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Abstract The unmanned aerial vehicle (UAV) and the unmanned ground vehicle (UGV) can complete complex tasks through information sharing and ensure the mission execution capability of multiple unmanned carrier platforms. At the same time, cooperative navigation can use the information interaction between multi‐platform sensors to improve the relative navigation and positioning accuracy of the entire system. Aiming at the problem of deviation of the system model due to gross errors in sensor measurement data or strong manoeuvrability in complex environments, a robust and adaptive UGV‐UAV relative navigation and positioning algorithm is proposed. In this paper, the relative navigation and positioning of UGV‐UAV is studied based on inertial measurement unit (IMU)/Vision. Based on analyzing the relative kinematics model and sensor measurement model, a leader (UGV)‐follow (UAV) relative navigation model is established. In the implementation of the relative navigation and positioning algorithm, the robust adaptive algorithm and the non‐linear Kalman filter (extended Kalman filter [EKF]) algorithm are combined to dynamically adjust the system state parameters. Finally, a mathematical simulation of the accompanying and landing process in the UGV‐UAV cooperative scene is carried out. The relative position, velocity and attitude errors calculated by EKF, Robust‐EKF and Robust‐Adaptive‐EKF algorithms are compared and analyzed by simulating two cases where the noise obeys the Gaussian distribution and the non‐Gaussian distribution. The results show that under the non‐Gaussian distribution conditions, the accuracy of the Robust‐Adaptive‐EKF algorithm is improved by about two to three times compared with the EKF and Robust‐EKF and can almost reach the relative navigation accuracy under the Gaussian distribution conditions. The robust self‐adaptive relative navigation and positioning algorithm proposed in this paper has strong adaptability to the uncertainty and deviation of system modelling in complex environments, with higher accuracy and stronger robustness.

This study utilizes a lab-developed pneumatic-extrusion condensing 3D food printer to prepare astragalus–starch mixed gels by blending different ratios of astragalus polysaccharide and wheat starch and applies these gels to 3D printing experiments. The aim of this paper is to investigate the impacts of mixed-gel concentrations on printing outcomes in order to identify the optimal blending ratio. Under this rationale, the effects of printing layer height and nozzle diameter on print quality were studied. Single-factor analysis and response surface methodology were employed to optimize the experiments and determine the optimal printing process parameters for the astragalus–starch mixed gels. The results indicate that an increase in astragalus polysaccharide content leads to a decrease in the sedimentation rate of the mixed gels and a tendency towards a more fluid consistency. After storage of samples in a sealed space for equal durations, it was found that an increase in astragalus polysaccharide content enhances the textural properties of the mixed gels, with optimal printing effects achieved at a 2% polysaccharide content. The optimal print quality is achieved when the ratio of nozzle diameter to layer height is between 0.5 and 0.55. The influence order of printing process parameters on the overall completion rate of the samples is nozzle diameter > printing speed > fill rate. The predicted optimal printing parameters are a nozzle diameter of 0.6 mm, a printing speed of 767 mm/min, and a fill rate of 83%, with a predicted overall completion rate of the printed samples at 99.45%. Experimental validation revealed an actual overall completion rate of the printed samples at 99.52%, slightly higher than the predicted value. This discrepancy was attributed to the precision of the measurement methods and the variability in the printing process. The study demonstrates that the addition of astragalus polysaccharide significantly improves the 3D printing molding effect of wheat starch, and the printing parameter settings obtained by response surface optimization effectively enhance printing accuracy. This research provides experimental evidence and parameter optimization references for the application of non-starch polysaccharides in starch-based 3D food printing. [ABSTRACT FROM AUTHOR]

To improve the potassium availability of feldspar at ordinary temperatures, the mechanical grinding and addition of sodium hydroxide/salts were employed to study the effects of mechanical activation and strong alkali addition on particle characteristics, water-soluble potassium, and the available potassium of feldspar. A laser particle size analyzer was utilized for the direct determination of particle size distribution (PSD) using ground samples. The Brunauer–Emmett–Teller (BET) method was employed for specific surface areas. X-ray diffraction (XRD) was employed for structural characterization, scanning electron microscopy (SEM) for morphology exploration, and energy dispersive spectroscopy (EDS) to determine the chemical composition of potassium feldspar powder. The results revealed that the mechanical activation of potassium feldspar could reduce the particle size and produce agglomerated nanoparticles in the later period. The addition of NaOH and sodium salt did not cause agglomeration, and NaOH dissolved the nanoparticles. The water-soluble potassium content of feldspar in each treatment increased during mechanical grinding, from 21.64 mg kg−1 to 1495.81 mg·kg−1, by adding NaOH 5% weight of potassium feldspar powder and to 3044.08 mg·kg−1 by adding NaOH 10% weight with effects different from those of mechanical shaking. By comparison, only 162.93 mg·kg−1 water-soluble potassium was obtained by adding NaOH 5% weight. The dissolved potassium in the former case was significantly higher than in the latter, and the addition of NaOH and sodium salts significantly enhanced the water-soluble potassium contents due to ion exchange. Furthermore, the addition of sodium hydroxide improved the water-soluble potassium due to its mechanochemical action on potassium feldspar. The mechanical energy changed the crystal structure of potassium feldspar, explaining the increase in available potassium. The addition of sodium salts did not promote change in the feldspar's structure, thereby did not raise the available potassium content. The reason for this was related to the mechanochemical action on sodium hydroxide and feldspar, which could promote the dissolution of fine particles, thereby incrementing the available potassium. [ABSTRACT FROM AUTHOR]