Abstract This paper presents a magnetically tuned quasi-zero-stiffness piezoelectric energy harvester (QZS–PEH) for efficient low-frequency vibration energy capture. The device integrates two piezoelectric cantilevers, a vertical spring, and three permanent magnets. Magnetic repulsion introduces adjustable negative stiffness, enabling a quasi-zero-stiffness region that lowers the effective natural frequency. An electromechanical model is formulated using the Lagrange method, and static and dynamic analyses reveal that magnetic spacing governs the transition among negative-, quasi-zero-, and positive-stiffness states. Experimental simulations show that the QZS–PEH achieves significantly enhanced low-frequency energy harvesting, with a 14.4-fold increase in peak power and a 7.4-fold reduction in resonant frequency compared with a conventional cantilever. The results demonstrate that magnetically induced quasi-zero stiffness is an effective approach for improving low-frequency energy harvesting performance.

This work proposes a bionic excitation strategy in which the impact force harvested by magnetic snap-through instability directly acts on the triboelectric substrate film, thereby significantly enhancing the power output of the bio-inspired energy harvester (BEH). Unlike traditional compression-driven TENGs, the impulsive stress loading shortens the single-pulse force response by approximately 50 ms, as confirmed by synchronized real-time time-voltage measurements. The rapid stress transition leads to a faster transient electrical response and contributes to increased charge output. As a result, the overall BEH achieves a 127% increase in total power output, a 161.5% increase in TENG power output, and a 126.5% increase in PET power output compared with traditional harvesters. These findings demonstrate that bionic abrupt-transition mechanisms can overcome the intrinsic limitations of low-frequency(<30hz) harvesters and unlock higher energy conversion efficiency. Furthermore, the BEH enables wireless temperature and humidity sensing, highlighting its potential for self-powered IoT applications. Overall, this work establishes a generalizable impulsive-excitation paradigm that breaks existing performance bottlenecks and advances the development of next-generation high-efficiency energy harvesting systems.

Biological systems such as mountain goats and felines exhibit remarkable agility and adaptability when traversing complex terrains. Inspired by these capabilities, quadruped robots have been developed to mimic legged locomotion and improve mobility over uneven environments. To further enhance locomotion efficiency and terrain versatility, wheeled-legged robots integrate wheels and legs into a hybrid platform, enabling both high-speed traversal and robust ground contact in unstructured terrain. However, planning coordinated locomotion across diverse terrains remains challenging due to the nonlinear dynamics, complex terrain contact constraints, and multimodal locomotion capabilities. In this paper, we propose a real-time, integrated planning framework that jointly optimizes gait scheduling, footstep placement, and whole-body motion trajectories. Our method adopts a two-stage approach. First, a sampling-based planner generates candidate gait sequences and nominal footstep targets based on terrain features and kinematic feasibility. Second, a constrained trajectory optimizer reformulates the planning problem as a Quadratic Programming (QP) task to compute dynamically feasible base trajectories and corresponding ground reaction forces. This hybrid formulation balances planning efficiency and physical realism. The planned trajectories and contact forces are tracked using a hierarchical control architecture combining Model Predictive Control (MPC) and Whole-Body Control (WBC), enabling fast and stable execution on real hardware. Simulation and real-world experiments demonstrate that our approach enables adaptive gait transitions and improves terrain adaptability compared to traditional planners.

Variable stiffness endows continuum robots with both compliance and tunable rigidity, making them promising alternatives to traditional rigid manipulators in confined and unstructured environments. Over the past decade, great progress has been made in variable stiffness technologies involving structural design, actuation, modeling, and control. However, current research is fragmented and mostly focuses on individual aspects, lacking a systematic review and a unified framework integrating structure, modeling, and control. This paper presents a comprehensive review of variable stiffness in continuum robots, emphasizing the interrelationships among stiffness principles, modeling, and control strategies. We summarize classical and emerging variable stiffness methods, analyze their integration with control approaches, and evaluate the evolution of control strategies, especially multi-modal fusion of actuation, sensing, and control. Such fusion can improve control accuracy and robustness in human-centered environments and is regarded as a key driver for next-generation intelligent continuum robots. Finally, we outline future directions, highlight the “actuation–stiffness–control” paradigm, and discuss existing challenges and open research opportunities for high-performance intelligent control.

