A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm

Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency

Optimization of Truck–Loader Matching Based on a Simulation Method for Underground Mines

The choice of transportation system has an important impact on the production efficiency and economic behavior of underground mines. Trackless vehicle transportation has gradually become the main method in underground mines because mining companies have realized that mining efficiency can be improved using advanced vehicle mechanization and automation in the mining process. The extracted ore is loaded onto trucks by loaders in situ, then the trucks drive to ore passes for unloading Trucks load and unload ore in a cyclical manner between stopes and ore passes. Numerous trucks drive in tunnels simultaneously to achieve production targets, and there are interactions and influences among trucks, such as blocking and queuing, due to limited underground space. To address this issue, a transportation route model was built, and the ore transportation process was divided into three parts, including ore loading, truck transportation, and ore unloading. The simulation method was applied to optimize the number of loaders and trucks under the constraints of stope production capacity, transportation route and capacity, and vehicle capacity, to achieve the optimal vehicle utilization rate and transportation capability. The Monte Carlo simulation method was utilized to take the uncertainties of the transportation parameters into account to improve the robustness of the simulation results. The model was verified using the case study of an underground gold mine located in Shandong Province, China, with the objective of accomplishing optimal truck–loader matching considering various stopes in a mining area

Development of new 7%Ni steel for LNG storage tanks

Considering the scarcity of nickel in China and cost-saving for construction of LNG storage tanks, it is very significant to develop a new generation of low-nickel steel for storage tanks. Herein, the material performance and weldability of the new low-nickel (7%Ni) steel were analyzed by tests. The results indicate that the 7%Ni steel plate has high strength and toughness, with a tensile strength of 690-730 MPa and a low-temperature impact energy of 150-200 J at-196 ℃, and the welded joints have good comprehensive performance. Definitely, the tensile strength of the welded joints is no less than 690 MPa, the impact energy of the welds and the heat affected zones is not less than 80 J, and the hardness of the base material and the heat affected zones is not greater than 340 HV10. Generally, the performance of the new 7%Ni steel plate for LNG storage tanks satisfies the technical requirements for the construction and welding of LNG storage tanks

Characterization of Six Diamide Insecticides on Ryanodine Receptor: Resistance and Species Selectivity

Ryanodine receptor (RyR) has been used as an insecticide target to control many destructive agricultural pests. The effectiveness of these insecticides has been limited by the spread of resistance mutations identified in pest RyRs, but the detailed molecular impacts of the individual mutations on the activity of different diamide compounds have not been fully explored. We created five HEK293 cell lines stably expressing wild type rabbit RyR1, wild type Spodoptera frugiperda RyR (Sf RyR), or Sf RyR carrying different resistance mutations, including G4891E, G4891E/I4734M, and Y4867F, respectively. R-CEPIA1er, a genetically encoded fluorescent protein, was also introduced in these cell lines to report the Ca2+ concentration in the endoplasmic reticulum. We systematically characterized the activities of six commercial diamide insecticides against different RyRs using the time-lapse fluorescence assay. Among them, cyantraniliprole (CYAN) displayed the highest activity against all three resistant Sf RyRs. The good performance of CYAN was confirmed by the toxicity assay using gene-edited Drosophila expressing the mutant RyRs, in which CYAN showed the lowest LD50 value for the double resistant mutant. In addition, we compared their acitivty between mammalian and insect RyRs and found that flubendiamide has the best insect-selectivity. The mechanism of the anti-resistance property and selectivity of the compounds was proposed based on the structural models generated by homology modeling and molecular docking. Our findings provide insights into the mechanism of insect resistance and guidance for developing effective RyR agonists that can selectively target resistant pests