服務機器人二維激光里程計構建及自主導航
發(fā)布時間：2019-04-20 11:56
【摘要】：近年來,服務機器人正逐漸走進千家萬戶,也不斷改變著人們的日常生活方式。自主導航是服務機器人完成其它復雜任務的前提。本文研究了服務機器人在未知環(huán)境中基于二維激光測距的自主導航問題,其重點包括位姿跟蹤、自主定位、柵格地圖構建和路徑規(guī)劃。為有效實現(xiàn)移動機器人的位姿跟蹤,對Mb-ICP、PSM口PL-ICP這三種典型的掃描匹配算法進行對比分析,并確定采用PL-ICP算法作為最終里程計構建算法。為減少原始雜亂數(shù)據(jù)對算法的影響,分別采用中值濾波和分割處理對激光數(shù)據(jù)進行預處理,剔除雜亂點和無效點,從而提高位姿跟蹤算法的魯棒性。此外利用關鍵幀技術解決由激光數(shù)據(jù)動態(tài)誤差引起的位姿漂移問題。采用Rao-Blackwellized粒子濾波算法將機器人同時定位與地圖構建(SLAM)問題分解為機器人全局位姿估計和柵格地圖構建兩個部分。為了改善粒子濾波算法的建議分布性能,采用激光里程計信息代替碼盤里程計,從而在不影響算法精度的前提下減少了粒子的數(shù)量。此外根據(jù)粒子重要性權重的離散程度決定是否進行重采樣,從而有效避免了粒子衰退現(xiàn)象。基于柵格地圖各個柵格之間狀態(tài)獨立的假設,將柵格地圖構建問題轉化為估計每個柵格后驗概率問題。考慮到機器人自身尺寸對實際導航的影響,根據(jù)機器人尺寸在柵格地圖中將靜態(tài)障礙物和實時檢測到的動態(tài)障礙物進行膨脹處理。以代價地圖、機器人位姿和目標點為基礎,采用A*算法進行全局路徑規(guī)劃,給出從當前位姿到目標位姿的全局路徑規(guī)劃結果。在全局路徑的指導下,采用動態(tài)窗口法進行局部路徑規(guī)劃,該方法可在速度空間中完成最優(yōu)速度的搜索。此外引入機器人動力學約束,可進一步減小搜索空間,只保留可達速度,并最終基于評價函數(shù)選出最優(yōu)局部路徑解。在室內(nèi)大范圍環(huán)境中,以Pioneer DX3移動機器人為硬件平臺,以ROS為基礎構建服務機器人的自主導航軟件系統(tǒng)。面向不同室內(nèi)場景開展實驗驗證工作,試驗結果表明本文所提方法的有效性和實用性。
[Abstract]:In recent years, service robots are gradually entering thousands of households, but also constantly changing people's daily life style. Autonomous navigation is the prerequisite for the service robot to complete other complex tasks. In this paper, the autonomous navigation problem of service robot based on two-dimensional laser ranging in unknown environment is studied. The emphasis includes pose tracking, autonomous positioning, grid map construction and path planning. In order to realize the pose tracking of mobile robot effectively, the three typical scanning matching algorithms, Mb-ICP,PSM port PL-ICP, are compared and analyzed, and the PL-ICP algorithm is chosen as the final odometer construction algorithm. In order to reduce the influence of the original clutter data on the algorithm, median filtering and segmentation are used to pre-process the laser data, and the clutter points and ineffective points are eliminated, so as to improve the robustness of the position-and-pose tracking algorithm. In addition, the key frame technique is used to solve the pose drift caused by the dynamic error of laser data. The Rao-Blackwellized particle filter algorithm is used to decompose the (SLAM) problem of robot simultaneous location and map construction into two parts: global pose estimation and grid map construction. In order to improve the proposed distribution performance of particle filtering algorithm, laser odometer information is used instead of disk odometer to reduce the number of particles without affecting the accuracy of the algorithm. In addition, resampling is determined according to the discrete degree of particle importance weight, so that the particle decay can be avoided effectively. Based on the assumption that each grid is independent of each grid, the problem of constructing raster map is transformed into the problem of estimating the posterior probability of each grid. Considering the influence of robot size on actual navigation, static obstacle and real-time detected dynamic obstacle are expanded in grid map according to robot size. Based on the cost map, robot pose and target point, the global path planning is carried out by using the A * algorithm, and the global path planning results from the current position to the target position are given. Under the guidance of the global path, the dynamic window method is used to carry out the local path planning. This method can search the optimal speed in the velocity space. In addition, the search space can be further reduced by introducing robot dynamics constraints, and only the reachable speed can be retained. Finally, the optimal local path solution can be selected based on the evaluation function. In the large-scale indoor environment, the autonomous navigation software system of service robot is constructed based on ROS and Pioneer DX3 mobile robot as hardware platform. Experiments are carried out on different indoor scenes, and the experimental results show that the method proposed in this paper is effective and practical.
【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP242
本文編號：2461588
[Abstract]:In recent years, service robots are gradually entering thousands of households, but also constantly changing people's daily life style. Autonomous navigation is the prerequisite for the service robot to complete other complex tasks. In this paper, the autonomous navigation problem of service robot based on two-dimensional laser ranging in unknown environment is studied. The emphasis includes pose tracking, autonomous positioning, grid map construction and path planning. In order to realize the pose tracking of mobile robot effectively, the three typical scanning matching algorithms, Mb-ICP,PSM port PL-ICP, are compared and analyzed, and the PL-ICP algorithm is chosen as the final odometer construction algorithm. In order to reduce the influence of the original clutter data on the algorithm, median filtering and segmentation are used to pre-process the laser data, and the clutter points and ineffective points are eliminated, so as to improve the robustness of the position-and-pose tracking algorithm. In addition, the key frame technique is used to solve the pose drift caused by the dynamic error of laser data. The Rao-Blackwellized particle filter algorithm is used to decompose the (SLAM) problem of robot simultaneous location and map construction into two parts: global pose estimation and grid map construction. In order to improve the proposed distribution performance of particle filtering algorithm, laser odometer information is used instead of disk odometer to reduce the number of particles without affecting the accuracy of the algorithm. In addition, resampling is determined according to the discrete degree of particle importance weight, so that the particle decay can be avoided effectively. Based on the assumption that each grid is independent of each grid, the problem of constructing raster map is transformed into the problem of estimating the posterior probability of each grid. Considering the influence of robot size on actual navigation, static obstacle and real-time detected dynamic obstacle are expanded in grid map according to robot size. Based on the cost map, robot pose and target point, the global path planning is carried out by using the A * algorithm, and the global path planning results from the current position to the target position are given. Under the guidance of the global path, the dynamic window method is used to carry out the local path planning. This method can search the optimal speed in the velocity space. In addition, the search space can be further reduced by introducing robot dynamics constraints, and only the reachable speed can be retained. Finally, the optimal local path solution can be selected based on the evaluation function. In the large-scale indoor environment, the autonomous navigation software system of service robot is constructed based on ROS and Pioneer DX3 mobile robot as hardware platform. Experiments are carried out on different indoor scenes, and the experimental results show that the method proposed in this paper is effective and practical.
【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP242
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