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.

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.

Traditional dexterous hands can readily grasp objects but face limitations in dexterous manipulation due to complex control systems and high actuation demands. This paper presents a novel dexterous hand designed to address these challenges. The hand consists of four fingers, each equipped with two mecanum wheels at the fingertips to allow for the omnidirectional manipulation of objects. Continuous rotation of the mecanum wheels enables unbounded motion of grasped objects without the need for finger gaiting. Object pose adjustment is achieved by controlling the rotation of mecanum wheels, thus significantly reducing operational complexity and enhancing manipulative agility. Furthermore, to address the control difficulty of multi-finger coordinated motion, a four-finger coupled mechanism is implemented, resulting in a dexterous hand with three degrees of freedom. Kinematic models of omnidirectional manipulation are established for typical geometric objects, including a flat plate, a cuboid, a sphere, and a cylinder. Simulations confirm the correctness of the kinematic models. Experimental results show that the hand can achieve omnidirectional manipulation of objects. Finally, the extended functionality of the dexterous hand is briefly presented, which allows it to be reconfigured into an omnidirectional mobile robot.

Purpose: The need for compliant control of dexterous hands as end-effectors in environmental interactions is increasingly emphasized. Traditional compliant control methods are often characterized by significant grasping force errors and poor adaptability. To address the constant force control requirements for object grasping and posture adjustment for the omnidirectional dexterous hand (ODH), this paper aims to propose a nonlinear disturbance observer-based adaptive admittance control (NDOAAC) method. Design/methodology/approach: Based on the established kinematic model, an admittance control framework is constructed. A gradient descent-based adaptation law is designed to continuously estimate environmental stiffness and dynamically modify the reference trajectory. A nonlinear disturbance observer is incorporated to aggregate unmodeled dynamics and external disturbances as composite disturbances. The parameters of the admittance model are then regulated according to the observed values. The stability of the proposed control system is theoretically demonstrated. Findings: The superiority of the NDOAAC method is validated through comparative simulations with Classical Admittance Control (CAC) and Adaptive Admittance Control (AAC), while its effectiveness is confirmed via physical experiments. Results demonstrate that the NDOAAC strategy achieves precise tracking of desired grasping forces, improves system response speed and maintains excellent adaptability to objects with varying stiffness levels. Originality/value: The NDOAAC strategy is proposed in this paper, which effectively fulfills the requirements of grasping tasks and provides a viable solution for compliant manipulation with the ODH.

Compliant control of dexterous hands is increasingly demanded for environmental interaction. Conventional strategies often suffer from significant force tracking errors and poor adaptability when interacting with unknown environments. To meet the constant-force contact requirements in omnidirectional dexterous hand (ODH) grasping and object pose adjustment, this paper proposes an admittance control strategy integrating an extended state observer (ESO) with sliding mode control (SMC), termed extended state observer and sliding-mode admittance control. This method maintains the required contact forces for grasping and manipulation while demonstrating good adaptability to unknown object properties. A modified D-H method is employed to establish the kinematic model of the ODH, based on which its workspace and graspable object range are analyzed. Within the virtual mass-damping admittance framework, SMC is introduced to enhance the system’s robustness against unknown environmental parameters. The ESO is utilized to estimate and compensate for the disturbance in the derivative of the force error, thereby reconstructing a smooth sliding surface to suppress chattering. Furthermore, a continuous SMC law is designed based on Lyapunov stability theory, guaranteeing stable convergence and smooth force tracking. Experimental results confirm that the proposed strategy can well track the desired grasping force and satisfy the requirements of grasping tasks. This work demonstrates the effectiveness of the proposed strategy in achieving robust and precise force control, offering a viable solution for the compliant manipulation of dexterous hands in complex environments.

Cable-driven minimally invasive surgical robots suffer from significant motion inaccuracies due to nonlinear transmission effects such as friction, elasticity, and hysteresis. These factors lead to strong nonlinear and direction-dependent behaviors, making accurate modeling and compensation challenging. To address this issue, this study investigates the error characteristics of a cable-driven surgical robot prototype based on its structural features. A kinematic model is first established, and geometric errors are corrected through Denavit–Hartenberg (DH) parameter identification using a least-squares method. To further characterize nonlinear effects, the LuGre friction model and equivalent stiffness theory are introduced to analyze friction and cable deformation behaviors. Since physics-based models alone cannot accurately capture the coupled nonlinear errors, a radial basis function (RBF) neural network is employed to approximate the residual errors. To enable real-time implementation, the predicted errors are further simplified using equivalent polynomial functions for efficient compensation. Experimental results demonstrate that the proposed method significantly improves the motion accuracy of the cable-driven system, effectively reducing both tracking error and hysteresis effects. By integrating mechanism-based modeling with data-driven compensation, this approach provides a practical and effective solution for precision enhancement in cable-driven surgical robotic systems.