Relevant bibliographies by topics / Autonomous mobile robot indoor navigation

Contents

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

Academic literature on the topic 'Autonomous mobile robot indoor navigation'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Autonomous mobile robot indoor navigation"

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Valliappan, Karthik C*, and Vikram R. "Autonomous Indoor Navigation for Mobile Robots." Regular issue 10, no. 7 (May 30, 2021): 122–26. http://dx.doi.org/10.35940/ijitee.g9038.0510721.

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An autonomous navigation system for a robot is key for it to be self-reliant in any given environment. Precise navigation and localization of robots will minimize the need for guided work areas specifically designed for the utilization of robots. The existing solution for autonomous navigation is very expensive restricting its implementation to satisfy a wide variety of applications for robots. This project aims to develop a low-cost methodology for complete autonomous navigation and localization of the robot. For localization, the robot is equipped with an image sensor that captures reference points in its field of view. When the robot moves, the change in robot position is estimated by calculating the shift in the location of the initially captured reference point. Using the onboard proximity sensors, the robot generates a map of all the accessible areas in its domain which is then used for generating a path to the desired location. The robot uses the generated path to navigate while simultaneously avoiding any obstacles in its path to arrive at the desired location.
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Sleaman, Walead Kaled, and Sırma Yavuz. "Indoor mobile robot navigation using deep convolutional neural network." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5475–86. http://dx.doi.org/10.3233/jifs-189030.

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Robot can help human in their everyday life and routine. These are not an indoor robot which was designed to perform desired task, but they can adapt to our environment by themselves and to learn from their own experiences. In this research we focus on high degree of autonomy, which is a must for social robots. For training purpose autonomous exploration and unknown environments is used along with proper algorithm so that robot can adapt to unknown environments. For testing purpose, simulation is carried with sensor fusion method, so that real world noise can be reduced and accuracy can be increased. This dissertation focuses on the intelligent robot control in autonomous navigation tasks and investigates the robot learning in following aspects. This method is based on human instinct of imitation. In this standard real time data set is provided to the robot for training purpose, it gets train from these data and generalize over all unseen potential situations and environments. Convolutional Neural Network is used to determine the probability and based on that robot can act. After acceptable number of demonstrations, robot can predict output with high accuracy and hence can acquire the independent navigation skills. State-of-the-art reinforcement learning techniques is used to train the robot via interaction with the robots. Convolutional Neural Network is also incorporated for fast generalization. Robot is train based on all past state-action pairs collected during interaction. This training model can predict output which helps robot for autonomous navigation.
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Yi, Soo-Yeong, and Byoung-Wook Choi. "Autonomous navigation of indoor mobile robots using a global ultrasonic system." Robotica 22, no. 4 (August 2004): 369–74. http://dx.doi.org/10.1017/s0263574704000335.

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Autonomous navigation of an indoor mobile robot, using the global ultrasonic system, is presented in this paper. Since the trajectory error of the dead-reckoning navigation increases significantly with time and distance, the autonomous navigation system of a mobile robot requires self-localization capa-bility in order to compensate for trajectory error. The global ultrasonic system, consisting of four ultrasonic generators fixed at a priori known positions in the work space and two receivers mounted on the mobile robot, has a similar structure to the well-known satellite GPS(Global Positioning System), which is used for the localization of ground vehicles. The EKF (Extended Kalman Filter) algorithm is utilized for self-localization and autonomous navigation, based on the self-localization algorithm is verified by experiments performed in this study. Since the self-localization algorithm is efficient and fast, it is appropriate for an embedded controller of a mobile robot.
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Wang, Chaoqun, Jiankun Wang, Chenming Li, Danny Ho, Jiyu Cheng, Tingfang Yan, Lili Meng, and Max Q. H. Meng. "Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments." Sensors 19, no. 13 (July 7, 2019): 2993. http://dx.doi.org/10.3390/s19132993.

