Relevant bibliographies by topics / TurtleBot

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'TurtleBot'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 June 2025

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Journal articles on the topic "TurtleBot"

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Hou, Yew Cheong, Khairul Salleh Mohamed Sahari, Leong Yeng Weng, et al. "Development of collision avoidance system for multiple autonomous mobile robots." International Journal of Advanced Robotic Systems 17, no. 4 (2020): 172988142092396. http://dx.doi.org/10.1177/1729881420923967.

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This article presents a collision avoidance system for multiple robots based on the current autonomous car collision avoidance system. The purpose of the system is to improve the current autonomous car collision avoidance system by including data input of other vehicles’ velocity and positioning via vehicle-to-vehicle communication into the current autonomous car collision avoidance system. There are two TurtleBots used in experimental testing. TurtleBot is used as the robot agent while Google Lightweight Communication and Marshalling is used for inter-robot communication. Additionally, Gazebo software is used to run the simulation. There are two types of collision avoidance system algorithm (collision avoidance system without inter-robot communication and collision avoidance system with inter-robot communication) that are developed and tested in two main road crash scenarios, rear end collision scenario and junction crossing intersection collision scenario. Both algorithms are tested and run both in simulation and experiment setup, each with 10 repetitions for Lead TurtleBot sudden stop, Lead TurtleBot decelerate, Lead TurtleBot slower speed, and straight crossing path conditions. Simulation and experimental results data for each algorithm are recorded and tabulated. A comprehensive comparison of performance between the proposed algorithms is analyzed. The results showed that the proposed system is able to prevent collision between vehicles with an acceptable success rate.
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Butt, Rizwan Aslam, and Syed M. Usman Ali. "Semantic Mapping and Motion Planning with Turtlebot Roomba." IOP Conference Series: Materials Science and Engineering 51 (December 16, 2013): 012024. http://dx.doi.org/10.1088/1757-899x/51/1/012024.

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Pramod Thale, Sumegh, Mihir Mangesh Prabhu, Pranjali Vinod Thakur, and Pratik Kadam. "ROS based SLAM implementation for Autonomous navigation using Turtlebot." ITM Web of Conferences 32 (2020): 01011. http://dx.doi.org/10.1051/itmconf/20203201011.

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This paper presents the autonomous navigation of a robot using SLAM algorithm.The proposed work uses Robot Operating system as a framework.The robot is simulated in gazebo and Rviz used for data visualization.Gmapping package is used for mapping by utilizing laser and odometry data from various sensors.The Turtlebot provides open source software to perform navigation.
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Phalak, Yogesh, Gaurav Charpe, and Kartik Paigwar. "Omnidirectional Visual Navigation System for TurtleBot Using Paraboloid Catadioptric Cameras." Procedia Computer Science 133 (2018): 190–96. http://dx.doi.org/10.1016/j.procs.2018.07.023.

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B., Cristian F. Penagos, Luis A. Pacheco R., and Fredy H. Martínez S. "ARMOS TurtleBot 1 Robotic Platform: Description, Kinematics and Odometric Navigation." International Journal of Engineering and Technology 10, no. 5 (2018): 1402–9. http://dx.doi.org/10.21817/ijet/2018/v10i5/181005043.

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黎, 凯龙. "Experimental Design of Autonomous Navigation and Obstacle Avoidance for Turtlebot Robot." Artificial Intelligence and Robotics Research 10, no. 03 (2021): 257–67. http://dx.doi.org/10.12677/airr.2021.103026.

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Quan, Hao, Yansheng Li, and Yi Zhang. "A novel mobile robot navigation method based on deep reinforcement learning." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092167. http://dx.doi.org/10.1177/1729881420921672.

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At present, the application of mobile robots is more and more extensive, and the movement of mobile robots cannot be separated from effective navigation, especially path exploration. Aiming at navigation problems, this article proposes a method based on deep reinforcement learning and recurrent neural network, which combines double net and recurrent neural network modules with reinforcement learning ideas. At the same time, this article designed the corresponding parameter function to improve the performance of the model. In order to test the effectiveness of this method, based on the grid map model, this paper trains in a two-dimensional simulation environment, a three-dimensional TurtleBot simulation environment, and a physical robot environment, and obtains relevant data for peer-to-peer analysis. The experimental results show that the proposed algorithm has a good improvement in path finding efficiency and path length.
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Kumar, Neerendra, and Zoltán Vámossy. "Obstacle recognition and avoidance during robot navigation in unknown environment." International Journal of Engineering & Technology 7, no. 3 (2018): 1400. http://dx.doi.org/10.14419/ijet.v7i3.13926.

