A circularly polarized (CP) Microstrip Yagi array antenna (MSYA) is designed in order to achieve high front-to-back ratioR(F/B)and high gain over wide range in the forward radiation space. A Wilkinson power divider owning two output ways with the same magnitude and different phase is used to feed the antenna. Parametric studies are carried out to investigate the effects of some key geometrical sizes on the antenna’s performance. A prototype of the antenna is fabricated, and good agreement between the measured results and the numerical simulations is observed. The overlap bandwidth of VSWR ≤ 1.5 and AR ≤ 3 dB is about 11%. The steering angle (α) between the peak gain direction and the broadside can achieve 35°,R(F/B)reaches 19 dB, and the gain at the front point (G0) is only 4.3 dB lower than the maximum gain (Gm). The antenna has a wide beamwidth CP radiation pattern over wide spatial range including 0° ≤θ≤ 90° in vertical plane and −35° ≤φ≤ 55° in horizontal plane.

Knowledge of stringent value of the robot end-effector position is necessary in several industrial applications such as macroscopic structure measurement and assemblage. However, industrial robots cannot meet this challenge because of numerous errors. This paper presents a new method using quantum behaved particle swarm optimization (QPSO) to calibrate the robot geometrical parameters to improve the positioning accuracy of serial robots. Firstly, the errors of the axes are analysed, then the QPSO is used to calculate the errors. Experimental results show that the proposed method can effectively identify the robot geometrical parameters.

The architecture of coordination is the basis of the coordination of multiple intelligent vehicles, as well as the framework for integrated control and decision-making. By analyzing the multiple intelligent vehicle coordination problems, the parallel coordination architecture based on the multiagent systems coordination model has been designed by considering the composition and operating characteristics of the local intelligent transportation system. This coordination architecture integrates the behavior coordination and the task coordination, and can meet the requirements of the multiple intelligent vehicle coordination in local transportation system. Refer to the above-mentioned coordination architecture, a multiple intelligent vehicle coordination system for the local intelligent transportation has been realized.