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Complex environments pose great challenges for autonomous mobile robot navigation. In this study, we address the problem of autonomous navigation in 3D environments with staircases and slopes. An integrated system for safe mobile robot navigation in 3D complex environments is presented and both the perception and navigation capabilities are incorporated into the modular and reusable framework. Firstly, to distinguish the slope from the staircase in the environment, the robot builds a 3D OctoMap of the environment with a novel Simultaneously Localization and Mapping (SLAM) framework using the information of wheel odometry, a 2D laser scanner, and an RGB-D camera. Then, we introduce the traversable map, which is generated by the multi-layer 2D maps extracted from the 3D OctoMap. This traversable map serves as the input for autonomous navigation when the robot faces slopes and staircases. Moreover, to enable robust robot navigation in 3D environments, a novel camera re-localization method based on regression forest towards stable 3D localization is incorporated into this framework. In addition, we utilize a variable step size Rapidly-exploring Random Tree (RRT) method which can adjust the exploring step size automatically without tuning this parameter manually according to the environment, so that the navigation efficiency is improved. The experiments are conducted in different kinds of environments and the output results demonstrate that the proposed system enables the robot to navigate efficiently and robustly in complex 3D environments.
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Daza, Marcos, Dennis Barrios-Aranibar, José Diaz-Amado, Yudith Cardinale, and João Vilasboas. "An Approach of Social Navigation Based on Proxemics for Crowded Environments of Humans and Robots." Micromachines 12, no. 2 (February 13, 2021): 193. http://dx.doi.org/10.3390/mi12020193.

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Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot–robot and human–robot interactions. However, existing approaches are focused either on multi-robot navigation (robot–robot interaction) or social navigation with human presence (human–robot interaction), neglecting the integration of both approaches. Proxemic interaction is recently being used in this domain of research, to improve Human–Robot Interaction (HRI). In this context, we propose an autonomous navigation approach for mobile robots in indoor environments, based on the principles of proxemic theory, integrated with classical navigation algorithms, such as ORCA, Social Momentum, and A*. With this novel approach, the mobile robot adapts its behaviour, by analysing the proximity of people to each other, with respect to it, and with respect to other robots to decide and plan its respective navigation, while showing acceptable social behaviours in presence of humans. We describe our proposed approach and show how proxemics and the classical navigation algorithms are combined to provide an effective navigation, while respecting social human distances. To show the suitability of our approach, we simulate several situations of coexistence of robots and humans, demonstrating an effective social navigation.
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Tang, Lixin, and Shin'ichi Yuta. "Mobile Robot Playback Navigation Based on Robot Pose Calculation Using Memorized Omnidirectional Images." Journal of Robotics and Mechatronics 14, no. 4 (August 20, 2002): 366–74. http://dx.doi.org/10.20965/jrm.2002.p0366.

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We propose a method of autonomous navigation for mobile robots in indoor environments by a teaching and playback scheme. During teaching, an operator guides a robot to move by manual control. While moving, the robot memorizes its motion measured by odometry and an environmental image taken by an omnidirectional camera at each time interval, and regards places where images were taken as target positions. When navigating autonomously, the robot plays back memorized motion to track each target position and corrects its position by calculating its relative pose using current and memorized images, to follow the taught route. In this method, vertical edges existing in the environment are used as landmarks to calculate robot position, and an evaluation function defined by us is used to find corresponding vertical edges between two images. The robot thus can navigate robustly in real building environments. The system can avoid the problem of the operator covering a part of the environment in images during the teaching stage.
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Cheng, Hongtai, Heping Chen, and Yong Liu. "Topological Indoor Localization and Navigation for Autonomous Mobile Robot." IEEE Transactions on Automation Science and Engineering 12, no. 2 (April 2015): 729–38. http://dx.doi.org/10.1109/tase.2014.2351814.

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Nurhafizah Anual, Siti, Mohd Faisal Ibrahim, Nurhana Ibrahim, Aini Hussain, Mohd Marzuki Mustafa, Aqilah Baseri Huddin, and Fazida Hanim Hashim. "GA-based Optimisation of a LiDAR Feedback Autonomous Mobile Robot Navigation System." Bulletin of Electrical Engineering and Informatics 7, no. 3 (September 1, 2018): 433–41. http://dx.doi.org/10.11591/eei.v7i3.1275.

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Autonomous mobile robots require an efficient navigation system in order to navigate from one location to another location fast and safe without hitting static or dynamic obstacles. A light-detection-and-ranging (LiDAR) based autonomous robot navigation is a multi-component navigation system consists of various parameters to be configured. With such structure and sometimes involving conflicting parameters, the process of determining the best configuration for the system is a non-trivial task. This work presents an optimisation method using Genetic algorithm (GA) to configure such navigation system with tuned parameters automatically. The proposed method can optimise parameters of a few components in a navigation system concurrently. The representation of chromosome and fitness function of GA for this specific robotic problem are discussed. The experimental results from simulation and real hardware show that the optimised navigation system outperforms a manually-tuned navigation system of an indoor mobile robot in terms of navigation time.
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Omrane, Hajer, Mohamed Slim Masmoudi, and Mohamed Masmoudi. "Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9548482.

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This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path.
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Gomez, Clara, Alejandra Carolina Hernandez, Jonathan Crespo, and Ramon Barber. "A topological navigation system for indoor environments based on perception events." International Journal of Advanced Robotic Systems 14, no. 1 (December 22, 2016): 172988141667813. http://dx.doi.org/10.1177/1729881416678134.

