The wearable exoskeleton system is a typical strongly coupled human–robotic system. Human–robotic is the environment for each other. The two support each other and compete with each other. Achieving high human–robotic compatibility is the most critical technology for wearable systems. Full structural compatibility can improve the intrinsic safety of the exoskeleton, and precise intention understanding and motion control can improve the comfort of the exoskeleton. This paper first designs a physiologically functional bionic lower limb exoskeleton based on the study of bone and joint functional anatomy and analyzes the drive mapping model of the dual closed-loop four-link knee joint. Secondly, an exoskeleton dual closed-loop controller composed of a position inner loop and a force outer loop is designed. The inner loop of the controller adopts the PID control algorithm, and the outer loop adopts the adaptive admittance control algorithm based on human–robot interaction force (HRI). The controller can adaptively adjust the admittance parameters according to the HRI to respond to dynamic changes in the mechanical and physical parameters of the human–robot system, thereby improving control compliance and the wearing comfort of the exoskeleton system. Finally, we built a joint simulation experiment platform based on SolidWorks/Simulink to conduct virtual prototype simulation experiments and recruited volunteers to wear rehabilitation exoskeletons to conduct related control experiments. Experimental results show that the designed physiologically functional bionic exoskeleton and adaptive admittance controller can significantly improve the accuracy of human–robotic joint motion tracking, effectively reducing human–machine interaction forces and improving the comfort and safety of the wearer. This paper proposes a dual-closed loop four-link knee joint exoskeleton and a variable admittance control method based on HRI, which provides a new method for the design and control of exoskeletons with high compatibility.

Multirotor drones are widely used in fields such as environmental monitoring, agricultural inspection, and package delivery, but they still face numerous challenges in durability and aerial operation capabilities. To address these issues, this paper presents a biomimetic leg-claw mechanism (LCM) inspired by the biomechanics of birds. The claw of the LCM adopts a bistable gripper design that can rapidly close through external impact or actively close via the coordination of internal mechanisms. Additionally, its foldable, parallelogram-shaped legs bend under external forces, stretching the main tendon. A ratchet and pawl mechanism at the knee joint locks the leg in the bent position, thereby enhancing the gripping force of the claw. This paper calculates and experimentally verifies the degrees of freedom in different states, the forces required to open and close the gripper, the application scenarios of active and passive grasping, and the maximum load capacity of the mechanism. Furthermore, perching experiments demonstrate that the LCM enables the drone to perch stably on objects of varying diameters.

The Yoshimura tubular origami mechanism possesses numerous advantageous properties and, when integrated with advanced material technologies, can be applied across various engineering disciplines. However, current research on Yoshimura origami predominantly focuses on centrally symmetric tubular origami mechanisms, which restricts the structural forms and motion patterns of these mechanisms. Drawing inspiration from the biological concept of “morphological variation,” we propose a novel tubular origami mechanism based on the Yoshimura pattern, which is the main contribution of this research. We analyze the Yoshimura planar crease elements and introduce both heterocellular and homocellular tubular origami mechanisms. Furthermore, we establish the origami topology matrices for the Yoshimura tubular origami mechanisms. This research also investigates complex motion forms that differ from traditional Yoshimura origami mechanisms, including macroscopic twisting and compound movements, thereby providing an intuitive design approach and extensive structural guidance for research in Yoshimura tubular origami engineering. Based on the tubular origami mechanism, we created an origami robot and investigate its motion characteristics.

To simplify the grasping mechanism of drones, this paper presents a dual-function landing gear with an actively closing mechanism, designed for coaxial drones. The proposed design integrates self-locking and clutch mechanisms, enabling the landing gear to be actuated by a single motor. This novel approach eliminates the need for additional actuators, optimizing the drone’s takeoff, landing, and grasping capabilities while reducing energy consumption compared to traditional designs. Kinematic and static analyses, along with experimental evaluations, were conducted to assess the grasping performance of the mechanism. Experimental results demonstrate that the landing gear can close within 0.2 s and provide a grasping force exceeding 10 N. Compared to conventional designs, this solution offers a reliable and energy-efficient approach for dual-function drone operations.

Interventional robots play a crucial role in surgical procedures, where accurate force measurement is essential for enhancing safety. Compliant mechanisms utilize material deformation to achieve millinewton-scale force output and millimeter-level displacement with high repeatability. Motivated by this, we propose a method for measuring the catheter force by integrating a compliant mechanism and a sensor. First, we designed an operating force detection module. It comprises a double-parallelogram structure with four elastic units, a catheter drive module, and a sensor. The sensor connects the compliant mechanism to the base. Second, stiffness and gravity compensation models were established and validated experimentally. Finally, we constructed an experimental platform to evaluate the force measurement accuracy, drive accuracy, and real-time detection capability. Experimental results demonstrate that the proposed method achieves a maximum detection error of 0.1482 N, an average error of 0.0096 N, a resolution of 0.01 N, and an average axial delivery error of 0.8287 mm. Additionally, a master–slave control framework was developed, along with a master controller that manipulates the slave robot to deliver the catheter within a vascular phantom, while simultaneously displaying real-time force information via the human–computer interaction interface.

Addressing the issue of excessive manual intervention in discharging fermented grains from underground tanks in traditional brewing technology, this paper proposes an intelligent grains-out strategy based on a multi-degree-of-freedom hybrid robot. The robot’s structure and control system are introduced, along with analyses of kinematics solutions for its parallel components and end-effector speeds. According to its structural characteristics and working conditions, a visual-perception-based motion control method of discharging fermented grains is determined. The enhanced perception of underground tanks’ positions is achieved through improved Canny edge detection algorithms, and a YOLO-v7 neural network is employed to train an image segmentation model for fermented grains’ surface, integrating depth information to synthesize point clouds. We then carry out the downsampling and three-dimensional reconstruction of these point clouds, then match the underground tank model with the fermented grain surface model to replicate the tank’s interior space. Finally, a digging motion control method is proposed and experimentally validated for feasibility and operational efficiency.