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In this paper, firstly, a model for robot navigation in unknown environment is presented as a Simulink model. This model is applicable for obstacles avoidance during the robot navigation. However, the first model is unable to recognize the re-occurrences of the obstacles during the navigation. Secondly, an advanced algorithm, based on the standard deviations of laser scan range vectors, is proposed and implemented for robot navigation. The standard deviations of the lasers scans, robot positions and the time of scans with similar standard deviations are used to obtain the obstacle recognition feature. In addition to the obstacle avoidance, the second algorithm recognizes the re-appearances of the obstacles in the navigation path. Further, the obstacle recognition feature is used to break the repetitive path loop in the robot navigation. The experimental work is carried out on the simulated Turtlebot robot model using the Gazebo simulator.
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BENSACI, Chaima, Youcef ZENNIR, and Denis POMORSKI. "Control Of Mobile Robot Navigation Under The Virtual World Matlab-Gazebo." Algerian Journal of Signals and Systems 2, no. 4 (2017): 207–17. http://dx.doi.org/10.51485/ajss.v2i4.46.

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In this paper, we present our navigation control approach of a mobile robot (Turtlebot 2 robot) based on the stability Lyapunov function; our mobile robot is composed of two differential wheels. The kinematic model of the robot is presented followed by the description of the control approach. A 3D simulation under the Gazebo software is developed in interaction with the kinematic model and the control approach under MATLAB-SIMULINK software. The purpose of this study is to carry out an autonomous navigation; we initially planned different trajectories then we tried to be followed them by the robot. Our navigation strategy based on its odometry information, based on robot position and orientation errors; Velocity commands are sent for the robot to follow the chosen path. Different simulations were performed in 2D and 3D and the results obtained are presented followed by the envisaged future work.
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Tai, Lei, Shaohua Li, and Ming Liu. "Autonomous exploration of mobile robots through deep neural networks." International Journal of Advanced Robotic Systems 14, no. 4 (2017): 172988141770357. http://dx.doi.org/10.1177/1729881417703571.

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The exploration problem of mobile robots aims to allow mobile robots to explore an unknown environment. We describe an indoor exploration algorithm for mobile robots using a hierarchical structure that fuses several convolutional neural network layers with decision-making process. The whole system is trained end to end by taking only visual information (RGB-D information) as input and generates a sequence of main moving direction as output so that the robot achieves autonomous exploration ability. The robot is a TurtleBot with a Kinect mounted on it. The model is trained and tested in a real world environment. And the training data set is provided for download. The outputs of the test data are compared with the human decision. We use Gaussian process latent variable model to visualize the feature map of last convolutional layer, which proves the effectiveness of this deep convolution neural network mode. We also present a novel and lightweight deep-learning library libcnn especially for deep-learning processing of robotics tasks.
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Dissertations / Theses on the topic "TurtleBot"

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Qiu, Yinan, and Jianyuan Ma. "Tracking of more than one person in a smart environment using fixed sensors and a mobile robot." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-28147.

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In this thesis work, a system for locating different occupants in a smart environment setting using a set of small, simple (binary) sensors and a robot (Turtlebot) is designed, implemented and tested. The sensors were chosen to be simple devices containing only "ON" and "OFF" status without any functionality to identify occupants. These sensors were ubiquitously installed in an office environment at Halmstad University. The Turtlebot is an assistant robot with a Kinect camera that supports the system to recognize the occupant. The system combines new inputs to previous information using a data association algorithm that makes predictions about the future location of the occupants. Preliminary results on a short time experiments of two different scenarios show that localizing two different occupants at the same time, using the proposed data association algorithm and face recognition can be achieved with more than 80% accuracy depending on the activities in the smart home.
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Nordlund, Fredrik Hans. "Enabling Network-Aware Cloud Networked Robots with Robot Operating System : A machine learning-based approach." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-160877.