This study aims to develop a “sensing-actuation integrated” intelligent pressurized thigh band to assist the quadriceps, indirectly alleviate knee joint load, and achieve high-precision recognition of movement modes. The system comprises a portable integrated controller and a textile-integrated flexible pneumatic actuator. Experiments were conducted to evaluate the effects of different air bladder pressure conditions on metabolic rate and muscle activity. Simultaneously, pneumatic data corresponding to six common activities were collected, and a lightweight deep learning model was developed to enable high-precision motion classification. Finally, the model was deployed to an embedded platform to demonstrate its application potential. Results indicate that appropriate air bladder pressure significantly reduces quadriceps muscle activation and average metabolic cost. Furthermore, the deep learning model achieved 99.17% accuracy in recognizing the six activities and was successfully deployed to the embedded platform. This study validates the effectiveness of the intelligent pressurized thigh band in improving locomotor performance under static pressures and demonstrates the potential of air bladder pressure variations as a proxy indicator for movement intent for future closed-loop control.

BACKGROUND:Continuum manipulators have exceptional bending capabilities and gentle interaction with surrounding tissues; these manipulators have been widely adopted in medical applications. In single-port surgeries, the surgical field is narrow and densely populated with tissues and nerves, often extending beyond the continuum manipulator's dexterous workspace. METHODS:This study proposes a control strategy designed to balance the trade-off between high dexterity and the spatial limitations of the non-dexterous workspace. This strategy employs Jacobian-based differential kinematics to compute solutions, thereby expanding the reachable workspace boundaries based on dexterity. By integrating the concept of spatial solid angles, the dexterity of the continuum manipulator within the reachable workspace is intuitively analysed. RESULTS:Experimental results indicate that the proposed method can effectively expand the workspace and enhance fault tolerance in spatial matching. CONCLUSIONS:The system maintains high trajectory-tracking consistency and orientation continuity within the non-dexterous workspace, thereby improving the accuracy and stability of teleoperation.

This paper introduces a novel knee exoskeleton designed to provide assistive torque to the knee joint and facilitate postoperative rehabilitation.Drawing inspiration from the anatomy of the human knee joint,the exoskeleton incorporates a biomimetic structure that integrates the"optimal axis of rotation"with the trajectory of the instantaneous center of rotation.This design effectively mitigates the issue of kinematic misalignment in the human‒machine joint,offering a more comfortable wearing experience for users.Furthermore,a highly integrated quasi-direct drive system is incorporated into the knee exoskeleton.The drive system can deliver a continuous torque of 18 N·m and a peak torque of 45 N·m to sufficiently meet the daily walking needs of patients.From a control perspective,a unified sliding mode control strategy based on iterative learning control is proposed to maintain high tracking accuracy.This strategy enables the system to achieve accurate tracking of the knee motion trajectory by continuously updating the control inputs through successive gait cycles.Experimental results demonstrate that the designed biomimetic joint mechanism effectively resolves the issue of misalignment in the human‒machine joint.Furthermore,the synergistic control strategy demonstrates excellent steady-state precision and transient robustness in terms of trajectory tracking during human‒exoskeleton cooperative walking.

Miniature turbojet engines are critical components in emerging low-altitude aerial systems, where accurate and adaptive control is required due to nonlinear and time-varying dynamics. This paper proposes a reinforcement learning-based control approach for turbojet engine speed regulation using proximal policy optimization. The controller is trained in simulation with an optimal control-inspired reward design incorporating tracking accuracy, control effort constraints, and action smoothness. The learned policy is directly deployed onto real engine systems without additional fine-tuning. Experimental validation is conducted on multiple engines to evaluate cross-engine generalization, and dynamic performance is further assessed under stepwise throttle inputs across a wide operating range. Both simulation and real-world results demonstrate that the proposed controller achieves consistently faster transient response than the onboard Proportional-Integral-Derivative (PID) controller while maintaining smooth and stable actuation. Under low spool speed operating conditions, the rise time is reduced to approximately 0.55 s, compared to 2.52 s for the PID controller, representing a reduction of approximately 78%. The controller also exhibits strong robustness to measurement noise and adapts effectively to unseen operating conditions. These results demonstrate the effectiveness of the proposed approach for real-time turbojet engine control and highlight the potential of reinforcement learning in safety-critical nonlinear systems.

