Motion/force transmissibility is an essential property reflecting the kinematic performance of parallel robots. Research on this performance of the single-platform parallel robots (SPPRs) has long been concerned and studied. In contrast, although many innovations and applications of the high-speed articulated-platform parallel robots (APPRs) have been presented, few studies on their motion/force transmissibility have been reported. This paper deals with the motion/force transmissibility analysis of high-speed parallel robots with articulated platforms. A modified output transmission index (MOTI) for the high-speed parallel robots with articulated platforms is proposed based on a newly defined concept of equivalent transmission wrench screw. Furthermore, by having an insight into the instantaneous relative motion inside the mobile platform, a medial transmission index (MTI) is proposed to evaluate its internal motion/force transmissibility. Based on these foundations, the local transmission index (LTI) is redefined as the minimum value of the input, modified output, and medial transmission indices. Under the framework of the above performance indices, motion/force transmissibility analysis of two typical high-speed articulated-platform parallel robots, i.e., Heli4 and Par4, are presented. The proposed indices are excepted to be applied to the optimal design of high-speed parallel robots with articulated platforms.

Driving system parameters optimization, especially the optimal selection of specifications of motor and gearbox, is very important for improving high-speed parallel robots' performance. A very challenging issue is parallel robots' performance evaluation that should be able to illustrate robots' performance accurately and guide driving system parameters optimization effectively. However, this issue is complicated by parallel robots' anisotropic translational and rotational dynamic performance, and the multiparameters of motors and gearboxes. In this paper, by separating the influence of translational and rotational degrees-of-freedom (DOFs) on robots' performance, a new dynamic performance index is proposed to reflect the driving torque in instantaneous acceleration. Then, the influence of driving system's multiparameters on robots' driving torque in instantaneous acceleration and cycle time in continuous motion is investigated. Based on the investigation, an inertia matching index is further derived which is more suitable for minimizing the driving torque of parallel robots with translational and rotational DOFs. A comprehensive parameterized performance atlas is finally established. Based on this atlas, the performance of a high-speed parallel robot developed in this paper can be clearly evaluated, and the optimal combination of motors and gearboxes can be quickly selected to ensure low driving torque and high pick-and-place frequency.

To improve the adaptability of TBMs in diverse geological environments, this paper proposes a reconfigurable Type-V thrust mechanism (V-TM) with rearrangeable working states, in which structural stiffness can be automatically altered during operation. Therefore, millions of configurations can be obtained, and thousands of instances of working status per configuration can be set respectively. Nonetheless, the complexity of configurations and diversity of working states contributes to further complications for the structural stiffness algorithm. This results in challenges such as difficulty calculating the payload compliance index and the environment adaptability index. To solve this problem, we use the configuration matrix to describe the relationship between propelling jacks under reconfiguration and adopt pattern vectors to describe the working state of each hydraulic cylinder. Then, both the dynamic compatible equation between propeller forces of the hydraulic cylinders and driving forces, and the kinematic harmonizing equation between the hydraulic cylinder displacements and their deformations are established. Next, we derive the stiffness analytical equation using Hooke’s law and the Jacobian Matrix. The proposed approach provides an effective algorithm to support structural rigidity analysis, and lays a solid theoretical foundation for calculating the performance indexes of the V-TM. We then analyze the rigidity characteristics of typical configurations under different working states, and obtain the main factors affecting structural stiffness of the V-TM. The results show the deviation degree of structural parameters in hydraulic cylinders within the same group, and the working status of propelling jacks. Finally, our constructive conclusions contribute valuable information for matching and optimization by drawing on the factors that affect the structural rigidity of the V-TM.

Endoscopic images are often degraded by uneven illumination, motion blur, and tissue occlusion, which obscure critical anatomical details and complicate surgical manipulation. This issue is particularly pronounced in single-port endoscopic surgery, where the imaging capability of the camera is further constrained by limited working space. While deep learning-based enhancement methods have demonstrated impressive performance, most existing approaches remain too computationally demanding for real-time surgical use. To address this challenge, we propose an efficient stepwise endoscopic image enhancement framework that introduces an implicit degradation representation as an intermediate feature to guide the enhancement module toward high-quality results. The framework further exploits the temporal continuity of endoscopic videos, based on the assumption that image degradation evolves smoothly over short time intervals. Accordingly, high-quality degradation representations are estimated only on key frames at fixed intervals, while the representations for the remaining frames are obtained through fast inter-frame propagation, thereby significantly improving computational efficiency while maintaining enhancement quality. Experimental results demonstrate that our method achieves an excellent balance between enhancement quality and computational efficiency. Further evaluation on the downstream segmentation task suggests that our method substantially enhances the understanding of the surgical scene, validating that implicitly learning and degradation representation propagation offer a practical pathway for real-time clinical application.