deterministic search, providing dense workspace point clouds, surfaces, and quantitative translational/rotational limits for multiple stroke ranges. Experimental validation is performed on a hexapod platform controlled through an embedded inverse-kinematics layer within a cascaded position–velocity–current architecture with dual-encoder actuator feedback. For a ±25 mm actuator travel range, the experiments confirm close agreement with translation simulations with differences of the order of 2% to 3% in x, y, and z, while larger discrepancies were observed in orientation limits, i.e., the model predicts γ ≈ ±32.5° and α, β ≈ ±10–11°, whereas measurements yield γ ≈ ±30° and α,β ≈ ±14–15°, evidencing higher sensitivity of rotational capability to real mechanical and control factors. Full article

Tuned liquid dampers (TLDs) are widely used in structural vibration mitigation, but they are limited by their damping frequency to use as passive damping equipment. To enhance the damping performance of the conventional TLD, a unique layered tuned liquid damper (LTLD) filled with water and diesel is proposed. The interfacial wave coupling mechanism for broadband energy dissipation has not been previously explored in sloshing-type dampers. A series of frequency-sweeping tests were carried out in the laboratory to compare the vibration suppression performance of the proposed LTLD against conventional TLD. The dampers were installed on an elastic supporting structural platform (SSP) with a height of one meter, and the bottom was horizontally excited with different amplitudes and frequencies using a hexapod motion simulator. The results indicate that the LTLD showed a better damping performance than the TLD under small-amplitude excitation and achieved optimization at two peaks. The separation surface movement dissipated the liquid motion’s energy and enhanced the hydrodynamic force in the horizontal direction. However, the damping effect of the LTLD weakened when the two liquids were no longer immiscible under large-amplitude excitation. Therefore, we recommend utilizing the LTLD to improve structural damping performance when $d_{max}/L$< 0.04984. In addition, the LTLD reduced the maximum wall pressure by about 25% in the transient state under large-amplitude excitation. This study presents experimental evidence that a water–diesel LTLD achieves broadband damping through interfacial wave coupling. The stable interfacial waves enhance energy dissipation and excite new vibration mitigation frequencies, offering a novel approach to overcoming the narrow-band limitation of conventional TLD. Full article

Exploring the lunar complex and extreme terrain presents formidable challenges for conventional lunar rovers. To address these limitations, this study proposes a novel hexapod jumping hybrid robot that incorporates a “figure-of-eight” (butterfly-shaped) six-branched wheel-legged mechanism and a jumping system that stores elastic energy via deformation of its elastic body. Inspired by the multimodal locomotion of grasshoppers, the robot dynamically switches between two operational modes: high-efficiency wheeled locomotion on relatively flat surfaces and agile jumping to traverse steep slopes and surmount large obstacles. A bio-inspired gait, inspired by the crawling patterns of a hexapod insect, is implemented using a Central Pattern Generator (CPG)-based controller to produce coordinated, rhythmic limb movements. Dynamic simulations of the jumping mechanism were conducted to optimize the critical parameters of the elastic structure and its associated control strategy. Experiments on a physical prototype were conducted to validate the robot’s wheeled mobility and jumping performance. The results demonstrate that the robot exhibits excellent adaptability to rugged terrains and obstacle-dense environments. The integration of multimodal locomotion and adaptive gait control significantly enhances the robot’s operational robustness and survivability in the harsh lunar environment, opening new possibilities for future lunar exploration missions. Full article

Hexapod walking robots are a subject of intense research in the existing literature. To move effectively in natural terrain, these robots must be able to adapt to surface irregularities. While most existing designs employ sophisticated technical solutions for the leg mechanisms, none of these projects allow for combined roll and pitch movements of the body segments. This paper addresses this gap, presenting the concept of a hexapod robot with a body formed of three segments connected by two active universal joints. This unique architecture allows the robot to locomote on both sides and autonomously recover from a rollover event. The robot’s legs are underactuated, utilizing a passive spring element to simplify the mechanical design and control system while maintaining effective terrain adaptation capabilities. Experimental results are presented and discussed, validating the theoretical model and demonstrating the effectiveness of the proposed solution on varied terrains. Full article

