【摘要】：自動(dòng)檢測(cè)及復(fù)雜系統(tǒng)自動(dòng)控制總免不了多傳感器數(shù)據(jù)采集與信息的綜合運(yùn)用。因此,作為一種重要的信息處理方法,異質(zhì)傳感器的數(shù)據(jù)融合成為當(dāng)前的一個(gè)研究熱點(diǎn)。本文分別使用傳統(tǒng)數(shù)學(xué)算法和計(jì)算智能算法對(duì)超聲和紅外傳感器的數(shù)據(jù)融合問(wèn)題進(jìn)行了研究。在編程仿真的基礎(chǔ)上,構(gòu)建了一個(gè)模糊神經(jīng)網(wǎng)絡(luò),研究了多傳感器數(shù)據(jù)融合在吸塵機(jī)器人自主避障中的應(yīng)用。本文主要做了以下幾方面的工作:(1)使用傳統(tǒng)數(shù)學(xué)算法對(duì)異質(zhì)傳感器數(shù)據(jù)融合進(jìn)行了研究。本文提出了一種自適應(yīng)加權(quán)融合算法,對(duì)超聲和紅外傳感器測(cè)距系統(tǒng)的距離信息進(jìn)行融合。使用MATLAB對(duì)提出的算法進(jìn)行了編程仿真,仿真結(jié)果表明:本文提出的自適應(yīng)加權(quán)融合算法融合結(jié)果穩(wěn)定,算法收斂速度快,能根據(jù)傳感器的方差大小對(duì)各傳感器的權(quán)值進(jìn)行自適應(yīng)分配。(2)使用計(jì)算智能算法、構(gòu)建BP神經(jīng)網(wǎng)絡(luò)對(duì)異質(zhì)傳感器的數(shù)據(jù)融合進(jìn)行了研究。本文針對(duì)超聲和紅外傳感器數(shù)據(jù)融合,構(gòu)造了一個(gè)三層BP神經(jīng)網(wǎng)絡(luò),使用MATLAB對(duì)設(shè)計(jì)的BP神經(jīng)網(wǎng)絡(luò)進(jìn)行了編程仿真。為解決仿真結(jié)果中出現(xiàn)的標(biāo)準(zhǔn)BP神經(jīng)網(wǎng)絡(luò)收斂速度慢,融合結(jié)果不夠理想的問(wèn)題,提出了增加附加動(dòng)量項(xiàng)的訓(xùn)練算法,并對(duì)其改進(jìn)效果進(jìn)行了對(duì)比分析,結(jié)果顯示:較之于普通網(wǎng)絡(luò),附加動(dòng)量項(xiàng)的BP神經(jīng)網(wǎng)絡(luò)收斂速度更快,融合結(jié)果更加精確。(3)對(duì)數(shù)據(jù)融合技術(shù)在吸塵機(jī)器人避障中的應(yīng)用進(jìn)行研究。針對(duì)具有五個(gè)超聲測(cè)距傳感器和一個(gè)角度傳感器的掃地機(jī)器人原型系統(tǒng),構(gòu)建了一個(gè)五層模糊神經(jīng)網(wǎng)絡(luò)控制系統(tǒng)。將五個(gè)超聲傳感器測(cè)得的距離和一個(gè)角度傳感器測(cè)得的目標(biāo)方位角作為系統(tǒng)的輸入變量,輸入模糊神經(jīng)網(wǎng)絡(luò)系統(tǒng),經(jīng)過(guò)系統(tǒng)的推理計(jì)算得到網(wǎng)絡(luò)輸出。使用MATLAB對(duì)設(shè)計(jì)的模糊神經(jīng)網(wǎng)絡(luò)系統(tǒng)進(jìn)行了仿真,仿真結(jié)果表明:當(dāng)環(huán)境無(wú)障礙物時(shí),掃地機(jī)器人可以從設(shè)置的起點(diǎn)沿直線運(yùn)動(dòng)到目標(biāo)點(diǎn);當(dāng)環(huán)境中存在障礙物時(shí),掃地機(jī)器人可以有效避開(kāi)障礙物到達(dá)目標(biāo)點(diǎn)。
[Abstract]:Automatic detection and automatic control of complex systems can not avoid multi-sensor data acquisition and comprehensive application of information. Therefore, as an important information processing method, data fusion of heterogeneous sensors has become a hot topic. In this paper, traditional mathematical algorithms and computational intelligence algorithms are used to study the data fusion of ultrasonic and infrared sensors. On the basis of programming and simulation, a fuzzy neural network is constructed, and the application of multi-sensor data fusion in autonomous obstacle avoidance of dust cleaning robot is studied. The main work of this paper is as follows: (1) the data fusion of heterogeneous sensors is studied by using traditional mathematical algorithms. In this paper, an adaptive weighted fusion algorithm is proposed to fuse the range information of ultrasonic and infrared sensor ranging systems. The proposed algorithm is programmed and simulated with MATLAB. The simulation results show that the fusion result of the proposed adaptive weighted fusion algorithm is stable and the convergence speed of the algorithm is fast. The weights of each sensor can be allocated adaptively according to the variance of the sensor. (2) A BP neural network is constructed to study the data fusion of heterogeneous sensors. In this paper, a three-layer BP neural network is constructed for the data fusion of ultrasonic and infrared sensors. The designed BP neural network is programmed and simulated by MATLAB. In order to solve the problem of slow convergence speed and unsatisfactory fusion result of standard BP neural network in the simulation results, a training algorithm to increase the additional momentum term is proposed, and the improved results are compared and analyzed. The results show that the convergence speed of BP neural network with momentum term is faster and the fusion result is more accurate than that of ordinary network. (3) the application of data fusion technology in obstacle avoidance of dust cleaning robot is studied. A five-layer fuzzy neural network control system for a floor sweeping robot with five ultrasonic ranging sensors and an angle sensor is constructed. The distance measured by five ultrasonic sensors and the target azimuth angle measured by one angle sensor are taken as the input variables of the system and the fuzzy neural network system is inputted and the network output is obtained by the inference calculation of the system. The design of fuzzy neural network system is simulated by MATLAB. The simulation results show that when there are no obstacles in the environment, the floor sweeping robot can move along a straight line from the setting point to the target point, and when there are obstacles in the environment, The floor sweeping robot can effectively avoid obstacles to reach the target point.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

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