This paper presents a novel multi-robot coverage path planning (MCPP) algorithm focusing on large-scale indoor closed obstacle-constrained area. We enhance traditional spanning tree coverage (STC) algorithm with backtracking for energy saving by implementing back-end optimization based on chaotic mapping to reduce the number of turns. The proposed method simplifies the non-deterministic polynomial hard (NP-hard) problem into an NP problem by stochastic optimization method, ensuring that the computational complexity remains manageable in a large-scale environment. To substantiate the feasibility of the proposed method, we have developed a technical framework for algorithm deployment and conducted real-world experiments on a custom platform.

To address the challenges of low-frequency vibration isolation and energy harvesting in engineering environments, this study proposes a magnet-piezoelectric cantilever beam-based dualfunctional quasi-zero stiffness vibration isolation and energy harvesting device (MQZSI-EH). The magnetic repulsive force between the cantilever-tip magnet and the platform-mounted magnet induces quasi-zero stiffness (QZS) characteristics, thereby reducing the isolation onset frequency while simultaneously enhancing the cantilever bending response under low-frequency excitation to improve energy harvesting performance. An electromechanical coupling model is established to investigate the effects of magnet spacing and cantilever length on the stiffness characteristics. The harmonic balance method is employed to analyze the influence of key parameters on vibration isolation and energy harvesting performance. Experimental results validate the theoretical predictions. Compared with a conventional linear isolator, the proposed device achieves an isolation onset frequency of approximately 7.45 Hz, representing a reduction of about 50%, along with a significant decrease in peak force transmissibility. Moreover, a maximum output power of approximately 0.6 mW is achieved under low-frequency excitation, which is about 350% higher than that of a cantilever-beam harvester. This study provides a new approach for low-frequency vibration isolation and energy harvesting.

Existing continuum manipulators excel at operations because of their omnidirectional bending capabilities. However, their operational performance is constrained by the lack of torsional function along their backbone curves. This paper addresses this issue by proposing a pose reconstruction control strategy that has been successfully applied to tendon-driven continuum manipulators with specific structural designs. This strategy achieves high-precision pose control through bend-torsion decoupling. Additionally, we introduce a model-based backlash function to address the non-linear relationship between the end effector and the actuators and to improve positional and directional accuracy during pose reconstruction. By incorporating indirect rotational compensation and separating control variables, the proposed method enables the manipulator to perform torsional functions along its backbone curves while ensuring applicability to different structural configurations. Simulation and experimental results confirm the efficacy of the proposed method: indirect rotational compensation reduces positional and directional errors by 30.76% and 87.29%, respectively. These findings demonstrate that the proposed approach provides a robust solution for pose reconstruction and high-precision manipulation with continuum manipulators in minimally invasive surgery.

Grating displacement sensing is regarded as one of the key technologies for achieving cross-scale nanopositioning. This paper proposes a real-time grating sensing correction and compensation technology to enhance the performance of the embedded grating displacement sensor in Cross-scale piezoelectric actuators (CSPAs), thereby enabling nano-scale motion positioning of CSPAs. Firstly, based on the principle of diffracted image reflection, a miniaturized grating sensing unit that can be monolithically integrated with the CSPA structure is designed. Secondly, an online self-correction algorithm based on amplitude iteration is proposed to dynamically eliminate DC offset and amplitude imbalance errors in the signals. Furthermore, a real-time error compensation strategy is constructed to compensate for inherent periodic errors and measurement lag errors of the system induced by stick-slip effects. Experimental results demonstrate that, with the proposed technology, the embedded grating displacement sensor can achieve a detection resolution of 0.9 nm within the full stroke. The CSPA integrated with this sensor achieved a positioning accuracy within +/- 1.3 nm over its scanning range, and a full-stroke bidirectional positioning consistency of 3.093 +/- 1.358 nm.

