【摘要】：由于除冰機(jī)器人工作在覆冰的電力線上,障礙物的識(shí)別存在著各類障礙物區(qū)分較難,準(zhǔn)確率較低等不足。為提高機(jī)器人自主識(shí)別能力,設(shè)計(jì)一種自適應(yīng)閥值的小波變換邊緣提取算法來(lái)提取出障礙物的圖像邊緣,并針對(duì)電力線障礙物結(jié)構(gòu)特點(diǎn),在障礙物邊緣提取過(guò)程中設(shè)計(jì)一種基于電力線位置約束的有效剔除部分干擾背景的方法;引入小波矩,通過(guò)提取邊緣圖像的小波矩作為障礙物的特征匹配數(shù)據(jù);根據(jù)神經(jīng)網(wǎng)絡(luò)和粒子群算法的原理,設(shè)計(jì)一種粒子群優(yōu)化的小波神經(jīng)網(wǎng)絡(luò)進(jìn)行障礙物的識(shí)別分類,該方法用粒子群算法取代傳統(tǒng)的梯度下降法,并改進(jìn)慣性權(quán)重因子,優(yōu)化小波網(wǎng)絡(luò)的各個(gè)參數(shù)。試驗(yàn)結(jié)果表明所提出的方法對(duì)電力線上防震錘、懸垂線夾和耐張線夾等障礙物能夠有效地識(shí)別,并具有比普通識(shí)別方法更高的識(shí)別精度。
[Abstract]:Because the deicing robot works on the power line covered with ice, there are some shortcomings in the recognition of obstacles, such as difficult to distinguish all kinds of obstacles, low accuracy and so on. In order to improve the autonomous recognition ability of the robot, an adaptive threshold wavelet transform edge extraction algorithm is designed to extract the image edge of the obstacle, and according to the structural characteristics of the obstacle, an effective method based on the power line position constraint is designed to eliminate the interference background, and the wavelet moment is introduced to extract the wavelet moment of the edge image as the feature matching data of the obstacle. According to the principle of neural network and particle swarm optimization algorithm, a wavelet neural network optimized by particle swarm optimization is designed to identify and classify obstacles. In this method, particle swarm optimization algorithm is used to replace the traditional gradient descent method, and the inertia weight factor is improved to optimize the parameters of wavelet network. The experimental results show that the proposed method can effectively identify obstacles such as shock hammer, drape clamp and tension clamp on the power line, and has higher recognition accuracy than the common recognition method.
【作者單位】： 湖南大學(xué)電氣與信息工程學(xué)院;邵陽(yáng)學(xué)院多電源地區(qū)電網(wǎng)運(yùn)行與控制湖南省重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家科技支撐計(jì)劃(2015BAF11B01) 湖南省科技計(jì)劃(2016TP1023) 湖南省教育廳科研(14C1015)資助項(xiàng)目
【分類號(hào)】：TP183;TP242

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