Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Abstract

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.

Indoor location recognition device and method based on indoor robot

“…rplidar The ranging core of A1 rotates clockwise, which can realize 360-degree scanning and ranging detection of the surrounding environment, so as to obtain the contour map of the surrounding environment; the internal optical and algorithm system is fully adopted, so that the robot can build more quickly and accurately. frequency, connect the rplidar with the single-board computer Raspberry Pi via a USB cable, and it can be used directly without any coding work [8].…”

Lidar-based Robot Detection and Positioning using Machine Learning Methods

“…Finally, the Support Vector Machine (SVM) machine learning method has been used to detect the target circular objects. Another Lidarbased kin detection study was performed by us recently [35]. This study developed a geometric approach for kin detection and used the same simulation model and the Lidar for the proposed method.…”

“…Third, ROS has many high-level libraries (such as navigation and planning) that can be valuable for swarm robotic systems. In future works, we plan to finalize the development of the Layka swarm robotic platform, test proposed methods on the real robot platform, and analyze the effect of sensor noise which is one of the limitations of this study and the previous one [35].…”

Semantic Trajectory Planning for Industrial Robotics

“…The industrial robotic landscape frequently presents scenarios involving mobile objects or evolving conditions. Notable discourse has emerged regarding the importance of object detection in multi-robot and swarm robotic applications, underlining its irreplaceable role in these contexts (Yilmaz & Bayindir, 2019). This research paper presents a significant contribution to the field of industrial robotics through the introduction and validation of an innovative Semantic Trajectory Planning approach.…”