Polyethylene oxide (PEO)-based solid-state electrolytes for lithium-ion batteries have garnered significant interest due to their enhanced potential window, high energy density, and improved safety features. However, the issues such as low ionic conductivity at ambient temperature, substantial ionic conductivity fluctuations with temperature changes, and inadequate electrolyte interfacial compatibility hinder their widespread applications. Electrospinning is a popular approach for fabricating solid-state electrolytes owing to its superior advantages of adjustable component constitution and the unique internal fiber structure of the resultant electrolytes. Thus, this technique has been extensively adopted in related studies. This review provides an overview of recent advancements in optimizing the performance of PEO solid-state electrolytes via electrospinning technology. Initially, the impacts of different lithium salts and their concentrations on the performance of electrospun PEO-based solid-state electrolytes were compared. Subsequently, research pertaining to the effects of various additives on these electrolytes was reviewed. Furthermore, investigations concerning the enhancement of electrospun solid-state electrolytes via modifications of PEO molecular chains are herein detailed, and lastly, the prevalent challenges and future directions of PEO-based solid-state electrolytes for lithium-ion batteries are summarized.

Study Design.: Prospective observational study. Objective.: This article aims to develop a spatial augmented reality-based surgical navigation system to assist in the placement of pedicle screws in minimally invasive spine surgery and to verify the accuracy of this method. Summary of Background Data.: Due to their high accuracy and good visualization ability, augmented reality surgical navigation systems have been used in minimally invasive surgeries. However, augmented reality does not allow information to be shared and restricts doctors. Methods.: A surgical navigation system that implements augmented reality based on a projector can be used to realize the external visualization of virtual organs and surgical information through an improved multiple information fusion method. Using fiducial markers and imaging technology, the patient's spatial position is tracked and registered in real time. All the information is accurately fused with the patient's back skin, and the surgeon can see surgical information such as the preoperative plan and bones. Phantom experiments were used to verify the accuracy and effectiveness of the system. Results.: In the phantom experiments, the accuracy of the pedicle screw insertion point on the dummy's skin was 0.441 ± 0.214 mm, the average location error into the dummy's body was 1.645 ± 0.355 mm, and the average axial and sagittal angulation errors were <0.9°. Conclusion.: This article introduces and verifies the design of a new surgical navigation system based on spatial augmented reality for lumbar pedicle screw implantation. The system passed a series of phantom accuracy experiments. Compared with the traditional augmented reality navigation system, this system avoids the use of glasses and truly realizes the effect of naked-eye 3D, which is more convenient for doctors to use for communication during an operation. Level of Evidence: N/A

Laser technology can be used to control weeds by irradiating the apical meristematic tissue (AMT) of weeds when they are still seedlings. Two factors are necessary for the successful large-scale implementation of this technique: the ability to accurately identify the apical meristematic tissue and the effectiveness of the localization algorithm used in the process. Based on this, this study proposes a lightweight weed AMT localization algorithm based on YOLO (look only once) instance segmentation. The YOLOv8n-seg network undergoes a lightweight design enhancement by integrating the FasterNet lightweight network as its backbone, resulting in the F-YOLOv8n-seg model. This modification effectively reduces the number of parameters and computational demands during the convolution process, thereby achieving a more efficient model. Subsequently, F-YOLOv8n-seg is combined with the connected domain analysis algorithm (CDA), yielding the F-YOLOv8n-seg-CDA model. This integration enables the precise localization of the AMT of weeds by calculating the center-of-mass coordinates of the connected domains. The experimental results indicate that the optimized model significantly outperforms the original model; the optimized model reduces floating-point computations by 26.7% and the model size by 38.2%. In particular, the floating-point calculation is decreased to 8.9 GFLOPs, and the model size is lowered to 4.2 MB. Comparing this improved model against YOLOv5s-seg and YOLOv10n-seg, it is lighter. Furthermore, it exhibits exceptional segmentation accuracy, with a 97.2% accuracy rate. Experimental tests conducted on five different weed species demonstrated that F-YOLOv8n-seg-CDA exhibits strong generalization capabilities. The combined accuracy of the algorithm for detecting these weeds was 81%. Notably, dicotyledonous weeds were detected with up to 94%. Additionally, the algorithm achieved an average inference speed of 82.9 frames per second. These results indicate that the algorithm is suitable for the real-time detection of apical meristematic tissues across multiple weed species. Furthermore, the experimental results demonstrated the impact of distinctive variations in weed morphology on identifying the location of the AMT of weeds. It was discovered that dicotyledonous and monocotyledonous weeds differed significantly in terms of the detection effect, with dicotyledonous weeds having significantly higher detection accuracy than monocotyledonous weeds. This discovery can offer novel insights and avenues for future investigation into the identification and location of the AMT of weeds.

