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Polishing plays an indispensable role in optical processing, especially for large-aperture optical reflective mirrors with freeform surfaces. Robotic polishing requires effective control of the contact force between the robot and the mirror during processing. In order to maintain a constant contact force during polishing, traditional polishing robots rely on closed-loop control of air cylinders, whose performances heavily rely on high-fidelity force sensing and real-time control. This paper proposes to employ a compliant constant-force mechanism in the end-effector of a polishing robot to passively maintain a constant force between the robot and the mirror, thus eliminating the requirement for force sensing and closed-loop control. The compliant constant force mechanism utilizing the second bending mode of fixed-guided compliant beams is adopted and elaborated for the passive end-effector. An end-effector providing a constant contact force of 40 N is designed and prototyped. The polishing experiment shows that the passive constant-force end-effector provides stable contact force between the robot and the mirror with fluctuation within 3.43 N, and achieves RMS (Root Mean Square) lower than λ/10 (λ = 632.8 nm) of the polished surface of the large-aperture optical reflective mirror. It is concluded that the constant-force compliant mechanism provides a low-cost and reliable solution for force control in robotic polishing.

The existing research on rescue robots has focused mainly on reconnaissance, detection, and firefighting, and a small number of robots that can achieve human rescue have problems such as poor safety and stability and insufficient carrying capacity. This article addresses the above issues and cleverly combines the advantages of soft robotic arms, underactuated robotic arms, and suction cups based on the principles of bionics. A new design for a robotic arm was proposed, and its working principle was explained. Then, the human rescue process was divided into two stages, and the grasping force of the robotic arm in each stage was analyzed separately. Finally, a prototype of the principle was developed, and the feasibility of the design principle of the robotic arm was verified through grasping experiments on a cross-sectional contour model of the human chest. At the same time, grasping experiments were conducted on different objects to demonstrate the potential application of the robotic arm in grasping ground objects. This research proposes a stress envelope adsorption rescue robot arm inspired by the adhesion ability of the Drosera plant and the stress envelope effect, which can apply force to the entire surface of the human body, reduce local force on the human body, ensure load-bearing capacity and adaptability, and improve the safety and stability of rescue grasping.

Unbalanced force produced by the unbalanced mass will affect vibrations of rotor systems, which probably results in the components failures of rotating machinery. To study the effects of unbalanced mass on the vibration characteristics of rotor systems, a flexible rotor system model considering the unbalanced mass is proposed. The time-varying bearing force is considered. The developed model is verified by the experimental and theoretical frequency spectrums. The displacements and axis orbits of flexible and rigid rotor systems are compared. The results show that the unbalanced mass will affect the vibration characteristics of rotor system. This model can be more suitable and effective to calculate vibration characteristics of rotor system with the flexible deformation and unbalanced mass. This paper provides a new reference and research method for predicting the vibrations of flexible rotor system considering the unbalanced mass.

Retinal surgery continues to be one of the most technical demanding surgeries for its high manipulation accuracy requirement, small and constrained workspace, and delicate retinal tissue. Robotic systems have the potential to enhance and expand the capabilities of surgeons during retinal surgery. Thus, focusing on retinal vessel bypass surgery, a master-slave robot system is developed in this paper. This robotic system is designed based on characteristics of retinal vascular bypass surgery and analysis of the surgical workspace in eyeball. A novel end-effector of two degrees of freedom is designed and a novel remote center of motion mechanism is adopted in the robot structure. The kinematics and the mapping relationship are then established, the gravity compensation control strategy and the hand tremor elimination algorithm are applied to achieve the high motion accuracy. The experiments on an artificial eyeball and an in vitro porcine eye are conducted, verifying the feasibility of this system.

Robot manipulators perform a point-point task under kinematic and dynamic constraints. Due to multi-degree-of-freedom coupling characteristics, it is difficult to find a better desired trajectory. In this paper, a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm (INSGA-II) is proposed. Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves. Then, an INSGA-II, by introducing three genetic operators: ranking group selection (RGS), direction-based crossover (DBX) and adaptive precision-controllable mutation (APCM), is developed to optimize travelling time and torque fluctuation. Inverted generational distance, hypervolume and optimizer overhead are selected to evaluate the convergence, diversity and computational effort of algorithms. The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory. Taking a serial-parallel hybrid manipulator as instance, the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method. The effectiveness and practicability of the proposed method are verified by simulation results. This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.