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During the recent years, a new area called Cloud Networked Robotics (CNR) has evolved from conventional robotics, thanks to the increasing availability of cheap robot systems and steady improvements in the area of cloud computing. Cloud networked robots refers to robots with the ability to offload computation heavy modules to a cloud, in order to make use of storage, scalable computation power, and other functionalities enabled by a cloud such as shared knowledge between robots on a global level. However, these cloud robots face a problem with reachability and QoS of crucial modules that are offloaded to the cloud, when operating in unstable network environments. Under such conditions, the robots might lose the connection to the cloud at any moment; in worst case, leaving the robots “brain-dead”. This thesis project proposes a machine learning-based network aware framework for a cloud robot, that can choose the most efficient module placement based on location, task, and the network condition. The proposed solution was implemented upon a cloud robot prototype based on the TurtleBot 2 robot development kit, running Robot Operating System (ROS). A continuous experiment was conducted where the cloud robot was ordered to execute a simple task in the laboratory corridor under various network conditions. The proposed solution was evaluated by comparing the results from the continuous experiment with measurements taken from the same robot, with all modules placed locally, doing the same task. The results show that the proposed framework can potentially decrease the battery consumption by 10% while improving the efficiency of the task by 2.4 seconds (2.8%). However, there is an inherent bottleneck in the proposed solution where each new robot would need 2 months to accumulate enough data for the training set, in order to show good performance. The proposed solution can potentially benefit the area of CNR if connected and integrated with a shared-knowledge platform which can enable new robots to skip the training phase, by downloading the existing knowledge from the cloud.
Under de senaste åren har ett nytt forskningsområde kallat Cloud Networked Robotics (CNR) växt fram inom den konventionella robottekniken, tack vare den ökade tillgången på billiga robotsystem och stadiga framsteg inom området cloud computing. Molnrobotar syftar på robotar med förmågan att flytta resurstunga moduler till ett moln för att ta del av lagringskapaciteten, den skalbara processorkraften och andra tjänster som ett moln kan tillhandahålla, t.ex. en kunskapsdatabas för robotar över hela världen. Det finns dock ett problem med dessa sorters robotar gällande nåbarhet och QoS för kritiska moduler placerade på ett moln, när dessa robotar verkar i instabila nätverksmiljöer. I ett sådant scenario kan robotarna när som helst förlora anslutningen till molnet, vilket i värsta fall lämnar robotarna hjärndöda. Den här rapporten föreslår en maskininlärningsbaserad nätverksmedveten ramverkslösning för en molnrobot, som kan välja de mest effektiva modulplaceringarna baserat på robotens position, den givna uppgiften och de rådande nätverksförhållanderna. Ramverkslösningen implementerades på en molnrobotsprototyp, baserad på ett robot development kit kallat TurtleBot 2, som använder sig av ett middleware som heter Robot Operating System (ROS). Ett fortskridande experiment utfördes där molnroboten fick i uppgift att utföra ett enkelt uppdrag i laboratoriets korridor, under varierande nätverksförhållanden. Ramverkslösningen utvärderades genom att jämföra resultaten från det fortskridrande experimentet med mätningar som gjordes med samma robot som utförde samma uppgift, fast med alla moduler placerade lokalt på roboten. Resultaten visar att den föreslagna ramverkslösningen kan potentiellt minska batterikonsumptionen med 10%, samtidigt som tiden för att utföra en uppgift kan minskas med 2.4 sekunder (2.8%). Däremot uppstår en flaskhals i framtagna lösningen där varje ny robot kräver 2 månader för att samla ihop nog med data för att maskinilärningsalgoritmen ska visa bra prestanda. Den förlsagna lösningen kan dock vara fördelaktig för CNR om man integrerar den med en kunskapsdatabas för robotar, som kan möjliggöra för varje ny robot att kringå den 2 månader långa träningsperioden, genom att ladda ner existerande kunskap från molnet.
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Biro, Alexander. "Combining adjustable autonomy and shared control as a new platform for controlling robotic systems with ROS on TurtleBot." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-64637.

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Fully autonomous robotic systems can fulfill their functionality, without human interaction, but their efficiency is way lower, than a robotic system, which is teleoperated by a specialist. The teleoperation of robotic systems requires  continuously high attention from the operator, if this attention is taken away or reduced, the efficiency drops heavily. The combination of Adjustable Autonomy and Shared Control represent a promising approach, of how great efficiency could be maintained in a robotic system, with a minimum of human interaction.   The goal of this project is the re-implementation of the utilitarian voting scheme for navigation for usage with modern robotic platforms, as proposed in the publication "Experiments in Adjustable Autonomy" by Jacob W. Crandall and Michael A. Goodrich. This voting scheme combines a proposed direction, which is given by a human operator, with environmental sensor data to determine the best direction for a robots next movement.   The implemented prototype in this project was developed with ROS on TurtleBot and is processing the sensor data and calculating the best direction for the robot's movement in the same way, as the original prototype. Since the original setup consists of a Nomad SuperScout robot with sixteen sonar range finders, adjustments needed to be made, to run the same algorithm on a different setup. The  correct processing of the input data and estimation of the best direction was verified by pen and paper calculations. Finally, further ideas for improving the implemented prototype and usage in other scenarios were presented.
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Hjelmare, Fredrik, and Jonas Rangsjö. "Simultaneous Localization And Mapping Using a Kinect in a Sparse Feature Indoor Environment." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81140.