Low-Mach thrust vectoring control for micro turbojet powered low-altitude manned air vehicles requires an externally attached conical vector nozzle that redirects the exhaust flow. However, the geometric rules governing the compromise between thrust retention and vectoring effectiveness remain insufficiently quantified. In this study, a three-dimensional steady compressible Reynolds-averaged Navier-Stokes model was established for the engine aft section, vector nozzle, and near-field flow region. The diameter ratio 7D and length-to-diameter ratio 7L were used to characterize the outlet expansion level and streamwise development length of the vector nozzle, respectively. A total of 128 non-deflected configurations were first calculated, and representative deflected cases were further analyzed at B = 5 degrees, 10 degrees, 15 degrees, 20 degrees. The numerical setup was supported by grid independence, engineering consistency, and turbulence model sensitivity checks. The results show that the nozzle thrust first increases and then decreases with increasing 7D, with the maximum thrust appearing at 7D = 1.0 similar to 1.2 under non-deflected conditions. The thrust also exhibits a non-monotonic variation with 7L, indicating that outlet expansion and internal flow development must be geometrically matched. Under deflected conditions, the thrust optimal and thrust vectoring efficiency optimal 7D are different: 7D approximate to 1.2 favors thrust retention, whereas 7D approximate to 0.8 improves flow turning and vectoring efficiency. It provides a favorable balance between wall guided momentum redirection and suppression of separated asymmetric flow at 7L approximate to 1.8. Flow field analysis indicates that the performance variation is governed by outlet expansion matching, asymmetric pressure redistribution, wall attachment, and separation development. Within the investigated operating and geometric range, 7D = 0.8 and 7L = 1.8 are identified as a favorable design candidate rather than a universal optimum, providing an important reference for vector nozzle parameter selection in micro turbojet vector control applications.

Wearable electronic systems require compact and sustainable power sources together with reliable motion-sensing functions. This study presents a gear-driven plantar energy harvester that integrates biomechanical energy conversion with self-sensing locomotion recognition. The device converts low-frequency vertical foot loading into rotary motion through a wedge–lever transmission and amplifies the rotational speed using a multistage gear train with a total transmission ratio of 12. A one-way bearing enables directional power transmission during loading and prevents reverse rotation during recovery. The generated voltage serves both as the electrical output and as the sensing signal for locomotion recognition. Human-subject experiments were conducted under six locomotion modes: walking at 1, 2 and 3 m/s; running; ascending; and descending. Voltage signals were sampled at 2000 Hz and segmented into overlapping sequences. A CNN–LSTM model was used to extract local waveform features and temporal dependencies from the nonstationary signals. The model achieved an overall recognition accuracy of 98.8%, with most errors occurring between ascending and descending. The results demonstrate that a single plantar device can simultaneously harvest biomechanical energy and provide motion-related information, offering a compact solution for integrated energy harvesting and self-sensing in wearable systems.

This article presents a novel gait generation method and a flexible adaptive switching controller for a lower limb exoskeleton. The proposed Gaussian iterative dynamic movement primitives (GIDMP) gait generation method consists of three main components: a Gaussian distribution function based on the wearer’s joint angle data; an iterative process for updating the Gaussian distribution; and the generation of a personalized motion trajectory. Compared with traditional dynamic movement primitives, the GIDMP method optimizes the dynamic generation of trajectory and reduces computational requirements. The switching control strategy dynamically adjusts torque based on the exoskeleton’s current position and velocity. Experimental results confirm the stability and accuracy of gait learning and demonstrate the controller’s excellent assistance capability. Respirometry, pressure, and surface electromyography tests from five participants indicate that the proposed system reduces the average total metabolic by 18.77%, the average pressure by 66.73%, and decreases muscle activation in the rectus femoris, semitendinosus, and soleus by 14.82%, 28.80%, and 15.67%, respectively.

Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios.

