【摘要】：隨著傳感器技術和計算機科學的迅速發(fā)展,人們廣泛利用機器人在危險的環(huán)境或人類難以到達的地方執(zhí)行任務。移動機器人本身具有傳感器和處理器,可以自主進行探測、判斷、決策并在到達目標地點后執(zhí)行預先設定的任務。但由于未知環(huán)境的不確定性,單一的傳感器難以滿足復雜任務的要求,多傳感器數(shù)據(jù)融合技術能夠綜合各個傳感器的信息,進而獲得更全面、更準確的決策信息。本文利用多傳感器數(shù)據(jù)融合技術,設計并開發(fā)了一套基于Khepera IV嵌入式機器人在未知環(huán)境下的執(zhí)行目標標定任務系統(tǒng)。機器人在未知環(huán)境中移動,避開障礙物,并運用自帶的攝像頭和超聲波傳感器尋找目標物。首先對不同角度拍攝的目標物的圖片進行預處理,提取出目標物的特征,存入上位機。機器人在運動的過程中,運用超聲波傳感器檢測周圍的物體,并拍照將照片上傳到上位機與目標物的特征比對,判斷是否是目標物。如果找到目標物,則標定出目標物的位置。研究內(nèi)容包括以下幾個方面:1.利用zabbix開源軟件,在windows上位機的虛擬機中搭建機器人監(jiān)控系統(tǒng)。對機器的實時狀態(tài)進行監(jiān)控,包括機器人電池、CPU占用率和機器人的運動軌跡等,存放在MySQL數(shù)據(jù)庫中,便于在機器人的運動控制和數(shù)據(jù)融合的過程中的使用。同時,zabbix軟件中基于web的數(shù)據(jù)交互頁面可以直觀的顯示被監(jiān)控數(shù)據(jù)的變化情況。2.建立機器人的運動模型,并結合Khepera IV機器人的硬件配置,分析速度控制、位置控制、方向控制的方法,提出了在機器人在運動過程中的模糊避障策略,并對避障策略進行了仿真,仿真結果表明所提出的模糊避障策略是有效的。3.提出了基于機器學習和圖像處理的目標物識別方法。首先對目標物進行預處理,提取目標物的特征。在識別之前,運用機器人的攝像頭在不同距離、不同視角拍攝目標物的圖片,以200張含有目標物的圖像作為正樣本,300張不含有目標物的圖像作為負樣本,使用HOG特征提取算法提取特征,對正負樣本進行標記,利用線性SVM分類器進行訓練,得到一個二值分類器。然后在機器人搜索目標物的過程中,拍攝周圍物體,傳回上位機進行處理,通過圖像增強、二值化、邊緣檢測、霍夫變換等預處理,在圖片中劃分出可能存在目標物的區(qū)域,將此區(qū)域用SVM分類器進行識別,判斷是否為目標物。并估算目標物的實際大小,估計目標物的位置。4.進行機器人目標標定的實驗,進行機器人監(jiān)控系統(tǒng)的搭建、實現(xiàn)數(shù)據(jù)傳輸和實時圖像處理。實驗表明,機器人可以實現(xiàn)在未知環(huán)境中(障礙物比較規(guī)則)的目標識別任務,說明了本文所設計系統(tǒng)的有效性。
[Abstract]:With the rapid development of sensor technology and computer science, robots are widely used to perform tasks in dangerous environments or places that are difficult to reach by human beings. The mobile robot itself has sensors and processors, which can detect, judge, make decisions and perform predefined tasks after reaching the target location. However, because of the uncertainty of unknown environment, it is difficult for a single sensor to meet the requirements of complex tasks. The multi-sensor data fusion technology can synthesize the information of each sensor and obtain more comprehensive and accurate decision information. In this paper, we design and develop a target calibration task system based on Khepera IV embedded robot in unknown environment by using multi-sensor data fusion technology. The robot moves in unknown environment, avoids obstacles, and uses its own camera and ultrasonic sensor to find objects. Firstly, the images of the objects taken from different angles are preprocessed to extract the features of the objects and stored in the upper computer. In the process of motion, the robot uses ultrasonic sensor to detect the objects around it, and takes pictures and compares the features of the object with the host computer to judge whether the object is the object or not. If the object is found, the location of the object is calibrated. The research includes the following aspects: 1. Using zabbix open source software, the robot monitoring system is built in the virtual machine of windows host computer. The real-time state of the machine is monitored, including the battery of the robot, the CPU occupancy rate and the trajectory of the robot. It is stored in the MySQL database, which is convenient for use in the process of robot motion control and data fusion. At the same time, the zabbix software based on web data exchange page can visually display the monitored data changes. 2. The motion model of the robot is established, and the method of speed control, position control and direction control is analyzed based on the hardware configuration of the Khepera IV robot, and the fuzzy obstacle avoidance strategy is put forward in the course of the robot movement. The simulation results show that the proposed fuzzy obstacle avoidance strategy is effective. A method of object recognition based on machine learning and image processing is proposed. First, the target is pretreated to extract the characteristics of the target. Prior to recognition, the robot's camera was used to take pictures of the object from different angles of view at different distances. 200 images containing the object were used as positive samples, and 300 images without the object were taken as negative samples. HOG feature extraction algorithm is used to extract features, positive and negative samples are marked, and linear SVM classifiers are used to train them to obtain a binary classifier. Then, in the process of robot searching for objects, the objects around them are photographed and sent back to the upper computer for processing. Through the preprocessing of image enhancement, binarization, edge detection and Hoff transform, the region where the object may exist is divided in the picture. This area is identified by SVM classifier to determine whether it is the target. And estimate the actual size of the object, estimate the location of the target. 4. The experiment of robot target calibration is carried out, and the robot monitoring system is built to realize data transmission and real time image processing. Experiments show that the robot can realize the target recognition task in unknown environment (obstacle comparison rules), and the effectiveness of the system designed in this paper is illustrated.
【學位授予單位】：東華大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP242

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