Implementation of a unified simulation for robot arm control with object detection based on ROS and Gazebo

Abstract

In this paper, we present a method to implement a robotic system with deep learning-based object detection in a simulation environment. The simulation environment is developed in Gazebo and run on Robot Operating System(ROS). ROS is a set of open-source software libraries that aims to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms. Gazebo is the convenient 3D simulator for use along with ROS. This paper introduces the steps to create a robot arm system controlled by ROS and object detection system using images from camera in Gazebo environment.