Six-degree-of-freedom (6-DOF) nanopositioning stages are indispensable in precision engineering. However, these stages currently exhibit significant crosstalk, which degrades their accuracy. This study proposes a kinematically decoupled 6-DOF nanopositioning stage with minimized crosstalk based on flexure hinges, and its conceptual design, modelling, and experimental investigation are described. First, the working principle of the stage is introduced, followed by its design mechanism with flexure hinges. Second, its stiffness model is established using Castigliano's second theorem, which is then utilized for the optimization design. Finally, an experimental study conducted based on the fabricated prototype is described. The results reveal that the positioning stage features a resolution better than 20 nm, 0.07 mu rad , and set-point tracking accuracy better than 0.029 mu m and 0.192 mu rad for translation and rotation, respectively. Most importantly, its static single-axis crosstalk over the full range is less than 0.81%, and its dynamic crosstalk is reduced to less than 0.103 mu m and 0.778 mu rad , using a simple proportional-integral-derivative (PID) controller and quintic polynomial trajectory planning, respectively.

Multi-agent trajectory prediction plays an increasingly critical role in intelligent transportation systems. Despite significant progress in this field, several key challenges remain unresolved. Future trajectories of agents are jointly influenced by individual behavior patterns and the surrounding environment, while most existing methods extract temporal features at a single time scale, limiting their capacity to capture complex temporal dependencies within trajectory sequences. Moreover, many current approaches employ numerically precise formulations for interaction modeling, which are misaligned with the imprecise nature of real-world social behavior. To address these limitations, we propose a multi-agent trajectory prediction model that combines multi-scale temporal encoding and egocentric scan-based spatial representation. Temporally, we leverage a sliding-window-based multi-scale temporal encoder to capture trajectory features across diverse time scales. Spatially, we partition the surrounding environment into multiple egocentric bins to represent social zones, thereby simulating real-world interaction patterns. Experimental results on public benchmark datasets ETH/UCY and SDD demonstrate that our model outperforms existing approaches.

Soft manipulators have garnered significant research attention in recent years due to their flexibility and adaptability. However, the inherent flexibility of these manipulators imposes limitations on their load-bearing capacity and stability. To address this, this study compares various variable stiffness technologies and proposes a novel design concept: leveraging the phase-change characteristics of low-melting-point alloys (LMPAs) with distinct melting points to fulfill the variable stiffness requirements of soft manipulators. The pneumatic structure of the manipulator is fabricated via 3D-printed molds and silicone casting. The manipulator integrates a pneumatic working chamber, variable stiffness chambers, heating devices, sensors, and a central channel, achieving multi-stage variable stiffness through controlled heating of the LMPAs. A steady-state temperature field distribution model is established based on the integral form of Fourier’s law, complemented by finite element analysis (FEA). Subsequently, the operational temperatures at which the variable stiffness mechanism activates, and the bending performance are experimentally validated. Finally, stiffness characterization and kinematic performance experiments are conducted to evaluate the manipulator’s variable stiffness capabilities and flexibility. This design enables the manipulator to switch among low, medium, and high stiffness levels, balancing flexibility and stability, and provides a new paradigm for the design of soft manipulators.

Six-degree-of-freedom (6-DOF) nanopositioning stages are indispensable in precision engineering. However, these stages currently exhibit significant crosstalk, which degrades their accuracy. This study proposes a kinematically decoupled 6-DOF nanopositioning stage with minimized crosstalk based on flexure hinges, and its conceptual design, modelling, and experimental investigation are described. First, the working principle of the stage is introduced, followed by its design mechanism with flexure hinges. Second, its stiffness model is established using Castigliano’s second theorem, which is then utilized for the optimization design. Finally, an experimental study conducted based on the fabricated prototype is described. The results reveal that the positioning stage features a resolution better than 20 nm, 0.07μrad, and set-point tracking accuracy better than 0.029μm and 0.192μrad for translation and rotation, respectively. Most importantly, its static single-axis crosstalk over the full range is less than 0.81%, and its dynamic crosstalk is reduced to less than 0.103μm and 0.778μrad, using a simple proportional–integral–derivative (PID) controller and quintic polynomial trajectory planning, respectively.