Both the energy efficiency and correlation characteristics are important in airborne sonar systems to realize multichannel ultrasonic transducers working together. High energy efficiency can increase echo energy and measurement range, and sharp autocorrelation and flat cross correlation can help eliminate cross-talk among multichannel transducers. This paper addresses energy efficiency optimization under the premise that cross-talk between different sonar transducers can be avoided. The nondominated sorting genetic algorithm-II is applied to optimize both the spectrum and correlation characteristics of the excitation sequence. The central idea of the spectrum optimization is to distribute most of the energy of the excitation sequence within the frequency band of the sonar transducer; thus, less energy is filtered out by the transducers. Real experiments show that a sonar system consisting of eight-channel Polaroid 600 series electrostatic transducers excited with 2 ms optimized pulse-position-modulation sequences can work together without cross-talk and can measure distances up to 650 cm with maximal 1% relative error.

Ultrasonic crosstalk is one of the main causes of false distance measurements that reduce the work efficiency of sonar sensors in mobile robots. To enhance the real-time performance of sonar systems, short digital pulse-position modulation (PPM) sequences are used to trigger ultrasonic transducers. Due to their properties of sharp autocorrelation and flat cross correlation, chaotic and pseudorandom number series are used to modulate pulse positions. A genetic algorithm is adopted to optimize the range of duration between pulses. Real experiments using Polaroid 600 series instrument-grade electrostatic transducers validate the suitability of the proposed method.

A polynomial y=y0+α1x+α2x2+α3x3+...can be used for matching a non-linear functiony=f(x). The key is how to obtain its coefficients. In this paper, a normalized method is presented for matching any non-linear functions. There is a corresponding normalized polynomial y=y0+an+bn2+cn3+..., n=x/x1, n1=x1/x1=1, nmax=xmax/x1.ni is an integral, x1 is a ratio ruler of the abscissa x, then (α1α2α3...)T=(ax1−1bx1−2cx1−3...)T. a, b, c... can be obtained from the multiplication of normalized matrices. Two examples taken from transducers are presented. When the polynomial matches are used for two different sections in x abscissa, the conditions for continuation of the calculated values for these functions and their derivatives are discussed and the development of sine function in different sections of abscissa is presented as an example.

In this paper, an AEKF algorithm is used to localize a mobile robot equipped with eight Polaroid sonars in an indoor structured environment. The system state equation and sonar measurement models used for locating the mobile robot are set up. The localization process based on the AEKF algorithm is given. Four criteria used to judge the validity of predictive measurements of sonars are presented, which can increase the probability of the matching between predictive measurements and actual measurements. Experiments show that the localization precision based on our methods is greater than that using the conventional EKF algorithm.

In this paper, the wall-following problem of an AGV is characterized by keeping a constant distance from a wall. Four Polaroid ultrasonic range sensors (sonar) are used to obtain information of the distance between the AGV and the wall. Four sonars are divided into two groups being placed at the front and back end on the right side of the AGV, respectively. A digital filter is used to get rid of disturbance and wrong readings. The former results mainly from the door and the latter from the specular reflection and accidental scattering. To obtain good dynamic and steady performances, a new compound fuzzy-I control algorithm is presented. Our methods have been verified by following a 15-meter-long wall with one door by using our TIT-1 AGV (TIT: Tianjin Institute of Technology). The results show a good performance with an error of ±2 cm from the desired distance to the wall.