本文選題：農(nóng)業(yè)機械 + 導(dǎo)航　； 參考：《農(nóng)業(yè)工程學(xué)報》2017年08期

【摘要】：農(nóng)業(yè)機械的自動化和智能化包含內(nèi)容廣泛,有農(nóng)機定位與導(dǎo)航,動態(tài)路徑規(guī)劃,機器視覺和遠程監(jiān)控等,牽涉到大量的工程技術(shù)學(xué)科,包括導(dǎo)航、圖像、模型與策略、執(zhí)行器以及數(shù)據(jù)鏈等。農(nóng)機定位與導(dǎo)航一般采用基于農(nóng)機運動學(xué)模型結(jié)合GPS(global positioning system)/IMU(inertial measurement unit)組合導(dǎo)航信息,在導(dǎo)航路徑規(guī)劃算法指引下實現(xiàn)農(nóng)機軌跡跟蹤的方法。建立的農(nóng)機運動學(xué)模型精度,GPS數(shù)據(jù)的連續(xù)性以及慣導(dǎo)器件誤差系數(shù)漂移等因素都會影響該方法的有效性。路徑跟蹤通常采用各種現(xiàn)代控制理論與方法,而面對復(fù)雜的田間作業(yè)環(huán)境變化,農(nóng)機的自主避障以及動態(tài)路徑規(guī)劃能力也會影響軌跡跟蹤精度。機器視覺的穩(wěn)定性和目標特征信息分離度影響著農(nóng)機環(huán)境感知能力,目前目標識別主要采用hough變換,hough變換的全局檢測特性決定了該算法運算量較大,需要探究改進特征提取算法。遠程監(jiān)控農(nóng)機作業(yè)是智能農(nóng)機發(fā)展的一個方向,構(gòu)建無線導(dǎo)航,控制和視頻數(shù)據(jù)傳輸網(wǎng)絡(luò)有助于提高農(nóng)機的智能化水平,可以采用分布式哈希表(distributed hash table)來研究網(wǎng)絡(luò)覆蓋和互聯(lián)技術(shù)。該文融合多個學(xué)科,從高精度定位與導(dǎo)航技術(shù)、復(fù)雜環(huán)境及工況下農(nóng)機運動精確自主控制技術(shù)、穩(wěn)定清晰的機器視覺感知技術(shù)和基于4G網(wǎng)絡(luò)和新一代物聯(lián)網(wǎng)的高覆蓋數(shù)據(jù)傳輸技術(shù)幾個方面,論述了智能農(nóng)機在光機電液多個學(xué)科領(lǐng)域內(nèi)的研究現(xiàn)狀,并指出采用北斗地基增強網(wǎng)絡(luò)和網(wǎng)絡(luò)RTK(real-time kinematic)技術(shù)、慣導(dǎo)定位誤差精確建模與補償、環(huán)境感知與自主避障、立體結(jié)構(gòu)自組網(wǎng)技術(shù)以及多機協(xié)作是現(xiàn)代農(nóng)業(yè)機械的發(fā)展方向。以期為現(xiàn)代化智能農(nóng)業(yè)機械的設(shè)計提供參考。
[Abstract]:The automation and intelligence of agricultural machinery include a wide range of contents, including agricultural machinery positioning and navigation, dynamic path planning, machine vision and remote monitoring, involving a large number of engineering and technical disciplines, including navigation, images, models and strategies. Actuators and data links. Based on the kinematics model of agricultural machinery and the integrated navigation information of (global positioning system) / IMU (inertial measurement unit), agricultural machinery location and navigation are usually implemented under the guidance of navigation path planning algorithm. The precision of the established kinematics model and the continuity of GPS data, as well as the error coefficient drift of inertial navigation devices, will affect the effectiveness of the method. Path tracking usually adopts various modern control theories and methods, but in the face of complex field operation environment changes, the ability of autonomous obstacle avoidance and dynamic path planning of agricultural machinery will also affect the track tracking accuracy. The stability of machine vision and the separation degree of target feature information affect the environmental perception ability of agricultural machinery. At present, the global detection characteristics of hough transform and Hough transform are the main methods for target recognition. The improved feature extraction algorithm needs to be explored. Remote monitoring of agricultural machinery operation is a direction of intelligent agricultural machinery development. Building wireless navigation, control and video data transmission network will help to improve the intelligent level of agricultural machinery. Distributed hash table (distributed hash table) can be used to study network coverage and interconnection technology. This paper combines many disciplines, including high precision positioning and navigation technology, precise autonomous control technology of agricultural machinery movement under complex environment and working conditions. The stable and clear machine vision perception technology and the high coverage data transmission technology based on 4G network and the new generation of Internet of things are discussed in this paper. It is pointed out that the technology of beidou foundation enhancement network and network RTK (real-time kinematic), accurate modeling and compensation of inertial navigation positioning error, environment perception and autonomous obstacle avoidance, stereo structure ad hoc network technology and multi-machine cooperation are the development direction of modern agricultural machinery. In order to provide reference for the design of modern intelligent agricultural machinery.
【作者單位】： 西北工業(yè)大學(xué)自動化學(xué)院;飛行器控制一體化技術(shù)重點實驗室中航工業(yè)自控所;河南科技大學(xué)機電學(xué)院;
【基金】：航空科學(xué)基金(MIMU/單星偽距偽距率組合導(dǎo)航技術(shù)(20165853041))
【分類號】：S126;S220
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本文編號：2111822