RESUMO
With the wide application of unmanned ground vehicles (UGV) in a complex environment, the research on the obstacle avoidance system has gradually become an important research part in the field of the UGV system. Aiming at the complex working environment, a sensor detection system mounted on UGV is designed and the kinematic estimation model of UGV is studied. In order to meet the obstacle avoidance requirements of UGVs in a complex environment, a fuzzy neural network obstacle avoidance algorithm based on multi-sensor information fusion is designed in this paper. MATLAB is used to simulate the obstacle avoidance algorithm. By comparing and analyzing the simulation path of UGV's obstacle avoidance motion under the navigation control of fuzzy controller and fuzzy neural network algorithm, the superiority of the proposed fuzzy neural network algorithm was verified. Finally, the superiority and reliability of the obstacle avoidance algorithm are verified through the obstacle avoidance experiment on the UGV experimental platform.
RESUMO
The biological effect of Se and Cu²(+) on Escherichia coli (E. coli) growth was studied by using a 3114/3236 TAM Air Isothermal Calorimeter, ampoule method, at 37°C. From the thermogenesis curves, the thermokinetic equations were established under different conditions. The kinetics showed that a low concentration of Se (1-10 µg/mL) promoted the growth of E. coli, and a high concentration of Se (>10 µg/mL) inhibited the growth, but the Cu²(+) was always inhibiting the growth of E. coli. Moreover, there was an antagonistic or positive synergistic effect of Se and Cu²(+) on E. coli in the different culture medium when Se was 1-10 µg/ml and Cu²(+) was 1-20 µg/ml. There was a negative synergistic effect of Se and Cu²(+) on E. coli when Se was higher than 10 µg/ml and Cu²(+) was higher than 20 µg/ml. The antagonistic or synergistic effect between Se and Cu²(+) on E. coli was related to the formation of Cu-Se complexes under the different experimental conditions chosen.