To address the issues of low trajectory tracking accuracy and difficulties in tuning control parameters for crawler robots operating in uneven terrains, this paper proposes a trajectory tracking control method. The method is based on improved particle swarm optimization and sliding mode active disturbance rejection control (SPSO-SMADRC). Firstly, considering the influence of disturbances such as terrain undulations and soil inhomogeneity on trajectory deviation, the kinematic and dynamic models of the crawler robot are established. A vector field guidance approach is employed to transform the trajectory tracking task into a heading control problem. The heading angle is adaptively adjusted based on the position deviation and path curvature. A nonlinear extended state observer is introduced to estimate external disturbances. A velocity-based SMADRC controller is designed to dynamically regulate the robot’s linear and angular velocities. This allows real-time correction of the robot’s motion. To overcome the tendency of the standard particle swarm optimization (PSO) algorithm to fall into local optima during controller parameter tuning, a nonlinear dynamic adjustment strategy was adopted. This strategy adaptively adjusts the inertia weight and learning factors, enhancing the algorithm’s global search capability. Comparative experiments were conducted using two types of curved trajectories: U-shaped and V-shaped paths. The experimental results show that, under the proposed SPSO-SMADRC method, the crawler robot achieved maximum position errors of 8.28 cm and 9.26 cm, average position errors of 1.41 cm and 2.94 cm, and maximum heading angle deviations of 0.56 rad and 0.87 rad. The standard deviations of the position errors were 3.19 and 4.28, respectively. Compared with conventional PSO-based SMADRC and standard SMADRC methods, the proposed approach improved the navigation tracking accuracy. In the U-shaped trajectory, the maximum position error was reduced by 19.22% and 38.21%, the average position error by 40.00% and 65.53%, and the heading angle error by 28.21% and 74.66%. In the V-shaped trajectory, the maximum position error was reduced by 17.39% and 38.95%, the average position error by 51.71% and 52.04%, and the heading angle error by 80.58% and 84.49%. These results demonstrate that the proposed SPSO-SMADRC method significantly enhances trajectory tracking performance and system robustness. It provides effective support for high-precision autonomous navigation of crawler robots in complex and unstructured environments.

This paper presents an anti-swing control method to prevent situations where inspection robots detach and fall off transmission lines during obstacle crossing due to excessive swing angles caused by the rotation of the robot around the transmission line. Firstly, an obstacle-crossing model for the inspection robot was constructed and the causes of robot swinging phenomena were analyzed, in addition to their impact on obstacle crossing stability. By combining this with the obstacle-crossing model, a moment balance equation was established for the inspection robot. This equation can be used to solve mapping relationships between body offset and the tilt angle of transmission line gripping arms. We propose an anti-swing control strategy by adjusting the angle of the transmission line gripping arm’s pitching joint to make the body offset approach zero, and by utilizing the advantages of fuzzy logic in the fuzzy PID algorithm compared with the traditional PID algorithm, it can adaptively avoid the occurrence of robot swinging phenomena. The experimental results of obstacle-crossing experiments under no wind and wind turbulence conditions indicated that the proposed anti-swing control method in this study can effectively keep the body offset to within 3 mm. Compared with the methods of not using anti-swing control and using traditional PID anti-swing control, in the absence of wind effects, the peak values of body offset were reduced by 96.53% and 18.85%, respectively. Under the influence of wind turbulence, the peak values of body offset were reduced by 97.02% and 27.12%, respectively. The effectiveness of the anti-swing control method proposed in this paper has thus been verified.