Abstract
Recently, multi-robot systems are emerging in various industries. The multi-robot systems have several advantages in comparison with a single-robot system. The task of single-robot system is limited, because a bigger robot is required to perform more multiple functions. However, multi-robot systems can distribute the functions to each robot. The localization problem is essential for realization and it becomes more important in the multi-robot system. The multi-robot system needs to work maintaining a formation. In order to accomplish the given mission in the demanded formation, the localization is important. For multi-robot localization, we use the relative position to find robots' position based on odometry sensor, and the iterative Kalman filter algorithm is utilized to estimate the accurate position.
Highlights
The mobile robots can be used in various fields, and the mobile robots’ technology and devices are classified according to the applied areas
When the robot uses only the dead-reckoning technique based on an odometry sensor, the accuracy of localization is limited by accumulation of positioning error, slip of a robot’s wheel, kidnap problem, etc
These sensors observe the self-motion of the robot; 2) All robots are equipped with communication unit in order to exchange its own position and orientation within the group; 3) The master robot carries exteroceptive sensor which measures respective distant and bearing of satellite robots
Summary
The mobile robots can be used in various fields, and the mobile robots’ technology and devices are classified according to the applied areas. When the robot uses only the dead-reckoning technique based on an odometry sensor, the accuracy of localization is limited by accumulation of positioning error, slip of a robot’s wheel, kidnap problem, etc. The multi-robot localization using relative observations was proposed by Martinelli [5]. We propose the localization of multi-robot system using the relative position in this paper. In order to compensate the errors of odometry sensor, we use IR sensor as measurement values, we apply the iterative extended Kalman filter to this process to track the accurate paths of multi-robot. Ts is the discrete sampling time, r is the wheel radius, b is the distance between wheel centers of the vehicle. ε is a Gaussian white noise
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