As important auxiliary equipment, rehabilitation robots are widely used in rehabilitation treatment and daily life assistance. The rehabilitation robot proposed in this paper is mainly composed of an omnidirectional mobile platform module, a lower limb exoskeleton module, and a support module. According to the characteristics of the robot’s omnidirectional mobility and good stiffness, the overall kinematic model of the robot is established using the analytical method. Passive and active training control strategies for an omnidirectional mobile lower limb exoskeleton robot are proposed. The passive training mode facilitates the realization of the goal of walking guidance and assistance to the human lower limb. The active training mode can realize the cooperative movement between the robot and the human through the admittance controller and the tension sensor and enhance the active participation of the patient. In the simulation experiment, a set of optimal admittance parameters was obtained, and the parameters were substituted into the controller for the prototype experiment. The experimental results show that the admittance-controlled rehabilitation robot can perceive the patient’s motion intention and realize the two walking training modes. In summary, the passive and active training control strategies based on admittance control proposed in this paper achieve the expected purpose and effectively improve the patient’s active rehabilitation willingness and rehabilitation effect.

This paper presents the design, analysis, and development of a novel six degrees-of-freedom (6DOF) desktop upper limb rehabilitation robot. The upper limb rehabilitation robot is mainly composed of the omnidirectional mobile platform, armrest, and 3DOF wrist rehabilitation mechanism. The forward and inverse kinematics and Jacobian matrix of the upper limb rehabilitation robot are derived based on the kinematics of a rigid body, and its working space is also analyzed based on arm kinematics. The forward and inverse kinematics of the arm are derived based on the D–H method. A new control strategy and algorithm were developed based on the robot system's hardware structure and arm model. These were employed in simulated rehabilitation experiments on both a single joint and multiple joint linkages. The experimental results indicate that the maximum error for single-joint rehabilitation is 6.123 deg, while for multiple joint linkages, it is 5.323 deg. Therefore, the control strategy and control algorithm can complete the corresponding rehabilitation training. This 6DOF desktop upper limb rehabilitation robot can provide passive rehabilitation training for patients in the early stages of paralysis.

This article proposes a deformable water-mobile robot that can be used for rescue work. The robot body adopts an open-motion chain structure with two degrees of freedom, including two drive modules and one main control module. The three modules are connected through deformation joints, and each drive module is equipped with an underwater thruster. The robot can obtain a triangle, linear shape, curved shape, and U-shape through deformation and have three types of motion: linear shape motion, U-shaped motion, and curved shape motion. In the linear shape, a multi-island genetic algorithm was used to optimize the structural parameters with the minimum resistance and the maximum volume. Floating state analysis was conducted in the U-shape, and the structural parameters were reasonably designed. By experimenting with the robot prototype on water, the robot can achieve oscillating, linear, U-shaped, and horizontal rotary motion, has an automatic adjustment function, and effective buoyancy meets the required requirements.

Omnidirectional mobile robots have gained extensive application across diverse fields due to their exceptional maneuverability and adaptability in confined spaces. However, structural and systemic uncertainties significantly compromise motion accuracy. To enhance motion control precision, this paper proposes a sliding mode control (SMC) method integrated with a radial basis function (RBF) neural network. The approach aggregates model uncertainties, nonlinear dynamics, and unknown disturbances into a composite disturbance term. An RBF neural network is employed to approximate this disturbance, with compensation embedded within the SMC framework. An online adaptive law for neural network optimization is derived using the Lyapunov stability theorem, thereby improving the disturbance rejection capability. Comparative simulations and experiments validate the proposed method against modern control strategies. Results demonstrate superior tracking performance and robustness, significantly enhancing trajectory tracking accuracy for the MY3 wheeled omnidirectional mobile robot.

Based on the characteristics of wheeled, tracked and legged movements, a variable parallelogram tracked mobile robot(VPTMR) is proposed and developed to enhance its adaptability and stability in the complex environment. This VPTMR robot consists of two variable parallelogram structures, which are composed of one main tracked arm, two lower tracked arms and a chasis. The variable parallelogram structure is actuated by a DC motor. And another DC motor actuates the track rotation, which enables VPTMR robot to move in wheeled, tracked and legged mode that makes the robot to adapt to all rugged environments. The prototype(VPTMR) is developed to verify its performance on environmental adaptability, obstacle crossing ability and stability.