Andong Liu

Assistive rehabilitation gloves are critical for patients with hand dysfunction, but existing systems suffer from poor portability, and reliance on subjective therapist assessments. This letter presents a compact, embedded rehabilitation glove system based on the STM32F429 microcontroller, enabling personalized and precise rehabilitation training. A pruned cross-entropy support vector machine (SVM) algorithm is proposed to realize real-time movement recognition through visual data (320×240 pixels, 1 fps), overcoming the deployment limitation of complex networks on embedded hardware. A rehabilitation index α is defined with cross-entropy to quantify movement similarity with pre-stored standard movements is utilized, driving adaptive variable impedance control(VIC) to adjust movement stiffness. Experimental results show the system achieves 83.8%–94.3% recognition precision, a maximum grasping force of 1.4 N (Newton), and effective completion of dexterous tasks (e.g., egg grasping, cylinder manipulation). This portable solution realizes data-driven rehabilitation and addresses the gap between professional therapy and home-based training.

This article addresses the problem of cooperative path following for wheeled mobile robots (WMRs) under system constraints and external bounded disturbances within a switching communication network, by proposing a robust distributed model predictive control (DMPC) strategy. First, the cooperative path-following task is decoupled into two subtasks using a modified virtual structure: a cooperative task involving virtual reference robots and an individual path-following task between each actual robot and its corresponding virtual reference. A time-like path parameter is introduced to generate predefined path information for the virtual reference robot in advance, enabling dynamic formation tracking. Subsequently, discrete-time error dynamics subject to external bounded disturbances are derived for each robot, and a centralized predictive control problem is formulated as a baseline. A nominal DMPC strategy is then developed for the disturbance-free case, followed by an extension to a robust DMPC formulation that accounts for nonzero disturbances. In this context, a stability constraint is incorporated to ensure closed-loop stability without relying on neighboring agents’ real-time information. Theoretical analysis confirms the feasibility of the proposed scheme and guarantees the convergence of system trajectories to a disturbance invariant set. Finally, simulation and experimental results validate the effectiveness of the proposed strategy in cooperative path-following scenarios involving WMRs.

Autonomous dynamic systems (ADS)-based encoding of human motion skills has been widely demonstrated to exhibit high transfer efficiency in goal-directed tasks; however, significant gaps remain in the study of skill transfer for periodic motions, particularly complex periodic motions. In this letter, we propose a novel method for directly learning complex periodic movements using Lyapunov functions (LF), which is fundamentally different from all existing methods that learn periodic movements through limit cycle mapping. First, we introduce a polar coordinate transformation to decouple trajectory and angle modulation, enabling the radially increasing Lyapunov energy function to acquire non-radial expansion capabilities. Then, based on this, we learn a data-driven Lyapunov energy function by solving a dual objective optimization problem that combines geometric alignment and orbit parallelism constraints, aligning one of its horizontal surfaces with a periodic human demonstration trajectory. Finally, ADS is learned by sequentially solving LF-related constrained optimization problems. By designing appropriate constraint functions, we can ensure that the trajectories generated by ADS converge to an LF-level surface whose shape resembles that of periodic human demonstration trajectories. Both simulations and real robot experiments validate the effectiveness of the proposed method.

Stable dynamic systems enable robotic systems to plan and execute complex geometric motions in unstructured environments. However, in certain scenarios, such as precision assembly tasks, the constrained operational workspace of robotic arms, along with the presence of obstacles, may lead to unintended collisions. Furthermore, motion precision plays a crucial role in determining the success rate of such tasks. To address these challenges, we propose a novel Safe-Critical Dynamic System (SC-DS) framework. The SC-DS framework consists of a stable dynamic system and an obstacle avoidance controller. The stable dynamic system is formulated in a parametric nonlinear form, which enhances performance in terms of accuracy. Additionally, control barrier functions (CBF), corresponding to complex constraint spaces, are learned from demonstration data. By utilizing these learned CBFs, an obstacle avoidance controller is designed to ensure that the system trajectory remains within the learned safety boundaries. Moreover, the controller adaptively extends the effective range of control inputs, thus mitigating replication errors due to input limitations. Experimental results, both in simulation and with a physical robot, demonstrate that the SC-DS framework effectively reproduces trajectories with both stability and safety, outperforming existing methods in terms of overall task performance. Note to Practitioners—This study is motivated by the need to develop a safer and more precise skill-learning framework for practical applications, such as service robots and assembly robots. We propose the SC-DS framework, which integrates the challenges of uncertain environments into the learning of precise motion skills, ensuring both accuracy and safety in trajectory generation. This framework is particularly suitable for applications requiring strict performance and safety standards. By incorporating safety constraints directly into the learning process, our method provides a robust solution that effectively addresses robot-environment interactions, including obstacles, disturbances, and varying conditions. Our research enhances the reliability of dynamic system-based learning frameworks and offers practical tools for real-world applications, ensuring robots perform tasks efficiently while maintaining safety. Practitioners can leverage this framework to improve safety and maintain high-precision skill learning, ensuring effective handling of real-world challenges.