Özet:

In the last few decades, the robotics world has seen great progress at all levels, from personal assistant robots to multi-robotic and intelligent swarm systems. Simulation platforms play a critical role in this improvement due to efficiency, flexibility, and fault tolerance they provide during the cycles of developing and testing new strategies and algorithms. In this paper, we model a new mobile robot equipped with a 2D Lidar using Robot Operating System (ROS) and use this robot model to develop a robot detection method in Gazebo simulation environment. Detecting surrounding objects and distinguishing robots from these objects (kin detection) are essential in multi-robot and swarm robotic applications. In this paper, we use Lidar to handle this task by applying the following steps: (1) acquisition of laser data and pre-processing, (2) segmentation of data using the point-distance-based segmentation method, (3) classification of segments by applying two levels of filtering: filtering by segment diameter which aims to eliminate segments that don’t fit a certain size (Lidar size) using features for each segment, filtering by segment shape to check remaining segments to test if they fit the Lidar's shape (which is a circle with known radius) or not by using the circle fitting method and (4) identify the position of kin relative to the observer robot. Two different scenarios are discussed in the experiments section. In the first scenario, many cylindrical objects with radius different from the robot’s Lidar were used in addition to a robot, and thus objects are distinguished from the robot in the first level of filtering without using the second one which may be a complex operation. In the second scenario, various objects with a similar radius were used, and due to the similarity in the radius between the Lidar and the objects, it was necessary to apply all the method’s steps to detect the kin robots. It was noticed from experiments that the accuracy of the results depends on two main factors: the distance between the observer robot and other objects or robots and the amount of noise in the Lidar measurements.

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