Visual simultaneous localization and mapping (SLAM) is of great significance for flapping-wing flying robots (FWFRs) to enhance their autonomous navigation capabilities in complex environments. However, during the motion of FWFRs, there are intense image vibrations accompanied by significant illumination changes, which would prevent existing visual SLAM algorithms from being directly applied to FWFRs. Therefore, this paper proposes a modified ORB-SLAM3 algorithm called FW-ORB-SLAM for FWFRs. First, we adopt the fast Fourier transform (FFT) method to map the original images to the frequency domain. Then, based on the characteristic flapping motion of the FWFR, we decompose the frequency domain jitter to obtain stabilized images. Moreover, to mitigate the impact of illumination variations on feature point tracking during outdoor flight, a local adaptive contrast enhancement method is proposed, which enhances the stability of feature point tracking and augments the robustness of the SLAM algorithm. Finally, flight experiments carried out using our self-developed FWFR named U-Dove demonstrate that FW-ORB-SLAM outperforms the state-of-the-art ORB-SLAM3 algorithm, which provides insights into performing vision-based SLAM tasks for the FWFR.

This paper investigates fixed-time fault-tolerant tracking control for an uncertain robot in the presence of non-affine actuator faults. A broad learning-based adaptive control framework is developed to estimate uncertainties by integrating an improved radial basis function neural network into the broad learning architecture. On this basis, a fixed-time sliding-mode fault-tolerant controller is synthesized to compensate for actuator effectiveness degradation and improve closed-loop robustness. It is rigorously shown that all closed-loop signals remain bounded and that the tracking error converges to a compact neighborhood of the origin within a fixed settling time independent of initial conditions. The simulation studies carried out demonstrate the effectiveness, robustness, and fixed-time convergence capability of the proposed method.

Robotic assistance in scientific laboratories requires procedurally correct long-horizon manipulation, reliable execution under limited supervision, and robustness in low-demonstration regimes. Such conditions greatly challenge end-to-end vision-language-action (VLA) models, whose assumptions of recoverable errors and data-driven policy learning often break down in protocol-sensitive experiments. We propose CAPER, a framework for Constrained And ProcEdural Reasoning for robotic scientific experiments, which explicitly restricts where learning and reasoning occur in the planning and control pipeline. Rather than strengthening end-to-end policies, CAPER enforces a responsibility-separated structure: task-level reasoning generates procedurally valid action sequences under explicit constraints, mid-level multimodal grounding realizes subtasks without delegating spatial decision-making to large language models, and low-level control adapts to physical uncertainty via reinforcement learning with minimal demonstrations. By encoding procedural commitments through interpretable intermediate representations, CAPER prevents execution-time violations of experimental logic, improving controllability, robustness, and data efficiency. Experiments on a scientific workflow benchmark and a public long-horizon manipulation dataset demonstrate consistent improvements in success rate and procedural correctness, particularly in low-data and long-horizon settings.

This paper designs an obstacle avoidance and path planning method for flapping-wing flying robots (FWFRs). It is consisted of a multi-sensor-fusion-based obstacle detection and localization algorithm, and a path planning algorithm that incorporates the motion constraints of FWFRs. The obstacle detection and localization algorithm estimates the spatial coordinates of obstacles by fusing data from a monocular camera, a GPS module, an inertial measurement unit, and a laser ranging sensor. Then, the path planning algorithm generates the desired flight path based on the identified obstacle positions. The designed method is validated through experiments conducted on a dove-inspired FWFR weighing 210 g with an 80 cm wingspan. The experimental results demonstrate the effectiveness of the designed obstacle avoidance and path planning method.

