DGPS와 도로연석을 이용한 가변적 위치추정 환경에 적응하는 자율주행제어
Autonomous Navigation Control adapting to Various Localization Conditions using DGPS and Road Curbs
- 주제(키워드) autonomous navigation
- 주제(KDC) 569
- 주제(DDC) 621
- 발행기관 아주대학교
- 지도교수 홍석교
- 발행년도 2007
- 학위수여년월 2007. 8
- 학위명 박사
- 학과 및 전공 일반대학원 전자공학과
- 본문언어 영어
초록/요약
Autonomous navigation which is defined as estimating the position and attitude of the vehicle at any moment when they are needed in a local or global coordinate system has been a dream in computer science. There are many good reasons to operate the vehicle autonomously without human’s help in particular environments. To avoid the presence of human in dangerous places, to avoid human errors and to move the human effort to high-level activities or decisions can be examples. Additionally, they are expected to be faster, more efficient, safer and more reliable than human operated vehicles. Thanks to present computer and sensor technology, it is now possible to create high integrity autonomous vehicles for indoor/outdoor navigation system. In indoor cases, personal service robots perform the missions of guiding tourists in museum, cleaning room and nursing the elderly. In outdoor cases, mobile robots have been used for the purpose of patrol, reconnaissance, surveillance or exploration of other planets. Generally, navigation problem is divided by the four main subtasks: mapping, localization, planning, and path tracking. In this thesis, the autonomous mobile robot system is developed to autonomously navigate for the purpose of patrol. The localization of the robot is performed by EKF algorithm using odometry, angle sensor, and DGPS. Especially, in an urban environment, DGPS receiver is often blocked by high buildings and trees which cause inaccurate positioning and the blockage prevent the robot from navigating reliably and safely on the roadway. As a supplementary method, the curb on the roadway can be used to correct robot pose and it perform a role of a landmark. For this purpose, curb detection algorithm is developed and implemented in the navigation algorithm. Four different kinds of navigation controller are developed and they are switched to adapt to various localization conditions according to the availability of DGPS and the existence of the curbs on the roadway. Curb position extracted from the curb detection algorithm is used when the position error bound of DGPS becomes large. The waypoints along the roadway to patrol are selected and smoothed by the Bezier curve algorithm. A lot of autonomous experiments on the roadway are executed and the results show that the designed controller improves the navigation performance adapting to the environment conditions.
more목차
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related Research 2
1.3 Proposed Approach 6
1.4 Thesis Outline 7
Chapter 2 System Configuration 9
2.1 Hardware Configuration 9
2.1.1 System Setup 9
2.1.2 Sensors 11
2.2 Software Configuration 26
Chapter 3 Map Building 30
3.1 Map Representations 30
3.1.1 Feature Maps 30
3.1.2 Topological Maps 32
3.1.3 Occupancy Grid Maps 34
3.2 Local Environment Mapping 36
3.2.1 Implementation of ICP 37
3.2.2 Histogramic In-Motion Mapping (HIMM) 42
3.2.3 Experiments 43
3.3 Curb Detection Algorithm 44
3.3.1 Curb Point Detection 44
3.3.2 Parameter Estimation of Curb 48
3.3.3 Experiments 51
Chapter 4 Localization 55
4.1 EKF-based Localization 55
4.1.1 Discrete Extended Kalman Filter 55
4.1.2 Prediction Equation 56
4.1.3 Update Equation 57
4.2 Experiments 59
Chapter 5 Planning 62
5.1 Path Planning 62
5.1.1 Global Path Planning 63
5.1.2 Obstacle Avoidance 64
5.2 Trajectory Generation 66
Chapter 6 Autonomous Navation 71
6.1 Path Tracking Controller 71
6.2 Kinematic Tracking Controller 71
6.2.1 Kanayama Controller 72
6.2.2 Basic Controller Experiments 74
6.3 Strategy for Autonomous Navigation 82
6.3.1 Design of C1 Controller 83
6.3.2 Design of C2 Controller 84
6.3.3 Design of C3 Controller 85
6.3.4 Design of C4 Controller 86
6.4 Experiments 86
6.4.1 The Effects of the Number of Visible Satellite 91
6.4.2 The Effects of Distorted Magnetic Field 92
6.4.3 Autonomous Navigation Experiment 93
Chapter 7 Conclusions 101
Bibliography 102