To achieve high-precision trajectory tracking control for an underwater hexapod robot, this paper proposes a hierarchical control architecture. Firstly, a multi-rigid-body dynamic model for the robot is established based on the Newton-Euler method and reasonably simplified. Secondly, a Central Pattern Generator (CPG) network with the Hopf oscillator as its core is designed to generate stable and coordinated crawling gaits. By introducing a steering parameter, a kinematic model connecting the CPG output is constructed. Furthermore, based on this dynamic and kinematic model, an upper-layer Model Predictive Controller (MPC) is designed. The optimized control quantities output by the MPC are mapped into the rhythmic parameters of the CPG network via a transfer function established by fitting experimental data, thus forming the complete MPC-CPG controller. Finally, the proposed method is validated through simulations of circular trajectory tracking. The results show that even in the presence of initial errors, the controller can converge rapidly, with trajectory position error consistently maintained within −0.1 m~0.1 m, and heading angle error confined to the range of −15~15°. The experiments fully demonstrate the effectiveness of the proposed MPC-CPG controller in ensuring trajectory tracking accuracy, motion smoothness, and system stability. Full article

This paper presents the experimental validation of a computational kinematic model for a passive Stewart–Gough platform equipped with modified Cardan joints. The introduced joint geometry significantly increases structural stiffness but invalidates the standard spherical joint assumption commonly used in hexapod kinematic formulations. To address this, we develop an efficient numerical optimization-based framework that solves both the direct and inverse kinematics without relying on simplified joint models. Furthermore, to enable autonomous and absolute pose measurement, each cylindrical leg joint of the platform is equipped with a LinACE™ absolute linear encoder. This sensor integration transforms the passive mechanism into an IoT-enabled reconfigurable fixture capable of internally sensing, tracking, and recalling its own configuration. The direct kinematics are computed iteratively using a homogeneous transformation formulation and benchmarked against analytical models derived for ideal spherical joints. Experimental results demonstrate sub-millimeter accuracy in pose estimation, confirming the validity of the proposed kinematic model and highlighting the suitability of the sensor-equipped hexapod for industrial flexible fixturing applications. Full article

Recent advances in legged robotics have highlighted deep reinforcement learning (DRL)-based controllers for their robust adaptability to diverse, unstructured environments. While position-based DRL controllers achieve high tracking accuracy, they offer limited disturbance rejection, which degrades walking stability; torque-based DRL controllers can mitigate this issue but typically require extensive time and trial-and-error to converge. To address these challenges, we propose a Real-Time Motion Generator (RTMG). At each time step, RTMG kinematically synthesizes end-effector trajectories from target translational and angular velocities (yaw rate) and step length, then maps them to joint angles via inverse kinematics to produce imitation data. The RL agent uses this imitation data as a torque bias, which is gradually annealed during training to enable fully autonomous behavior. We further combine the RTMG-generated imitation data with a decaying action priors scheme to ensure both initial stability and motion diversity. The proposed training pipeline, implemented in NVIDIA Isaac Gym with Proximal Policy Optimization (PPO), reliably converges to the target gait pattern. The trained controller is Tensor RT-optimized and runs at 50 Hz on a Jetson Nano; relative to a position-based baseline, torso oscillation is reduced by 24.88% in simulation and 21.24% on hardware, demonstrating the effectiveness of the approach. Full article

