ناوبري خودمختار در محيط شهري به كمك نقشه

چكيده انگليسي :

80 Map Aided Autonomous Navigation in Urban Environment Soheil Boodaghie s boodaghie@ec iut ac ir Date of Submission 2016 6 22 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language FarsiSupervisor M Palhang Assoc Prof palhang@cc iut ac irAbstract Autonomous vehicle s structure consists of four parts The Perception localization motion planningand motion control For implementing navigation data obtained from various sensors are collected andthen required information is extracted from them Based on this information vehicle location vehiclestatus to its surrounding environment and position of all identified static and dynamic obstacles aroundvehicle can be achieved Then set of actions or paths that would guide vehicle to accomplish its missionare designed in motion planning segment and given to motion control segment for executing It can besaid that the most important factor in navigation is localization In an urban environment a map thatcontains all roads and intersections information would be a great help for localization process Thisinformation may be provided in the form of a graph In this graph links and nodes are respectivelyrepresented as roads and intersections of the real road network map With entering road networkinformation into localization domain a new concept called Map Matching is introduced The mainpurpose of map matching is finding the link of the graph that vehicle is located on This link is calledchosen link A map matching algorithm usually behave as follow at the first step all links which has thepossibility of being the chosen link are selected These links are called candidate links Then the chosenlink is selected among these candidate links This means vehicle is on this link And at the end vehiclelocation is estimated on the chosen link GPS data has an important role in map matching algorithms Inthis thesis some of the most commonly used map matching algorithms that follow the mentionedbehavior are presented and examined As an example of more advanced map matching algorithms particle filter based map matching algorithms are presented Disadvantages of these algorithms andproposals to their improvements are expressed All algorithms are evaluated on real dataset and arecompared based on two parameters correct estimation on map matching and execution time Thementioned autonomous structure is modeled with this difference that it has been assumed that there is nodynamic obstacle in the environment therefore the motion planner is only executed at the first step Dijkstra algorithm is used for motion planner The proposed map matching method that gives the bestperformance is selected and used in localization segment of autonomous navigation architecture Simulated vehicle has similar dynamic constrains of a real car and it can be guided just by controllingspeed and steering angle in motion control segment All map matching evaluation and autonomousexperiments are executed and simulated in MATLAB software environment Autonomous experimentsare performed for different GPS errors and in all of them the vehicle is accomplished its mission andreached the destination point successfully Presented architecture has the ability to be executed on everypossible map Keywords autonomous navigation localization urban environment digital map mapmatching

بوداغي، سهيل

ناوبري خودمختار در محيط شهري به كمك نقشه

مكان يابي , نقشه ي ديجيتالي , تطبيق نقشه