【摘要】：隨著經(jīng)濟(jì)的發(fā)展,城市人口的逐漸增多,城市中心醫(yī)院的護(hù)士在臨床護(hù)理工作也越來越繁重,護(hù)患矛盾時(shí)有發(fā)生。為了將護(hù)士從平時(shí)繁瑣的工作中解放出來,更好地為患者服務(wù),將護(hù)士助手移動(dòng)機(jī)器人引入醫(yī)院中,代替護(hù)士完成輸送問詢等工作。醫(yī)院環(huán)境中人流量大,要求護(hù)士助手移動(dòng)機(jī)器人在其中安全可靠地完成物品運(yùn)輸工作極負(fù)挑戰(zhàn)性。為此,本文針對(duì)室內(nèi)動(dòng)態(tài)環(huán)境下的移動(dòng)機(jī)器人行人目標(biāo)檢測(cè)跟蹤與避障策略問題展開研究,基于ROS系統(tǒng)進(jìn)行高效地開發(fā)與驗(yàn)證。提出的避障策略流程為:基于人體識(shí)別算法對(duì)機(jī)器人前方正在行進(jìn)的人體進(jìn)行檢測(cè)確定其所在的圖像區(qū)域,對(duì)確定的圖像區(qū)域進(jìn)行目標(biāo)跟蹤,通過這種方法我們可以獲得前后兩個(gè)時(shí)間段的人體相對(duì)于機(jī)器人的位置,進(jìn)而確定人體目標(biāo)的運(yùn)動(dòng)狀態(tài),經(jīng)過IMM濾波后獲取人體的準(zhǔn)確運(yùn)動(dòng)狀態(tài),最后基于改進(jìn)的人工勢(shì)場(chǎng)法進(jìn)行局部路徑規(guī)劃完成避障行為。具體工作如下:與單獨(dú)使用RGB圖像來進(jìn)行檢測(cè)不同,基于信息更加豐富的RGB-D圖像進(jìn)行行人檢測(cè)。提出的算法流程為:首先基于密度統(tǒng)計(jì)信息對(duì)點(diǎn)云圖像進(jìn)行魯棒高效地聚類分析獲得各點(diǎn)的集合,然后根據(jù)高度標(biāo)準(zhǔn)劃分出人體存在的可能區(qū)域,最后基于SVM-HOG框架進(jìn)行人體檢測(cè)。實(shí)驗(yàn)表明,采用上述方案在降低誤檢率的同時(shí)也可以降低計(jì)算機(jī)的運(yùn)算量從而達(dá)到實(shí)時(shí)性的要求。采用Online Boosting算法將目標(biāo)跟蹤問題轉(zhuǎn)化為一種二值分類問題進(jìn)行高效處理。該算法通過對(duì)人體所在區(qū)域進(jìn)行Haar-like特征提取,實(shí)時(shí)訓(xùn)練更新所需要的分類器。當(dāng)下一次檢測(cè)樣本到來時(shí),使用訓(xùn)練好的分類器對(duì)該樣本進(jìn)行判別并重復(fù)上一步驟實(shí)時(shí)更新,以此往復(fù)。另外還引入了貪婪數(shù)據(jù)關(guān)聯(lián)邏輯方法以解決當(dāng)目標(biāo)丟失后如何重新進(jìn)行跟蹤問題�；诮换ザ嗄Ｐ�(IMM)濾波算法準(zhǔn)確估計(jì)障礙物的運(yùn)動(dòng)狀態(tài),采用改進(jìn)的人工勢(shì)場(chǎng)法進(jìn)行局部路徑動(dòng)態(tài)規(guī)劃。針對(duì)卡爾曼濾波器在實(shí)驗(yàn)機(jī)器人上估計(jì)障礙物位置精度差的問題,提出基于交互多模型的濾波算法。對(duì)傳統(tǒng)的人工勢(shì)場(chǎng)法進(jìn)行改進(jìn),將障礙物的速度納入考慮范圍內(nèi),相較于傳統(tǒng)方法,本文提出的算法使機(jī)器人在動(dòng)態(tài)環(huán)境下能更好的完成避障,減少與障礙物的碰撞率。綜合實(shí)驗(yàn)表明,基于上述策略設(shè)計(jì)的護(hù)士助手機(jī)器人在傳感器檢測(cè)范圍內(nèi),能夠處理稀疏人群正常步行速度條件下的室內(nèi)動(dòng)態(tài)避障。與傳統(tǒng)避障策略相比較,在機(jī)器人在規(guī)劃效率有所提升,對(duì)行人的處理上更加智能,更加安全。
[Abstract]:With the development of economy and the increase of urban population, the nurses in urban central hospital have more and more heavy clinical nursing work, and the contradiction between nurse and patient occurs from time to time. In order to liberate nurses from the usual tedious work and better serve the patients, the nurse assistant mobile robot was introduced into the hospital, instead of nurses to complete the transportation of inquiries and other work. In the hospital environment, the mobile robot is required to carry out the transportation of goods safely and reliably. Therefore, this paper studies the problem of pedestrian target detection, tracking and obstacle avoidance strategy of mobile robot in indoor dynamic environment, and develops and verifies it efficiently based on ROS system. The flow chart of obstacle avoidance strategy is as follows: based on the human body recognition algorithm, the moving human body in front of the robot is detected to determine the image region, and the target tracking is carried out to the determined image region. Through this method, we can obtain the position of the human body relative to the robot in the two time periods before and after, and then determine the moving state of the human body target, and obtain the accurate motion state of the human body after IMM filtering. Finally, local path planning based on the improved artificial potential field method is used to accomplish obstacle avoidance. The main work is as follows: different from using RGB images alone, pedestrian detection is based on more informative RGB-D images. The proposed algorithm flow is as follows: firstly, based on the density statistics, the point cloud images are clustered to obtain the set of points, and then the possible regions of the human body are divided according to the height criteria. Finally, human body detection is carried out based on SVM-HOG framework. Experimental results show that the proposed scheme can not only reduce the false detection rate but also reduce the computational complexity of the computer so as to meet the real-time requirements. Online Boosting algorithm is used to transform the target tracking problem into a binary classification problem. By extracting the Haar-like features of the human body, the algorithm can train the classifier needed for updating in real time. When the next detection sample comes, the trained classifier is used to distinguish the sample and repeat the previous step to update the sample in real time. In addition, the greedy data association logic method is introduced to solve the problem of how to track the target again when the target is lost. Based on the interactive multi-model (IMM) filtering algorithm, the moving state of obstacles is estimated accurately, and the improved artificial potential field method is used for local path dynamic planning. Aiming at the problem of poor accuracy of Kalman filter in estimating obstacle position on experimental robot, a filtering algorithm based on interactive multi-model is proposed. The traditional artificial potential field method is improved and the velocity of obstacles is taken into account. Compared with the traditional method, the algorithm proposed in this paper can make the robot avoid obstacles better and reduce the collision rate with obstacles in dynamic environment. The comprehensive experiments show that the nurse assistant robot designed based on the above strategy can deal with the indoor dynamic obstacle avoidance under the condition of the normal walking speed of the sparse crowd within the detection range of the sensor. Compared with the traditional obstacle avoidance strategy, the robot is more intelligent and safer in planning efficiency and pedestrian handling.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP242