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Localization and mapping are two of the most central tasks when it comes toautonomous robots. It has often been performed using expensive, accurate sensorsbut the fast development of consumer electronics has made similar sensorsavailable at a more affordable price. In this master thesis a TurtleBot\texttrademark\, robot and a MicrosoftKinect\texttrademark\, camera are used to perform Simultaneous Localization AndMapping, SLAM. The thesis presents modifications to an already existing opensource SLAM algorithm. The original algorithm, based on visual odometry, isextended so that it can also make use of measurements from wheel odometry and asingle axis gyro. Measurements are fused using an Extended Kalman Filter,EKF, operating in a multirate fashion. Both the SLAM algorithm and the EKF areimplemented in C++ using the framework Robot Operating System, ROS. The implementation is evaluated on two different data sets. One set isrecorded in an ordinary office room which constitutes an environment with manylandmarks. The other set is recorded in a conference room where one of the wallsis flat and white. This gives a partially sparse featured environment. The result by providing additional sensor information is a more robust algorithm.Periods without credible visual information does not make the algorithm lose itstrack and the algorithm can thus be used in a larger variety of environmentsincluding such where the possibility to extract landmarks is low. The resultalso shows that the visual odometry can cancel out drift introduced bywheel odometry and gyro sensors.
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Robotka, Vojtěch. "Interaktivní rozhraní pro vzdáleného robota pro Android." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-235451.

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The development of autonomous robots has made a significant progress and first personal robots for common use start to appear. To use this robots, we need to develop applications and user interfaces to interact with them. The goal of this project is to make a universal interface for robot remote control. This work focuses on robots based on the ROS platform. This gives the final application a potential of use on other robotic projetcs running on ROS. The designed remote interface accomplishes two main purposes. The first is to show important data in a context of a 3D scene to help user understand the state of the controlled robot. And the second goal is to allow the user execute some basic manipulation with the robot. The final application was successfully adapted and tested on experimental robots Care-O-Bot and Turtlebot.
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Šťastný, Martin. "Modelování a simulace robotických aplikací." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232094.

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The aim of this master thesis is to make research of Open Source software, which are used for simulation autonomous robots. At the begining is performed research of selected robotic simulators. In the first part of this work is to get familiar with robotic simulator Gazebo and robotic framework ROS. The second part of this work deals with simulating and subsequent implementation of choosen robotic tasks through the simulator Gazebo and the ROS framework.
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Roccatello, Andrea. "Planning mobile robot tasks for autonomous UVC-based COVID-19 sanification of public environments with guaranteed minimum energy distribution." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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The present essay shows a solution to automize the sanitization process of public environments with UV lamps. Human planning of the procedure may lead to dangerous consequences in case of errors. Therefore, it was chosen to automatically manage the creation of a path that guides the robot through the rooms avoiding obstacles. The project needs the environment map and the selection of areas where sanitization is required. Velocities are defined using optimization techniques to guarantee the distribution of a minimum energy level in all the areas that need sanitization, considering the limited energy autonomy of the robots due to the UV lamps consumption. To demonstrate the validity of the solutions, it was created a simulation in ROS, where a Turtlebot3-Waffle Pi robot performs the sanitization in a simulated environment. The simulation includes several map layers where it is possible to control the state of the lamps and the energy distribution. Once the simulation is over, a control will be performed to check whether the minimum threshold has been reached.
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Carrera, Tânia Ferreira. "Movimentos reativos no robô turtlebot utilizando o kinect." Master's thesis, 2013. http://hdl.handle.net/10198/11982.

