Yiyong Yang

Existing deep-well multi-node observation instrument hoisting and lowering systems suffer from poor compartment coupling adaptability, fuzzy anti-jamming judgment criteria, non-standard operation procedures, and lack of classified hoisting control strategies, which easily cause compartment jamming. Aiming at the above research gaps, this study proposes a standardized full-process hoisting/lowering technology tailored for deep-well multi-node observation instruments. This paper first summarizes and classifies mainstream instrument compartment coupling and hoisting schemes and quantitatively compares their application boundaries, operational risks and applicable borehole conditions. Then it optimizes the integral system layout, compartment installation mode and unified standardized operating workflow for downhole instrument deployment. Furthermore, the downhole jamming mechanism and rapid identification means of instrument compartments are clarified. Classified collaborative control strategies are also established to match different hoisting/lowering working conditions. The research results fill the gap in standardized anti-jamming operation specification for deep-well multi-node instrument deployment and can effectively reduce downhole fault handling time and observation operation interruption frequency, providing feasible technical support for the safe and efficient deployment of downhole observation equipment.

To enhance the efficiency, safety, and data accuracy of drilling engineering, this study developed an integrated business management platform for drilling engineering grassroots units based on the Business Model Driven (BMD) approach. The platform is built on a “five horizontal, three vertical” cloud computing architecture, establishing a five-layer system from the infrastructure layer to the user layer horizontally, and supported by standard specifications, safety, and maintenance systems vertically, enabling collaboration across multiple business scenarios and data integration. Currently, four major modules with over 20 functionalities have been developed, supporting applications such as task coordination, engineering supervision, data analysis, and accident handling. Operational results demonstrate that the platform effectively promotes integrated management of drilling engineering through real-time data sharing, full-process quality control, and intelligent decision-making, thereby enhancing operational quality and safety, reducing accident risks, and providing critical technological support for the digital transformation and upgrading of the drilling industry.

Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic deformation. In this paper, we describe a new deformable object manipulation method including soft contact simulation, manipulation learning, and sim-to-real transfer. We propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. For a more realistic contact simulation, a new simulation environment including elastic, plastic, and elastoplastic deformations is created. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. Finally, we build a real experimental platform to complete the sim-to-real transfer and achieve a 90% success rate on difficult tasks such as cylinder and sphere. To test the robustness of our method, we use plasticine of different hardness and sizes to repeat the tasks including cylinder and sphere. The experimental results show superior performances of deformable object manipulation with the proposed method.

This paper proposes a vision-based tactile sensor (VBTS) embedded into the soft hand palm, named PaLmTac. We adopt a distributed modality design instead of overlaying function layers. On the one hand, the problem of unrelated modality integration (texture and temperature) is solved. On the other hand, combining regional recognition can avoid mixed unrelated information. Herein, a Level-Regional Feature Extraction Network (LRFE-Net) is presented to match the modality design. We leverage feature mapping, regional convolution, and regional vectorization to construct the regional recognition mechanism, which can extract features in parallel and control fusion degrees. The level recognition mechanism balances the learning difficulty of each modality. Compared with the existing VBTSs, the PaLmTac optimizes unrelated modality integration and reduces fusion interference. This paper provides a novel idea of multimodal VBTS design and sensing mechanism, which is expected to be applied to human-computer interaction scenarios based on multimodal fusion.

In this paper, a novel electric-hydraulic power steering (EHPS) system and a vehicle stability coordination control algorithm are proposed which can not only ensure the accuracy of the trajectory tracking but also solve the coordination control problem between the stability of the lateral control and the stability of the roll in the extreme condition. Firstly, the EHPS system is designed to provide accurate control input of front wheel angle for vehicle lateral dynamics control. Secondly, on the basis of optimal preview theory, a new trajectory tracking fusion controller combined with sliding mode control is proposed to improve the accuracy and stability of the system in the process of vehicle lateral trajectory tracking control. Then, the stability domain boundary function of the phase plane is determined according to the phase plane of the sideslip angle-yaw rate, and the stability margin of the phase plane is calculated during the steering process. Finally, considering the tracking accuracy, lateral stability and roll stability performance in the process of trajectory tracking, the linear weighted algorithm is used to coordinate above three objectives, and the HIL bench test and real vehicle experiment verify that the proposed algorithm has good reliability and effectiveness.

Visuo-tactile skins (VTS) are classified into coated-type, marker-type and thermochromic-type. Coating wear resistance and spatial resolution are central issues for the coated-type VTS. This paper proposes a wire-drawing process applied to the VTS to enhance the coating robustness. The wire-drawing coating has more stable mechanical properties, including high adhesion, adaptive deformation, and rupture resistance. Under different degrees of deformation, it can remain continuous and undamaged. In addition, the wire-drawing coating has a nanoscale particle distribution, whose microstructures are similar to the spraying coating to provide micron-scale spatial resolution. Via the wear resistance test, the wire-drawing coating has higher wear resistance than the spraying coating. Further, the VTS combined with a multitask learning model can identify textures and clothing properties of 18 different fabrics with more than 98% recognition accuracy. The results show that the VTS using the new process can provide highly learnable tactile data.

When the human body experiences heat stress beyond the normal range, it may affect health and work efficiency to a certain extent. This study aimed to investigate the effectiveness of cold stimulation on Dazhui point (GV14), bilateral Xinshu points (B15), bilateral Danshu points (B19), and bilateral Quchi points (LI11) in the body to relieve heat strain in a high-temperature environment. The experiment results showed that the skin temperature (Tskin), tympanic temperature (Tty), heart rate (HR), the physiological strain index based on heart rate (PSIHR), thermal sensation (TS), and thermal comfort (TC) of the participants with acupoints cold stimulation were better than those without acupoints cold stimulation (p < 0.05). Tskin decreased by an average of 0.04 ± 0.46Ԩ. Tty decreased by an average of 0.08 ± 0.21Ԩ. The study concluded that cold stimulation at acupoints could reduce the heat strain and improve the thermal comfort of the human body.

This letter proposes a vision-based tactile sensor named TIRgel, leveraging visual integration to simplify the sensing system. First, the sensor achieves conversion of visual and tactile modality via focus adjustment. Under far-focus imaging, the camera can observe external environments; Under near-focus imaging, we perform total internal reflection (TIR) within the lenticular elastomer to visualize tactile features. The effectiveness of the sensing mechanism is verified via MPM (Material Point Method) simulation. Second, the TIRgel can capture and extract micron-scale tactile details based on luminosity information. Tactile imaging has the natural fusion of visual and tactile information, where the luminosity boundary reflects the texture feature, and the background contains the object's appearance information. Inherent data fusion simplifies multimodal data acquisition and reduces the information processing difficulty. Finally, the TIRgel can resist the interference of ambient brightness via internal illumination and has a higher adaptive capacity to the environment than unidirectional perspective VBTSs. We believe that it has the potential to provide visual-tactile integration for robot operation.