The underwater hexapod robot has advantages such as lower energy consumption and reduced environmental interference compared to ROVs and AUVs. The foot-end contact detection with the seabed is the key technology for adapting to complex terrains. This paper focuses on the ‘Dragon Crab’ underwater hexapod robot developed by Shanghai Jiao Tong University and proposes an indirect detection method that does not require foot-end contact sensors. By establishing the kinematic and dynamic models of the robot’s legs, combined with multi-order polynomial trajectory planning to reduce non-contact force interference, the foot-contact determination condition is defined. Through simulation experiments and force analysis of the legs, the contact detection parameters are estimated. Then, single-leg contact tests are conducted to obtain joint motor torque variation curves and foot-end height variation curves through the kinematic model, verifying the proposed contact detection conditions and parameters. Finally, the method is applied to underwater obstacle-crossing experiments of the underwater hexapod robot using triangular and wave gait patterns. Experimental results show that the method can accurately identify the foot-end contact state and has high applicability in complex underwater terrains. Full article

Dynamic errors in robotic machining can degrade part quality, particularly in flexible platforms that are susceptible to both geometric and inertial disturbances. This work introduces a data-driven digital twin for pointwise prediction of circular trajectory errors in a hexapod-based machining cell, using a compact sensing configuration that combines ballbar measurements with tri-axial vibration signals. Deviations measured by ballbar, acceleration data, and CMM-measured profiles are synchronized in the angular domain via a unified pipeline for denoising, resampling, and phase alignment. Sliding-window vibration statistics and the ballbar path error are used as inputs to XGBoost, multilayer perceptron, and random forest regressors. Model performance is evaluated under a deployment-relevant leave-one-run-out protocol and a conventional random 70:30 point split. XGBoost achieves micrometer-level accuracy on unseen runs, with RMSE around 5 µm, R² exceeding 0.80, and near-complete coverage within a ±20 µm tolerance band. Compared to baseline models, it also provides improved suppression of extreme residuals. Feature importance and ablation studies show that the ballbar path error captures the dominant geometric component, while compact hybrid feature sets—combining this anchor with selected vibration descriptors—retain most of the predictive accuracy and enable practical offline batch-level compensation. Full article

By emulating the morphological structures of organisms, bionic robots achieve enhanced locomotion efficiency, stability, and environmental adaptability. Inspired by insect morphology and biological locomotion mechanisms, a wheel-legged transformation device for a hexapedal robot is proposed in this work. First, an iris-type wheel-legged transformation mechanism is designed. Subsequently, the operational principle of the iris–link composite mechanism is analyzed, and kinematic modeling of the transformation process is conducted. Finally, joint angle rotation, positional variation, and their effects under different gait states are examined through simulation of three typical gait patterns. Experimental results demonstrate that the proposed design significantly improves the motion stability of the bionic hexapedal robot. Furthermore, the adoption of a hollow leg structure reduces weight while enhancing locomotion flexibility, thereby strengthening the robot’s overall capability to respond to external disturbances. In summary, this study offers a valuable reference for the future development of wheel-legged transformable bionic robots. Full article

In field exploration, disaster rescue, and complex terrain operations, the accuracy of ground type recognition directly affects the walking stability and task execution efficiency of legged robots. To address the problem of terrain recognition in complex ground environments, this paper proposes a high-precision classification method based on single-leg triaxial force signals. The method first employs a one-dimensional convolutional neural network (1D-CNN) module to extract local temporal features, then introduces a long short-term memory (LSTM) network to model long-term and short-term dependencies during ground contact, and incorporates a convolutional block attention module (CBAM) to adaptively enhance the feature responses of critical channels and time steps, thereby improving discriminative capability. In addition, an improved whale optimization algorithm (iBWOA) is adopted to automatically perform global search and optimization of key hyperparameters, including the number of convolution kernels, the number of LSTM units, and the dropout rate, to achieve the optimal training configuration. Experimental results demonstrate that the proposed method achieves excellent classification performance on five typical ground types—grass, cement, gravel, soil, and sand—under varying slope and force conditions, with an overall classification accuracy of 96.94%. Notably, it maintains high recognition accuracy even between ground types with similar contact mechanical properties, such as soil vs. grass and gravel vs. sand. This study provides a reliable perception foundation and technical support for terrain-adaptive control and motion strategy optimization of legged robots in real-world environments. Full article