This article presents the design, fabrication, modeling, and preliminary tests of a bloodworm-inspired soft gripper for universal grasping. The gripper was designed and fabricated based on a toy called water snake wiggly (WSW). The toroidal WSW can evert itself inside-out or outside-in, just like a bloodworm everting its teeth outside to hunt and inside to feed. By driving a WSW rolling itself outside-in to wrap around the items, a bloodworm-inspired gripper was achieved with a flexible and passive form-fitting grasp. To enhance the capability of the gripper, two alternative detachable modules were added to the gripper—a vacuum suction cup for handling objects with smooth nonporous surfaces and an end-needle for taking in and expelling noncorrosive liquids like a syringe. We analyzed the working principles of the gripper and derived the relationship between the gripper's holding force and the objects' scale. Preliminary experiments with a motor-driven gripper prototype were conducted to verify its performance. The experimental results conform well with our theoretical analysis and also indicate the gripper's good universal grasping capacity and reliability in handling a wide range of objects with different surface shapes, geometric dimensions, and stiffness. In addition, the gripper has the unique abilities to pick more than one object during a maneuver, grasp multiple objects in a row without releasing the former ones, and even grasp powdered objects. These have presented a challenge for the existing robotic grippers.

As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F₁ and F₃, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.

The tiger beetle can maintain its stability by controlling the interaction force between its legs and an unstructured terrain while it runs. The biomimetic hexapod robot mimics a tiger beetle, and a comprehensive force sensing system combined with certain algorithms can provide force information that can help the robot understand the unstructured terrain that it interacts with. This study introduces a complicated leg force sensing system for a hexapod robot that is the same for all six legs. First, the layout and configuration of sensing system are designed according to the structure and sizes of legs. Second, the joint toque sensors, 3-DOF foot-end force sensor and force information processing module are designed, and the force sensor performance parameters are tested by simulations and experiments. Moreover, a force sensing system is implemented within the robot control architecture. Finally, the experimental evaluation of the leg force sensor system on the hexapod robot is discussed and the performance of the leg force sensor system is verified.

The positioning accuracy of a robot directly affects the quality of its operations. In this study, a calibration method is proposed based on combining a model with least-squares support vector regression (LSSVR) to improve robot positioning accuracy. First, a geometric error model of the robot is established. Second, singular value decomposition (SVD) and physical model analysis method are employed to process the coupling parameters in the error model to improve the accuracy and efficiency of identification. Third, as nongeometric errors hinder the construction of an accurate and complete mathematical model and affect the residual positioning errors of the robot, LSSVR is used to compensate for the residual positioning errors caused by nongeometric errors. The proposed method thus improves the accuracy and robustness of finite sample estimation. Finally, an experiment is performed on an IRB1410 robot with a parallelogram mechanism. The maximum/mean positioning errors of the robot decrease from 2.0348/1.0978 mm to 0.1659/0.0733 mm, and the effectiveness of the proposed method is verified. The proposed method has higher prediction accuracy and stability for small samples than other methods.

Retinal microsurgery, which requires surgeons to perform precise operations on delicate tissue in a tiny eye, is considered one of the most technically demanding operations. Lack of force perception is one of the major problems faced during retinal microsurgery. In order to solve the problem of lack of force sensing, many 3 degree of freedom (DOF) force sensing instruments have been developed. However, they all adopt complex structures, and the accuracy of axial force still has room for improvement. In this paper, we presented a 3-DOF force sensor based on fiber Bragg grating (FBG) sensors for retinal microsurgery. An improved FBG configuration which simplifies the structure of the force sensor and the corresponding temperature compensation method was designed. Two optical fibers with single FBG sensor and an optical fiber with dual FBG sensors are longitudinally attached along the grooves of a stainless steel tube. The calibration method used in the force sensor is a combination of linear calibration method and the least squares support vector regression (LSSVR), which can effectively reduce the influence of linear and nonlinear errors. The results of calibration experiment demonstrate that the hybrid calibration method has better accuracy than using linear or nonlinear method alone. Verification experiment and temperature compensation experiment show that the 3-DOF force sensor proposed has the ability to provide accurate and stable estimation of 3 dimensional forces in a temperature changing environment.