To address the limited maneuverability and low energy efficiency of autonomous aerial vehicles (AAVs) in confined spaces, we design and implement the Hybrid Sprawl-Tuned Vehicle (HSTV) - a deformable multi-modal robotic platform specifically engineered for operation in complex and spatially constrained environments. Based on the "FSTAR" platform, HSTV is equipped with passive front wheels and actively driven rear wheels. The gear transmission mechanism enables the actively driven wheels to be driven without the need for dedicated motors, simplifying the architecture of the system. For both flying and driving modes, detailed kinematics and dynamics models integrated with a mode switching strategy are constructed by using the Newton-Euler method. Based on the developed models, the constrained nonlinear model predictive controller is designed to achieve the accurate motion performance in flying and driving mode. Comprehensive experimental results and comparative analysis demonstrate that HSTV achieves significant trajectory tracking accuracy across both flying and driving modes, while saving energy by up to 70.9% with no significantly increasing structural complexity (maintained at 98.6% simplicity).

This paper investigates the trajectory-tracking problem of an uncertain robotic system subject to coupled output constraints. A decoupling approach is introduced to convert the coupled constraints into independent time-varying constraints on the transformed output coordinates. Based on this decoupling, a partial controller is developed using a time-varying barrier Lyapunov function (TVBLF) to enforce constraint satisfaction. In addition, fuzzy logic systems (FLSs) are employed to approximate the uncertain dynamics caused by unknown system parameters. To reduce unnecessary state transmissions and controller updates, a state-based event-triggered mechanism (ETM) is incorporated without significantly degrading tracking performance. A Lyapunov-based stability analysis is further provided to establish the feasibility of the proposed method. Finally, simulation results validate the effectiveness of the developed control scheme.

Bearing fault detection is essential for machinery health management. An issue of existing work is that the network trained with a publicly available dataset (source domain) often has a large accuracy drop when applied to a target machine (target domain), since the source domain and target domain originate from different machines, and thus exhibit significant differences in data distribution. Besides, in real industrial processes, collecting large amounts of fault data from the target machine is unrealistic. To address the above issues, a target domain one-class learning bearing fault detection framework (TDOCL-BFDF) is proposed in this article, inspired by the distribution discrepancy between the normal and the fault data. First, a new three-stage traininginitialization scheme, which fully uses data from source and target domains, is designed for the TDOCL-BFDF to extract features from the target normal data and increase the distribution discrepancy between the normal and the fault data. Second, a Gaussian fault detector (GFD), serving as a critical performance-enhancing module of the TDOCL-BFDF model, is proposed to mitigate the scale imbalance across the dimensions of latent features. The proposed framework is evaluated using four bearing datasets (three public and one in-house dataset) and achieves an accuracy of 0.990-1.0 in all the fault detection tasks. The results show that the TDOCL-BFDF outperforms other state-of-the-art methods and manifests the great potential of the proposed TDOCL-BFDF for real industrial application scenarios.

