The Technology Of Multisensor Data Fusion And Its Application For Mobile Robot

The technology of multisensor data fusion and autonomous mobile robot both are pop topics in the world at the present. But, in domestic relevant science fields, the technology is still in initial stages. Fortunately, the technology of multisensor data fusion was brought forth as an important research field in the Project 973. There is a broad application prospect about the technique in various fields, such as military decision-making, industry control, special robot, ocean scouting, and integrative navigation, etc. In this article, the author has performed fruitfully in theoretic aspect and practice aspect, and has gotten some significant results as well. There are four primary aspects about this article as below.Firstly, a worldwide survey of multisensor data fusion technology is presented, and the data fusion systems' function and framework are described clearly. Some successful solutions about this technology used in robotic field are enumerated in this article. Meanwhile, several common algorithms about data fusion are introduced respectively, such as Bayes-method, Dempster-Shafer evidential reasoning, fuzzy set theory, neural networks techniques, and so on.Secondly, the theory of strong tracking filter is adopted in multisensor data fusion system. In the article, most key deducing steps and related theorems are recounted and primary parameters formulas are recounted as well. Furthermore, the author implements the algorithm by coding m-file in Matlab software environment. And the author uses this algorithm to calculate the states and parameters in a typical nonlinear data fusion system. According to the computer simulation, we can get that this algorithm is prior to the extended Kalman filter theory. It enriches the data fusion theory in some degree.Thirdly, an electronic compass module is installed in the autonomous mobile robot system. The author combines the data from electronic compass, coding sensors, infrasonic sensors and infrared sensors to make the robot adjust orientation accurately and navigate by itself. At the same time, the author draws such a conclusion: navigation only depending electronic compass is not enough and inaccurate. The difficulties during programming include understanding thewhole software framework and interface functions, encapsulating electronic compass class, serial port communication, multithread communication & synchronization, etc.Fourthly, the author designs a system based on MCU AT89C52, which can detect obstacles with infrared sensors and measure the environmental temperature with temperature sensor. The global procedure about the system's hardware design and software implementation is described at length. In aspect of the software, the programs are coded in C-language in Keil C51 software environment. A program which receives data frame on PC is written in visual C++ language, and it completes the communication between PC and MCU.