Swimming microrobots guided in the circulation system offer considerable promise in precision medicine but currently suffer from problems such as limited adhesion to blood vessels, intensive blood flow, and immune system clearance—all reducing the targeted interaction. A swimming microrobot design with clawed geometry, a red blood cell (RBC) membrane–camouflaged surface, and magnetically actuated retention is discussed, allowing better navigation and inspired by the tardigrade’s mechanical claw engagement, coupled to an RBC membrane coating, to minimize blood flow impact. Using clinical intravascular optical coherence tomography in vivo, the microrobots’ activity and dynamics in a rabbit jugular vein was monitored, illustrating very effective magnetic propulsion, even against a flow of ~2.1 cm/s, comparable with rabbit blood flow characteristics. The equivalent friction coefficient with magnetically actuated retention is elevated ~24-fold, compared to magnetic microspheres, achieving active retention at 3.2 cm/s, for >36 hours, showing considerable promise across biomedical applications.

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.

Amphibious robots can undertake various tasks in terrestrial and aquatic environments for their superior environmental compatibility. However, the existing amphibious robots usually utilize multi‐locomotion systems with transmission mechanisms, leading to complex and bulky structures. Here, a miniature amphibious robot based on vibration‐driven locomotion mechanism is developed. The robot has two unique rigid‐flexible hybrid modules (RFH‐modules), in which a soft foot and a flexible fin are arranged on a rigid leg to conduct vibrations from an eccentric motor to the environment. Then, it can run on ground with the soft foot adopting the friction locomotion mechanism and swim on water with the flexible fin utilizing the vibration‐induced flow mechanism. The robot is untethered with a compact size of 75 × 95 × 21 mm³ and a small weight of 35 g owing to no transmission mechanism or joints. It realizes the maximum speed of 815 mm s^–1 on ground and 171 mm s^–1 on water. The robot, actuated by the RFH‐modules based on vibration‐driven locomotion mechanism, exhibits the merits of miniature structure and fast movements, indicating its great potential for applications in narrow amphibious environments.

As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

This paper investigates the dynamic parameter identification for flexible joints without additional sensors. The estimation accuracy of traditional methods based on the original dynamic model is limited by measurement of angular acceleration. Recently, approaches using integral operations are raised to solve this problem. However, the integral operations easily result in error accumulation and deteriorate the parameter estimation accuracy. This study proposes a new dynamic parameter identification method for flexible joints. By constructing an alternative system function and estimating its outputs through non-model-based adaptive control, we avoid the measurement of angular acceleration and integral operations. On this basis, dynamic parameters of flexible joints are estimated using the recursive least squares (RLS) method. The experimental results indicate that the proposed method possesses better estimation accuracy than the general RLS- and integral-based methods. It is also validated that the proposed method can provide accurate estimation even under a relatively low-resolution system. Applications to model-based motion control and physical human-robot interaction are also verified.

At present, the production lines of mobile phones are mainly manual and semi-automatic. Robots are the most important tools used to improve the intelligence level of industry production. The design of an intelligent robotic assembly system is presented in this paper, which takes the assembly of screen components and shell components as examples. There are major factors restricting the application of robots, such as the calibration of diversified objects, the moving workpiece with incomplete information, and diverse assembly types. A method was proposed to solve these technological difficulties. The multi-module calibration method is used to transform the pose relationship between the robot, the conveyor belt, and the two cameras in the robot assembly system. Aiming at a workpiece moving with incomplete information, the minimum matrix method is proposed to detect the position. Then dynamic fetching is realized based on pose estimation of the moving workpiece. At last, the template matching method is used to identify the assembly area of the workpiece type. The proposed method was verified on a real platform with a LINKHOU LR4-R5 60 robot. Results showed that the assembly success rate was above 98%, and the intelligent assembly system of the robot can realize the assembly of mobile phone screens and back shells without any staff.