Thickeners are critical components in mineral processing and hydrometallurgical circuits.Their operational performance directly determines the production efficiency and resource recovery rates.However,maintaining precise control of the underflow concentration presents considerable challenges owing to inherently strong nonlinearities,significant time delays,multivariable coupling,and pronounced sensitivity to external disturbances.Traditional control methods are often inadequate to achieve accurate and stable regulation under complex dynamic conditions.To address these challenges,we developed an advanced composite control strategy that synergistically integrates mechanistic modeling with intelligent compensation.This study aims to enhance the underflow concentration c ontrol performance through a novel framework that combines theoretical foundations with adaptive learning capabilities.The methodological implementation proceeded in two systematically coordinated phases.Initially,based on the sedimentation separation theory,a comprehensive state-space model of the thickener process was established.This model captures the fundamental dynamics of solid-liquid separation,including the particle settling behavior,sediment compression mechanisms,and continuous discharge characteristics.This mechanistic foundation supports the formulation of a nonlinear model predictive control(NMPC)framework.NMPC employs repeated finite-horizon optimization to compute optimal control actions while explicitly handling process constraints.Subsequently,considering the practical challenges posed by unmodeled dynamics,parameter perturbations,and external disturbances,a radial basis function(RBF)neural network was incorporated as an online compensator.This intelligent component continuously identifies discrepancies between mechanistic model predictions and actual process behavior.It generates real-time corrections to enhance prediction accuracy.The RBF architecture leverages Gaussian activation functions.The network parameters are adaptively tuned using online learning algorithms to ensure optimal performance under varying operational conditions.The experimental validation included realistic industrial scenarios.Both feed flow rate and feed concentration from upstream processes were set as continuously fluctuating variables.The disturbance input increments followed a Gaussian distribution.The relationship between disturbance variation and time was mathematically characterized.Model errors were represented by a sinusoidal error signal with Gaussian noise established from the mechanistic model output and actual measured output.The compensation effectiveness of the RBF neural network for unmodeled system dynamics demonstrated excellent tracking characteristics.Although minor time delays were observed,the compensation signal maintained high consistency with the disturbance signal in terms of amplitude variation trend.This indicates the effective learning and approximation capabilities of the RBF neural network in uncertain system dynamics.Comparative case studies were conducted for four control strategies:PID,MPC,standard NMPC,and the proposed NMPC-RBF.The underflow concentration tracking results clearly demonstrated that the NMPC-RBF controller achieves the best performance.The PID controller exhibits significant steady-state errors and lower overall tracking accuracy.Both the MPC and standard NMPC controllers maintain the output near the reference,but undergo apparent oscillatory phenomena.The standard NMPC is particularly prone to large overshoot.In contrast,the NMPC-RBF output curve maintains a high degree of coincidence with the reference throughout the entire time domain.This demonstrates its superior tracking precision.This enhancement is attributed to the introduced RBF compensation mechanism.This mechanism proactively corrects the system output during rolling optimization.It effectively overcomes the performance limitations caused by model mismatch and provides more accurate prediction information for optimal decision making.Notably,within the NMPC-RBF framework,the flocculant dosage input remains stable at relatively low levels.Concentration control is primarily achieved through modulated adjustments to the underflow rate.This operational pattern aligns closely with key industrial objectives.The aim is to maintain a stable discharged solid concentration while simultaneously minimizing flocculant consumption.This contributes to a reduction in operational costs and environmental impact.These comprehensive results confirm that the integrated NMPC-RBF approach substantially enhances control accuracy,adaptive capability,and operational robustness of thickener operations.This presents a viable advanced solution for optimizing thickener performance in complex industrial environments.

Motivated from pigeons flying in flocks, this paper delves into the outdoor flocking flight using pigeon-inspired flapping-wing robots (FWRs). Current research and implementation of cooperative flight for FWRs is scarce, generally exhibiting constrained operational environments, small number of individuals, and poor flight performance. This study aims to construct a stable outdoor flocking system based on our self-developed small FWR (USTB-Dove, 0.8 m wingspan). The objective is to achieve superior 3D flocking flight with multiple FWRs outdoors. To achieve this, we first improve the USTB-Dove to enhance its payload capacity and controllability. The upgraded platform, featuring a redesigned wing and tail, is capable of cruising for 30 minutes with a 65-gram payload. Additionally, within a leader-follower architecture, we design a guidance law and a velocity control model for the followers. To achieve superior leader tracking, the model is developed through physics-based modeling and parameter optimization of the actual FWR and is solved using the Newton-Raphson method. Furthermore, we develop a flocking architecture with distributed controllers and centralized monitoring. Virtual leader approach is adopted to minimize communication overhead, enabling stable 1-km operation with a system capacity of 31 FWRs. Ultimately, we conduct outdoor dual-FWR test to validate the leader-tracking capability. Then five-FWR test demonstrates system's stability and potential for scalability. The diameter of five-FWR 3D flock is controlled at approximately 10 m with an altitude stability of $35 \pm 3$ m, which is, to our knowledge, the best performance achieved in published outdoor flocking flight research of FWRs.