In this study, a humanoid prototype of 2-DOF (degrees of freedom) lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton. To improve the detection accuracy of the human-robot interaction torque, a BPNN (backpropagation neural networks) is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor. Meanwhile, the backstepping controller is designed to realize the exoskeleton's passive position control, which means that the person passively adapts to the exoskeleton. On the other hand, a variable admittance controller is used to implement the exoskeleton's active follow-up control, which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance. To improve the wearable comfortable effect, serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters. Finally, the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.

The hydraulic exoskeleton is one research hotspot in the field of robotics, which can take heavy load due to the high power density of the hydraulic system. However, the  traditional hydraulic system is normally centralized, inefficient, and bulky during application, which limits its development in the exoskeleton. For improving the robot’s performance, its hydraulic actuating system should be optimized further. In this paper a novel hydraulic actuating system (HAS) based on electric-hydrostatic actuator  is proposed, which is applied to hip and knee joints. Each HAS integrates  an electric servo motor, a high-speed micro pump, a specific tank, and other components into a module. The specific parameters are obtained through relevant simulation according to human motion data and load requirements. The dynamic models of the HAS are built, and validated by the system identification. Experiments of trajectory tracking and human-exoskeleton interaction are carried out, which demonstrate the proposed HAS has the ability to be applied to the exoskeleton. Compared with the previous prototype, the total weight of the HAS in the robot is reduced by about 40%, and the power density is increased by almost 1.6 times.

The “visual perception + hand-eye transformation + motion planning” paradigm of robotic coordination grasping has demonstrated feasibility in unstructured scenes such as logistics. However, further developments in handling complex and dynamic environments remain challenging. To address the issue of unknown targets requiring immediate deployment for grasping tasks, this paper proposes a novel hand-eye coordinated method for progressive grasping guided by the texture keypoints of the target. First, we develop an efficient system for acquiring texture-matching templates and an estimation algorithm for the feature region that filters the precisely registered texture feature points of the target. Then, we integrate optical flow estimation to update and track the robust texture region in real time, and design a feature-based servo grasping controller to map the optical flow points of the high-registration texture region to the robot joint velocities for precise tracking. Finally, we impose spatiotemporal constraints on the planned trajectory and decouple the target motion, to achieve progressive approach and rotationally invariant grasping for both dynamic and static targets. Comprehensive experiments demonstrate that this tracking grasping method exhibits a low latency, a high precision, and robustness in complex scenarios and dynamic disturbances, with an average position accuracy of approximately 5 mm, a rotation accuracy of approximately 0.02, and an overall grasping success rate of approximately 90%.

Robotic grippers have been used in industry as end-effectors but are usually limited to operations in pre-defined workspace. However, few devices can capture irregularly shaped dynamic targets in space, underwater and other unstructured environments. In this paper, a novel continuum arm group mechanism inspired by the morphology and motions of sea anemones is proposed. It is able to dissipate and absorb the kinetic energy of a fast moving target in omni-direction and utilize multiple arms to wrap and lock the target without accurate positioning control. Wire-driven actuation systems are implemented in the individual continuum arms, achieving both bending motion and stiffness regulation. Through finite element method, the influence of different configurations of the continuum arm group on the capture performance is analyzed. A robotic prototype is constructed and tested, showing the presented arm group mechanism has high adaptability to capture targets with different sizes, shapes, and incident angles.

Robot-assisted surgery has become an indispensable component in modern neurosurgical procedures. However, existing registration methods for neurosurgical robots often rely on high-end hardware and involve prolonged or unstable registration times, limiting their applicability in dynamic and time-sensitive intraoperative settings. This paper proposes a novel fully automatic monocular-based registration and real-time tracking method. First, dedicated fiducials are designed, and an automatic preoperative and intraoperative detection method for these fiducials is introduced. Second, a geometric representation of the fiducials is constructed based on a 2D KD-Tree. Through a two-stage optimization process, the depth of 2D fiducials is estimated, and 2D-3D correspondences are established to achieve monocular registration. This approach enables fully automatic intraoperative registration using only a single optical camera. Finally, a six-degree-of-freedom visual servo control strategy inspired by the mass-spring-damper system is proposed. By integrating artificial potential field and admittance control, the strategy ensures real-time responsiveness and stable tracking. Experimental results demonstrate that the proposed method achieves a registration time of 0.23 s per instance with an average error of 0.58 mm. Additionally, the motion performance of the control strategy has been validated. Preliminary experiments verify the effectiveness of MonoTracker in dynamic tracking scenarios. This method holds promise for enhancing the adaptability of neurosurgical robots and offers significant clinical application potential.