A Localization Method Using a Single Range-Based Landmark and Heading Information

• Main Authors: Sun, Kuan-Chun, 孫冠群
• Other Authors: Hu, Jwu-Sheng
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/5qeun7

博士 === 國立交通大學 === 電控工程研究所 === 103 === Mobile robot localization systems and methods are key technologies in robotic applications. The overall system design and algorithms have to be improved following the progress of newly developed hardware. So far there is not a method that is widely used as a general solution due to considerations of the estimation accuracy, cost and system complexity. This dissertation aims to find a new localization method using a limit hardware setup. This algorithm, which is based on the range to a single landmark and heading direction relative to a fixed frame, can converge to a pre-designed path without knowing the initial state. Due to the lack of measured information, this system not only suffers from the nonlinear noise, but also has no closed-form mathematical solution. Therefore, in this dissertation, several specific assumptions by geometrical and mathematical analysis of the problem are made to allow the system find a convergent analytical solution. A pair of ultrasonic sensors is used to verify the algorithm in experiment. One is installed on the landmark as a receiver and the other on a mobile robot as a transmitter. The time difference scaled by acoustic velocity is calculated as the range measurement between landmark and robot. In addition, the MARG sensors (Magnetic, Angular Rate, and Gravity) are used to estimate the orientation of the robot relative to the Earth’s NED frame (North, East, Down) in a local field. For achieving a robust estimation, a new fusion algorithm is derived. The DCM (Direct Cosine Matrix) is utilized for the rotation matrix relative to a fixed frame because of its linearity in system and measurement equations. Further, the robustness is achieved by following two online methods: compensation of hard iron effect for the magnetometer and separation of the accelerometer signals into gravity projections and linear accelerations. The fusion equations are solved by using a new developed equality and inequality state soft and hard constrained Kalman filter. In conclusion, both simulations and experiments are conducted to validate these proposed algorithms.