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 單文松;周風(fēng)余;徐法格;朱瑞;;一種基于紅外測(cè)距原理的清潔機(jī)器人定位新方法[J];可編程控制器與工廠自動(dòng)化;2015年08期

2 厲廣偉;夏英杰;王寧;李金屏;;機(jī)器人視覺導(dǎo)航路徑節(jié)點(diǎn)的編碼與識(shí)別[J];濟(jì)南大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年04期

3 崔明月;劉旭焱;蔣華龍;劉紅釗;;磁導(dǎo)航移動(dòng)機(jī)器人里程檢測(cè)系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[J];實(shí)驗(yàn)室研究與探索;2014年06期

4 肖鵬;欒貽青;郭銳;王明瑞;孫勇;;變電站智能巡檢機(jī)器人激光導(dǎo)航系統(tǒng)研究[J];自動(dòng)化與儀表;2012年05期

5 弋英民;劉丁;;基于單目視覺的輪式機(jī)器人同步定位與地圖構(gòu)建[J];儀器儀表學(xué)報(bào);2010年01期

6 趙杰文;劉少鵬;鄒小波;石吉勇;殷小平;;基于支持向量機(jī)的缺陷紅棗機(jī)器視覺識(shí)別[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2008年03期

7 譚冠政;賀歡;Aaron Sloman;;Global optimal path planning for mobile robot based onimproved Dijkstra algorithm and ant system algorithm[J];Journal of Central South University of Technology(English Edition);2006年01期

8 孟偉,黃慶成,韓學(xué)東,洪炳昒;一種動(dòng)態(tài)未知環(huán)境中自主機(jī)器人的導(dǎo)航方法[J];計(jì)算機(jī)研究與發(fā)展;2005年09期

9 王淑一,程楊,楊滌,崔祜濤;UKF方法及其在方位跟蹤問題中的應(yīng)用[J];飛行力學(xué);2003年02期

10 俞竹青,那須康雄;超聲波網(wǎng)絡(luò)導(dǎo)航中移動(dòng)機(jī)器人的位置計(jì)算[J];機(jī)器人技術(shù)與應(yīng)用;2002年03期

相關(guān)碩士學(xué)位論文 前1條

1 魏U，

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