Abstract--Localization is the challenge of determining the robot's pose in a mapped environment. This is done by implementing a probabilistic algorithm to filter noisy sensor measurements and track the robot's position and orientation. This paper focuses on localizing a robot in a known mapped environment using Adaptive Monte Carlo Localization or Particle Filters method and send it to a goal state. ROS, Gazebo and RViz were used as the tools of the trade to simulate the environment and programming two robots for performing localization. The benchmark robot's URDF was given The benchmark robot was called UdacityBot the robot's current position and orientation. So, it is very and the 2nd robot was called SagarBot. The world-map is obvious that without this knowledge, the robot won't be called'Jackal-Race' and was created by Clearpath Robotics. There are 3 different types of localization problems.

The Robot Operating System (ROS) is a popular framework and ecosystem that allows developers to build robot software systems from reusable, off-the-shelf components. Systems are often built by customizing and connecting components via configuration files. While reusable components theoretically allow rapid prototyping, ensuring proper configuration and connection is challenging, as evidenced by numerous questions on developer forums. Developers must abide to the often unchecked and unstated assumptions of individual components. Failure to do so can result in misconfigurations that are only discovered during field deployment, at which point errors may lead to unpredictable and dangerous behavior. Despite misconfigurations having been studied in the broader context of software engineering, robotics software (and ROS in particular) poses domain-specific challenges with potentially disastrous consequences. To understand and improve the reliability of ROS projects, it is critical to identify the types of misconfigurations faced by developers. To that end, we perform a study of ROS Answers, a Q&A platform, to identify and categorize misconfigurations that occur during ROS development. We then conduct a literature review to assess the coverage of these misconfigurations by existing detection techniques. In total, we find 12 high-level categories and 50 sub-categories of misconfigurations. Of these categories, 27 are not covered by existing techniques. To conclude, we discuss how to tackle those misconfigurations in future work.

The recent adoption of the Robot Operating System (ROS) as a software standard in robotics has contributed to novel solutions for several problems on the area. One such problem is known as Simultaneous Localization and Mapping (SLAM) with autonomous navigation, for which a number of algorithms from different classes are available as ROS packages ready to be used on any compatible robot. Many anticipated applications of autonomous mobile robots require for them to navigate in diverse complex environments without support from exterior infrastructures. To perform this on-board navigation, the robot must make use of the available sensor technologies and fuse the most reliable data respective to the present environment in an adaptive manner and optimize the algorithm parameters prior to the actual implementation to reduce the workaround time. This paper will review recent efforts to develop onboard navigation systems which can seamlessly transition between outdoor and indoor environments and different terrains seamlessly using Gazebo simulator with ROS integration. The methodologies surveyed include SLAM, Odometry and Localisation. An overview of the state-of-the-art is provided with a focus on approaches which are adaptive to dynamic sensor uncertainty, dynamic objects and dynamic scenes. The experiences reported on this work should provide insight for roboticists seeking an Autonomous SLAM solution for indoor applications.

This is the second chapter of the series "Exploring ROS2 with a wheeled robot". In this episode, you'll learn how to subscribe to a ROS2 topic using ROS2 C . Before anything else, make sure you have the rosject from the previous post, you can copy it from here. Launch the simulation in one webshell and in a different tab, checkout the topics we have available. Our goal is to read the laser data, so create a new file called reading_laser.cpp:

This is the 1st chapter of the series "Exploring ROS2 with a wheeled robot". From cloning, compiling and creating a package launch file to start the simulation! In this series we are using ROS2 foxy, go to this page, create a new rosject selecting ROS2 Foxy distro and and run it. The first step is to clone the dolly robot package. Open a web shell and execute the following: cd /ros2_ws/src/ git clone https://github.com/chapulina/dolly.git Notice we are ignoring the ignition related package, that's because we will work only with gazebo simulator.