This article investigates the model analysis and control design of a nonuniform continuum arm system, addressing the challenges posed by its inherent nonlinearity, coupled dynamics, and large deformations. A distributed control strategy is proposed, leveraging in-domain static forces and velocity feedback to achieve controlled transitions between static variable curvature configurations. The developed model accounts for variable curvature bending configurations and provides interpretable actuation mechanisms based on cable-driven control torques. A Lyapunov-based stability analysis demonstrates the asymptotic stability of the system under the proposed control scheme. Numerical simulations, including a target capture scenario in narrow spaces, illustrate how the continuum arm can transition smoothly between different static shapes while maintaining stability. The results highlight the potential of the proposed approach for practical applications that require reliable configuration changes in confined or task-specific environments.

Designing model-free controllers to achieve desired control performance often leads to reduced real-time performance, diminished robustness, or increased complexity. This paper proposes an attractive surface-based state-attracted control (SAC) method for uncertain nonlinear systems, where the attractive surface is designed through a performance-based state-attracted function (SAF) with global boundedness and convergence. This method ensures that the tracking error convergence direction and rate are consistent with the output of the SAF, so that the tracking error converges along the attractive surface to a small neighborhood of the origin. Notably, the SAC method eliminates model dependence and maintains low controller complexity while achieving desired control performance, robustness, and real-time performance in a complementary way. Furthermore, three types of SAC are developed: a fast SAC (FSAC) for rapid convergence, a convex SAC (CSAC) for smooth convergence, and a switched SAC (SSAC) for multi-phase convergence. Finally, the stability of the closed-loop system is analyzed by using Lyapunov theory, and its performance is validated through simulations.

This article investigates distributed formation tracking control for multiple quadrotor UAVs subject to switching topologies with external disturbances and mass uncertainties. We propose an adaptive finite-time disturbance observer combining time-varying bandwidth with fractional-power correction terms to outperform conventional linear observers. A Lyapunov-based event-triggered mechanism governs topology switching via formation energy metrics, guaranteeing minimum dwell time and finite switching frequency. Stability analysis characterizes convergence rates for both fixed and switching topologies through Lyapunov construction. Simulations comparing against four recent baselines demonstrate up to an 80% reduction in formation error, while hardware-in-the-loop (HIL) experiments validate the real-time implementability and robustness of the proposed framework under embedded implementation constraints.

The problem of vibration control in flexible air-breathing hypersonic vehicles (FAHVs) with variable bending stiffness is investigated in this paper. By introducing variable stiffness into the flexible part of FAHVs, we consider the change in the sectional moment of inertia due to deformation under aerodynamic loads. Additionally, a velocity damping coupling term is incorporated into the ODEs-PDEs coupled system to enhance the accuracy of the dynamic model. A boundary feedback controller is designed, based on the Lyapunov direct method, to ensure the ultimate asymptotic stability of the closed-loop system. Relative to previous studies, we simultaneously incorporate variable bending stiffness and velocity coupling, ensuring stable flight of FAHVs while suppressing fuselage vibrations. Finally, simulation results demonstrate the effectiveness of the proposed control strategy.

Micro flapping-wing aerial vehicles are highly sensitive to abrupt trajectory changes due to strong aerodynamic coupling and limited maneuverability. Conventional grid-based path planning methods often generate zig-zag trajectories with excessive sharp turns, leading to large attitude oscillations and increased control effort. To address this issue, this paper proposes an improved bidirectional A* path planning algorithm incorporating a dynamic turning penalty mechanism and pheromone-guided optimization. The turning penalty term suppresses abrupt heading variations while maintaining global search efficiency, and B-spline smoothing further ensures curvature continuity and dynamic feasibility. Simulation results demonstrate that the proposed method significantly reduces turning frequency with minimal path length increase.

