Sheng Zhao and Manish Kumar
Multi-robot control, experimental setup, swarm systems, self-localization
Self-localization, a process by which a robot obtains its own global positional state (consisting of position and bearing), is an important aspect of multi-robot experiments. A traditional method to carry out self-localization is to attach a unique visual pattern to the robot. A centralized sensor, such as a camera, is typically used to identify the pattern and measure its state directly. However, the use of patterns presents several challenges and limitations. To eliminate the use of a pattern, a novel method has been proposed in this paper for the robots to obtain their global states by themselves. In this method, we implement an extended Kalman filter (EKF) on each robot to fuse the global position data with the control input data to estimate the bearing. In a multi-robot setting, this becomes challenging because the positional data obtained from sensors such as a camera is untagged and robots do not have a prior knowledge about which data pertains to their own position. To overcome this issue, we propose a method in which each robot can identify its own track from the other’s tracks by comparing the average measurement residual of the EKFs that are run on each candidate track. Therefore, instead of identifying and localizing robots in a centralized manner, we distribute the task to each robot and let them localize themselves. Extensive experiments have been conducted and the results are provided to show the effectiveness of this method.
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