ROS-based Multiple Cameras Video Streaming for a  Teleoperation Interface of a Crawler Robot

Edgar, Martínez-García; Arkadevich, Magid Evgeni; Nabiullovich, Safin Ramil; Lavrenov, Roman

doi:10.2991/jrnal.2018.5.3.9

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…It is a collection of tools, libraries, and conventions that aim to simplify the task of designing complex and robust robot behavior across various platforms. Since creating robust robot software is very hard, no single individual, laboratory, or institution can hope to do it on their own [8,12,13]. Thus ROS was built to encourage collaborative robotics software development.…”

Section: Robot Operating Systemmentioning

confidence: 99%

Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning

Chang¹,

Tsai²,

Lu³

et al. 2020

IJCIS

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Nowadays, drones are expected to be used in several engineering and safety applications both indoors and outdoors, e.g., exploration, rescue, sport, entertainment, and convenience. Among those applications, it is important to make a drone capable of flying autonomously to carry out an inspection patrol. In this paper, we present a novel method that uses ArUco markers as a reference to improve the accuracy of a drone on autonomous straight take-off, flying forward, and landing based on Deep Reinforcement Learning (DRL). More specifically, the drone first detects a specific marker with one of its onboard cameras. Then it calculates the position and orientation relative to the marker so as to adjust its actions for achieving better accuracy with a DRL method. We perform several simulation experiments with different settings, i.e., different sets of states, different sets of actions and even different DRL methods, by using the Robot Operating System (ROS) and its Gazebo simulator. Simulation results show that our proposed methods can efficiently improve the accuracy of the considered actions.

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Section: Robot Operating Systemmentioning

confidence: 99%

Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning

Chang¹,

Tsai²,

Lu³

et al. 2020

IJCIS

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show abstract

“…This camera has a good angle of view, but the lens distorts the captured image; thus, it was necessary to implement an image correction model. Therefore, a calibration process was done with an asymmetric chessboard and several image samples, using the tools incorporated in the OpenCV (Open Source Computer Vision) library [38,39].…”

Section: Image Capture and Preprocessingmentioning

confidence: 99%

Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

2019

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This paper presents the design and construction of a robotic arm that plays chess against a human opponent, based on an artificial vision system. The mechanical design was an adaptation of the robotic arm proposed by the rapid prototyping laboratory FabLab RUC (Fabrication Laboratory of the University of Roskilde). Using the software Solidworks, a gripper with 4 joints was designed. An artificial vision system was developed for detecting the corners of the squares on a chessboard and performing image segmentation. Then, an image recognition model was trained using convolutional neural networks to detect the movements of pieces on the board. An image-based visual servoing system was designed using the Kanade–Lucas–Tomasi method, in order to locate the manipulator. Additionally, an Arduino development board was programmed to control and receive information from the robotic arm using Gcode commands. Results show that with the Stockfish chess game engine, the system is able to make game decisions and manipulate the pieces on the board. In this way, it was possible to implement a didactic robotic arm as a relevant application in data processing and decision-making for programmable automatons.

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“…Among a broad variety of popular simulators, our research group concentrates on the Gazebo simulator that was designed specifically for simulating robots and their environment [16]. Being fully compatible with Robot Operating System (ROS) [17] Gazebo simulator allows creating and using robot models with ROS-based control systems without additional efforts. In most cases the Gazebo simulator is used together with the RViz simulator [18] as they provide a good complementary level of sensory input and motion activities visualization.…”

Section: Introductionmentioning

confidence: 99%

Simulation Tools for Urban Search and Rescue Robotics

Magid

2023

Proceedings of International Conference on Artificial Life and Robotics

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Real world experiments are critical for validating performance of new concepts and algorithms in robotics field. Yet, experiments tend to be too expensive in terms of time and resources of a research team. Moreover, it is not feasible to conduct thousands of complex experiments with a physical robot in a real environment. To check new ideas, preliminary evaluate new algorithms and interaction protocols, on first stages of a research project it is reasonable to start within a simulation. To produce relevant results, a simulator should provide adequate models of robots and environments with realistic physical properties. This paper presents an overview of our experience in using robot operating system (ROS) with Gazebo and Webots simulators for urban search and rescue robotics projects and considers constructing new models of mobile robots and complicated environments, algorithm validation and comparative analysis.

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ROS-based Multiple Cameras Video Streaming for a Teleoperation Interface of a Crawler Robot

Cited by 6 publications

References 9 publications

Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning

Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning

Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

Simulation Tools for Urban Search and Rescue Robotics

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