Addressing communication constraints is a critical challenge for deploying autonomous quadrotor swarms in real-world environments. State-of-the-art methods typically rely on idealized communication assumptions, leading to performance degradation under communication disruptions. This article presents, a novel motion planning framework for autonomous quadrotor swarm navigation in communication-denied environments (MP-CDE). Its key innovation is the systematic exploitation of the inherent physical coupling between aerial mobility and visual perception. Via dynamic yaw angle optimization, individual quadrotors attain dual goals: maximizing environmental perception and enhancing operational safety, which, coupled with fast target tracking and prediction, significantly mitigates potential collisions. Comprehensive validation via simulations and real-world experiments verifies that MP-CDE enables reliable swarm navigation in communication-denied environments.

This article proposes an adaptive prescribed performance control strategy for nonlinear cyber–physical systems (CPSs) under false data injection (FDI) attacks, where some of the system’s state variables are unmeasured. A radial basis function (RBF) neural network is incorporated into the construction of observers to estimate unknown variables of the system. By combining the RBF neural network with a specific coordinate transformation and a symmetric barrier Lyapunov function (BLF), the proposed adaptive control law guarantees that the trajectory tracking error satisfies the prescribed performance. Simulation results verify the effectiveness of the method and confirm the theoretical analysis.

Motion planning for dual-arm robots poses significant challenges due to their structural complexity and high degrees of freedom, which intensify the difficulties of mitigating self-interference and exploring feasible paths in high-dimensional configuration spaces. Existing methods often fail to meet the efficiency requirements of continuous dual-arm tasks. To address these issues, this article proposes a novel dual-arm robotic cooperative motion planning algorithm leveraging pairwise clearance learning (PCL-CMP). The approach utilizes a pairwise clearance network (PCN)-based batch collision estimation model, consisting of two distinct modules: Pairwise_Clearance_Self for interarm links and Pairwise_Clearance_Obs for link-obstacle clearances (trained specifically for given environments). Leveraging batch processing, the model enables efficient large-scale collision estimation. An adaptive step-size batch expansion strategy accelerates the search for collision-free paths. Additionally, a path optimization and safety verification mechanism minimizes path length while ensuring safety. Simulation and experimental results demonstrate that PCL-CMP achieves an average planning time of under 0.7 s. Compared with baseline algorithms, planning time is reduced by at least 34.19%, and path length is shortened by at least 41.61%.

This article presents a novel hybrid triggering framework for trajectory tracking of underactuated unmanned aerial vehicles, incorporating adaptive prescribed performance specifications on the position error. Unlike traditional bounds that decay exponentially over time, we design less conservative performance bounds that evolve with the converging error, providing a tighter constraint. To address bandwidth limitations and enhance tracking accuracy, we introduce a novel hybrid triggering mechanism that fully fuses the advantages of both traditional continuous-time implementations and event-triggered methods and effectively responds to the varying sampling requirements in different control phases. This approach allows discrete control input updates within intervals of arbitrary duration, thereby avoiding zeno behavior. The uncertain and potentially time-varying vehicle mass is estimated using an adaptive law derived through backstepping. We demonstrate that the closed-loop system is uniformly ultimately bounded, thus ensuring that the performance bounds are enforced. The effectiveness and robustness of our approach are demonstrated through simulation and experimental results.

This paper proposes a novel two-layer prescribed performance control strategy for underactuated unmanned aerial vehicles (UAVs) with unknown mass. Unlike conventional approaches that heavily rely on barrier functions-often producing large control signals and potential instability due to actuator limitations-the proposed method introduces soft and hard performance bounds on position and velocity errors. A smooth switching mechanism selectively activates the barrier function, thereby reducing its usage and enhancing system robustness. To accommodate these bounds, a new integralmultiplicative barrier-like (IMBL) Lyapunov function is developed to determine the desired thrust. Second-order linear systems are employed as low-pass filters in the backstepping design, lowering computational complexity and improving robustness against disturbances. An adaptive law is integrated into the framework for real-time mass estimation, and torque inputs are derived accordingly. Simulation results demonstrate the effectiveness of the method and validate the theoretical analysis. (c) 2025 Published by Elsevier Ltd.