The treatment of patients with balance disorders is an urgent problem to be solved by the medical community. The causes of balance disorders are diverse. An aging population, traffic accidents, stroke, genetic diseases and so on are all possible factors. It has brought great pain and inconvenience to patients and their families. At present, there are two main types of assisted rehabilitation training robots for patients with balance disorders: exoskeleton robots and end robots. The exoskeleton robot is generally installed on the outside of the patient's body to follow their movement, which can support the weight of the body and provide power support to help the patient train and recover lower limb ability. The use of end robots is usually to secure the patient's foot to the motion platform and control the pedal to drive the lower limbs to conduct gait training. Such passive training is more suitable for patients with severe disorders. The patient has low awareness of active participation. This paper focuses on research on end rehabilitation training robots for balance disorders. In this paper, a robotic system for rehabilitation training of patients with balance disorders is invented. The robot body is a 9 degree of freedom (DOF) redundant series-parallel hybrid motion platform. Two sets of motion platforms with symmetrical mirror images are used together to simulate different motion modes of the human body and drive the human body to move. Each set of motion platforms is composed of a 6-DOF vestibular parallel device and a 3-DOF proprioception parallel device. It has the advantages of DOF decoupling and fast response, proposing a new structural form for the design of proprioceptive and vestibular simulation platforms. The robot's functional level can be divided into a vestibular sense module and a proprioception module according to the structure. The two modules can work independently to achieve different functions or work together to achieve complex motion and multisensory fusion. This robot is a redundant mechanism device with 9 DOFs. Through a reasonable distribution of DOF and motion, the robot's working space can be increased, and the robot's flexibility and motion performance can be improved. In this paper, a trajectory tracking control algorithm for vestibular and proprioceptive simulation is proposed, which can provide unlimited body sense training for patients within the robot's limited motion range.

This paper presents an innovative application of a 6-DOF robot in the field of rehabilitation training. This robot operates in a parallel fashion for lower limb movement, which adopts a new structure that can help patients to carry out a variety of rehabilitation exercises. Traditional parallel robots, such as the Stewart robot, have the characteristics of strong bearing capacity. However, it is difficult to achieve high-speed, high-acceleration and long journey movement. This paper presents a new robot configuration that can address these problems. This paper also conducts an all-around characteristic analysis of this new parallel robot, including kinematics, dynamics and structure, to better study the robot and improve its performance. This paper optimizes an algorithm to make it more suitable for rehabilitation training. Finally, the performance improvements brought by optimization are verified by simulations.

This study investigates the trajectory-tracking control problem of a two-degree-of-freedom underwater manipulator operating in a complex disturbance environment. A dynamic model of the multi-link serial manipulator is first established. In this study, water resistance and additional mass forces acting on the manipulator are analyzed and calculated using differential analysis and the Morrison formula. To account for coupling between joints, the concept of equivalent gravity is introduced to precisely calculate the underwater manipulator’s buoyancy and gravity. As a result, a relatively accurate dynamic model of the underwater manipulator is established. To mitigate the influences of external disturbances and unmodeled parts on the manipulator, a finite-time extended state observer (FTESO) is designed to estimate system quantities that are difficult to measure directly. The robustness of the controller is enhanced using a feedforward compensation mechanism, and it is demonstrated that the observation error of the observer converges in finite time. Finally, a global fast terminal sliding mode controller (GFTSMC) is developed for trajectory tracking, integrated with the aforementioned observer, and designed to smooth and limit the controller’s output. The controller’s stability is proven using Lyapunov stability theory, and its effectiveness is verified through simulation-based comparison experiments.

Due to the increasing demand for remote sensing imaging products, the agile earth observation satellite scheduling problem (AEOSSP) has garnered significant attention. In response, this paper proposes a reinforcement learning-based dung beetle optimization (RLDBO) algorithm to address AEOSSP. The proposed method dynamically adjusts the proportions of four types of dung beetles (ball-rolling beetle, brood ball beetle, small dung beetle, and thief beetle), enabling adaptive optimization of the scheduling scheme to better handle the complexities and uncertainties of the search space. The Q-learning mechanism guides the adjustment of these proportions, effectively balancing global exploration and local exploitation at different stages of the search process. Experimental results demonstrate that the RLDBO algorithm effectively solves AEOSSP across multiple instances, and it outperforms other algorithms in various aspects, including optimization performance, convergence speed, and scheduling effectiveness. The experimental validation confirms that RLDBO significantly enhances the efficiency and effectiveness of agile earth observation satellite scheduling.

The increasing prevalence of balance disorders presents significant challenges to rehabilitation therapy, prompting the development of rehabilitation robots as effective tools for motion training. To enhance the patient experience and rehabilitation outcomes in human-robot collaborative training, a motion control strategy based on reinforcement learning and genetic algorithms (RL-GA) with somatosensory enhancement is proposed and implemented in a novel rehabilitation robot. This study introduces a mathematical model of the human sensory system to quantify somatosensory feedback related to motion and integrates the washout algorithm (WA) into the robot's control system, facilitating the reproduction of somatosensation and motion simulation. Three typical rehabilitation modes—level walking, stair climbing, and stair descending—are selected, with natural gait features extracted as predefined trajectories. The WA parameters for each mode are optimized using RL-GA in various rehabilitation scenarios. Simulation results demonstrate that the washout filtering optimization method using RL-GA reduces theoretical somatosensory error by approximately 10% to 20% across all three rehabilitation modes, compared to traditional GA optimization. The experimental results further confirm the reliability and feasibility of the proposed method. The proposed approach enhances the realism of robot-assisted motion, thereby theoretically improving training effectiveness and accelerating the rehabilitation process.

To address the rapid population diversity loss and premature convergence of the Artificial Lemming Algorithm (ALA) in complex optimization problems, this paper proposes an Improved Artificial Lemming Algorithm (IALA) with multi-strategy enhancements inspired by lemming behavior. First, a non-uniform mutation operator and a nonlinear step-size strategy are introduced to strengthen local optima escape capability and optimization precision. Second, inspired by the foraging and positioning behavior of lemmings, a relative advantage learning strategy is designed to reduce dependence on the global best individual, further enhancing the algorithm’s exploration ability. Finally, a Q-learning-based adaptive mechanism is integrated to intelligently orchestrate five lemming-inspired behavioral modes through a nonlinear reward function, enabling adaptive switching among search patterns. Comparative experiments on the CEC2022 benchmark suite demonstrate that IALA achieves a Friedman mean rank of 1.25, ranking first with a significant margin. Compared with the original ALA and other six classical and state-of-the-art metaheuristic algorithms, and four recently proposed improved ALA variants (EALA, IALA_Tan, DMSALAs, and MsIALA), the Wilcoxon rank-sum test confirms that IALA is significantly outperformed in only 2 out of 120 pairwise comparisons, exhibiting remarkable advantages in optimization accuracy, convergence speed, and robustness. Ablation experiments validate the synergistic necessity of all three strategies, with the Q-learning adaptive mechanism identified as the most critical contributor. Exploration–exploitation balance analysis and search history visualization further confirm that IALA achieves a smooth adaptive transition from global exploration to local exploitation. Space complexity analysis reveals that the Q-table introduces only approximately 19.5 KB of fixed additional overhead, which becomes negligible for high-dimensional problems. Furthermore, IALA is successfully applied to the parameter tuning of underwater manipulator controllers, verifying its efficiency and reliability in real-world engineering applications.