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Com a chegada da visão computacional, cada vez mais os investigadores estudam novos métodos para aquisição de imagens 3D de objetos para poderem ser úteis no nosso dia-a-dia na recolha de informações tanto nas áreas de engenharia, medicina, arquitetura, educação, entre outras. O lançamento do sensor Microsoft Kinect para a Xbox, projetado como acessório para detetar o movimento dos jogadores, veio chamar a atenção, não só pelo seu preço acessível, mas também por conseguir disponibilizar informações sem criptografia, permitindo que fosse usado para outros fins além dos videojogos. Este trabalho teve como base a análise e investigação da capacidade de obter informações acerca das distâncias em profundidade, deste sensor e, aplicá-la aos movimentos reativos efetuados pelo robô TurtleBot permitindo desta forma a interação com o meio e tornar a sua movimentação autónoma. O algoritmo utilizado em linguagem C++, na estrutura de software ROS (Robot Operating System ), é uma síntese do método de mudança de direção baseado no centro (centroid em inglês), dos pontos detetados nos objetos, que permitirá o desvio do robô perante um obstáculo e, um método com clustering de PCL (Point Cloud Library), que permitirá o reconhecimento de humanos.
With the arrival of computer vision, researchers have been doing research in methods to capture 3D images of objects, in order to use the information collected in different areas such as medicine, architecture, engineering, education, and among others. The launch of the Microsoft Kinect sensor for Xbox, designed as an accessory to detect the movement of the players came calling the attention of researchers, not only by your affordable price but also for getting available information unencrypted, allowing it to be used for other purposes than videogames. In this work, have been explored the ability to get information about the distances in depth, of this sensor, and apply it to the movements made by the TurtleBot robot, thus enabling the interaction with the environment and make their movement autonomously. The algorithm used in C language + +, on software structure of ROS (Robot Operating System ), is a summary of the method of changing direction based on the centroid from detected points on objects, which will allow the change of direction of the robot when it finds an obstacle, and a method, with clustering of PCL (Point Cloud Library), which will allow the recognition of humans.
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Books on the topic "TurtleBot"

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Turtleboy and Jet the Wonderpup: A therapeutic comic for ritual abuse survivors. H.P.L. Pub, 1989.

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Book chapters on the topic "TurtleBot"

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Amsters, Robin, and Peter Slaets. "Turtlebot 3 as a Robotics Education Platform." In Robotics in Education. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26945-6_16.

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de Assis Brasil, Pedro Medeiros, Fabio Ugalde Pereira, Marco Antonio de Souza Leite Cuadros, Anselmo Rafael Cukla, and Daniel Fernando Tello Gamarra. "Dijkstra and A* Algorithms for Global Trajectory Planning in the TurtleBot 3 Mobile Robot." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71187-0_32.

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Song, Yoonji, Jaedong Kim, and Hanhyuk Cho. "TurtleGO: Application with Cubes for Children’s Spatial Ability Based on AR Technology." In Virtual, Augmented and Mixed Reality. Applications and Case Studies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21565-1_25.

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Conference papers on the topic "TurtleBot"

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Aagela, Hamza, Maha Al-Nesf, and Violeta Holmes. "An Asus_xtion_probased indoor MAPPING using a Raspberry Pi with Turtlebot robot Turtlebot robot." In 2017 23rd International Conference on Automation and Computing (ICAC). IEEE, 2017. http://dx.doi.org/10.23919/iconac.2017.8082023.

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Singh, Diksha, Esha Trivedi, Yukti Sharma, and Vandana Niranjan. "TurtleBot: Design and Hardware Component Selection." In 2018 International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2018. http://dx.doi.org/10.1109/gucon.2018.8675050.

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Baltovski, Ilia. "Turtlebot Euclid - A better intro to ROS." In ROSCon2017. Open Robotics, 2017. http://dx.doi.org/10.36288/roscon2017-900247.

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Baltovski, Ilia. "Turtlebot Euclid - A better intro to ROS." In ROSCon2017. Open Robotics, 2017. http://dx.doi.org/10.36288/roscon2017-900791.

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Lee, Jihoon. "Turtlebot 2 – the new standard hardware reference platform." In ROSCon2013. Open Robotics, 2013. http://dx.doi.org/10.36288/roscon2013-899053.

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Lee, Jihoon. "Turtlebot 2 – the new standard hardware reference platform." In ROSCon2013. Open Robotics, 2013. http://dx.doi.org/10.36288/roscon2013-900141.

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Lee, Jihoon. "Turtlebot 2 – the new standard hardware reference platform." In ROSCon2013. Open Robotics, 2013. http://dx.doi.org/10.36288/roscon2013-900685.

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Xiong, Chuantang, and Xu Zhang. "An exclusive human-robot interaction method on the TurtleBot platform." In 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2013. http://dx.doi.org/10.1109/robio.2013.6739662.

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Shahbaz, Syed Ali, and Abhijith Anil Anjana. "Autonomous Navigation Using Partial Artificial Potential Fields On Differential Drive Turtlebot." In 2018 International Conference on Intelligent Autonomous Systems (ICoIAS). IEEE, 2018. http://dx.doi.org/10.1109/icoias.2018.8494152.

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Horton, Michael, Lei Chen, and Biswanath Samanta. "Enhancing the security of IoT enabled robotics: Protecting TurtleBot file system and communication." In 2017 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2017. http://dx.doi.org/10.1109/iccnc.2017.7876208.

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