2,742 research outputs found
Autonomous exploration and mapping using a mobile robot running ROS
This paper proposes a practical solution to the autonomous exploration and mapping problem using a single mobile robot. Moreover, the authors implement the proposed scheme within the Robot Operating System (ROS), and validate it experimentally using PowerBot, a real wheeled mobile robot equipped with a 2D laser scanner. In essence, the proposed scheme integrates an efficient particle-filter-based SLAM algorithm, two different exploration strategies, and a path-planning and navigation module. The modular nature of the proposed scheme is intentional and advantageous. It allows the authors to compare the two exploration strategies under investigation objectively and with ease. Finally, hypotheses testing is also used to strengthen the results of the comparative analysis.peer-reviewe
Experiences on a motivational learning approach for robotics in undergraduate courses
This paper presents an educational experience carried out in robotics undergraduate courses from two
different degrees: Computer Science and Industrial Engineering, having students with diverse
capabilities and motivations. The experience compares two learning strategies for the practical
lessons of such courses: one relies on code snippets in Matlab to cope with typical robotic problems
like robot motion, localization, and mapping, while the second strategy opts for using the ROS
framework for the development of algorithms facing a competitive challenge, e.g. exploration
algorithms. The obtained students’ opinions were instructive, reporting, for example, that although they
consider harder to master ROS when compared to Matlab, it might be more useful in their (robotic
related) professional careers, which enhanced their disposition to study it. They also considered that
the challenge-exercises, in addition to motivate them, helped to develop their skills as engineers to a
greater extent than the skeleton-code based ones. These and other conclusions will be useful in
posterior courses to boost the interest and motivation of the students.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously
navigate through, identify, and reach areas of interest; and there recognize,
localize, and manipulate work tools to perform complex manipulation tasks. The
proposed contribution includes a modular software architecture where each
module solves specific sub-tasks and that can be easily enlarged to satisfy new
requirements. Included indoor and outdoor tests demonstrate the capability of
the proposed system to autonomously detect a target object (a panel) and
precisely dock in front of it while avoiding obstacles. They show it can
autonomously recognize and manipulate target work tools (i.e., wrenches and
valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve
stem). A specific case study is described where the proposed modular
architecture lets easy switch to a semi-teleoperated mode. The paper
exhaustively describes description of both the hardware and software setup of
RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International
Robotics Challenge, and the lessons we learned when participating at this
competition, where we ranked third in the Gran Challenge in collaboration with
the Czech Technical University in Prague, the University of Pennsylvania, and
the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics,
published by Taylor & Franci
Local Generating Map System Using Rviz ROS and Kinect Camera for Rescue Robot Application
This paper presents a model to generate a 3D model of a room, where room mapping is very necessary to find out the existing real conditions, where this modeling will be applied to the rescue robot. To solve this problem, researchers made a breakthrough by creating a 3D room mapping system. The mapping system and 3D model making carried out in this study are to utilize the camera Kinect and Rviz on the ROS. The camera takes a picture of the area around it, the imagery results are processed in the ROS system, the processing carried out includes several nodes and topics in the ROS which later the signal results are sent and displayed on the Rviz ROS. From the results of the tests that have been carried out, the designed system can create a 3D model from the Kinect camera capture by utilizing the Rviz function on the ROS. From this model later every corner of the room can be mapped and modeled in 3
Learning to Prevent Monocular SLAM Failure using Reinforcement Learning
Monocular SLAM refers to using a single camera to estimate robot ego motion
while building a map of the environment. While Monocular SLAM is a well studied
problem, automating Monocular SLAM by integrating it with trajectory planning
frameworks is particularly challenging. This paper presents a novel formulation
based on Reinforcement Learning (RL) that generates fail safe trajectories
wherein the SLAM generated outputs do not deviate largely from their true
values. Quintessentially, the RL framework successfully learns the otherwise
complex relation between perceptual inputs and motor actions and uses this
knowledge to generate trajectories that do not cause failure of SLAM. We show
systematically in simulations how the quality of the SLAM dramatically improves
when trajectories are computed using RL. Our method scales effectively across
Monocular SLAM frameworks in both simulation and in real world experiments with
a mobile robot.Comment: Accepted at the 11th Indian Conference on Computer Vision, Graphics
and Image Processing (ICVGIP) 2018 More info can be found at the project page
at https://robotics.iiit.ac.in/people/vignesh.prasad/SLAMSafePlanner.html and
the supplementary video can be found at
https://www.youtube.com/watch?v=420QmM_Z8v
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