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The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.
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Dissertations / Theses on the topic "Autonomous mobile robot indoor navigation"

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Dag, Antymos. "Autonomous Indoor Navigation System for Mobile Robots." Thesis, Linköpings universitet, Programvara och system, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129419.

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With an increasing need for greater traffic safety, there is an increasing demand for means by which solutions to the traffic safety problem can be studied. The purpose of this thesis is to investigate the feasibility of using an autonomous indoor navigation system as a component in a demonstration system for studying cooperative vehicular scenarios. Our method involves developing and evaluating such a navigation system. Our navigation system uses a pre-existing localization system based on passive RFID, odometry and a particle filter. The localization system is used to estimate the robot pose, which is used to calculate a trajectory to the goal. A control system with a feedback loop is used to control the robot actuators and to drive the robot to the goal.   The results of our evaluation tests show that the system generally fulfills the performance requirements stated for the tests. There is however some uncertainty about the consistency of its performance. Results did not indicate that this was caused by the choice of localization techniques. The conclusion is that an autonomous navigation system using the aforementioned localization techniques is plausible for use in a demonstration system. However, we suggest that the system is further tested and evaluated before it is used with applications where accuracy is prioritized.
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Althaus, Philipp. "Indoor Navigation for Mobile Robots : Control and Representations." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3644.

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This thesis deals with various aspects of indoor navigationfor mobile robots. For a system that moves around in ahousehold or office environment,two major problems must betackled. First, an appropriate control scheme has to bedesigned in order to navigate the platform. Second, the form ofrepresentations of the environment must be chosen.

Behaviour based approaches have become the dominantmethodologies for designing control schemes for robotnavigation. One of them is the dynamical systems approach,which is based on the mathematical theory of nonlineardynamics. It provides a sound theoretical framework for bothbehaviour design and behaviour coordination. In the workpresented in this thesis, the approach has been used for thefirst time to construct a navigation system for realistic tasksin large-scale real-world environments. In particular, thecoordination scheme was exploited in order to combinecontinuous sensory signals and discrete events for decisionmaking processes. In addition, this coordination frameworkassures a continuous control signal at all times and permitsthe robot to deal with unexpected events.

In order to act in the real world, the control system makesuse of representations of the environment. On the one hand,local geometrical representations parameterise the behaviours.On the other hand, context information and a predefined worldmodel enable the coordination scheme to switchbetweensubtasks. These representations constitute symbols, on thebasis of which the system makes decisions. These symbols mustbe anchored in the real world, requiring the capability ofrelating to sensory data. A general framework for theseanchoring processes in hybrid deliberative architectures isproposed. A distinction of anchoring on two different levels ofabstraction reduces the complexity of the problemsignificantly.

A topological map was chosen as a world model. Through theadvanced behaviour coordination system and a proper choice ofrepresentations,the complexity of this map can be kept at aminimum. This allows the development of simple algorithms forautomatic map acquisition. When the robot is guided through theenvironment, it creates such a map of the area online. Theresulting map is precise enough for subsequent use innavigation.

In addition, initial studies on navigation in human-robotinteraction tasks are presented. These kinds of tasks posedifferent constraints on a robotic system than, for example,delivery missions. It is shown that the methods developed inthis thesis can easily be applied to interactive navigation.Results show a personal robot maintaining formations with agroup of persons during social interaction.

Keywords:mobile robots, robot navigation, indoornavigation, behaviour based robotics, hybrid deliberativesystems, dynamical systems approach, topological maps, symbolanchoring, autonomous mapping, human-robot interaction

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Hennig, Matthias, Henri Kirmse, and Klaus Janschek. "Global Localization of an Indoor Mobile Robot with a single Base Station." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-83687.

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The navigation tasks in advanced home robotic applications incorporating reliable revisiting strategies are dependent on very low cost but nevertheless rather accurate localization systems. In this paper a localization system based on the principle of trilateration is described. The proposed system uses only a single small base station, but achieves accuracies comparable to systems using spread beacons and it performs sufficiently for map building. Thus it is a standalone system and needs no odometry or other auxiliary sensors. Furthermore a new approach for the problem of the reliably detection of areas without direct line of sight is presented. The described system is very low cost and it is designed for use in indoor service robotics. The paper gives an overview on the system concept and special design solutions and proves the possible performances with experimental results.
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Rojas, Castro Dalia Marcela. "The RHIZOME architecture : a hybrid neurobehavioral control architecture for autonomous vision-based indoor robot navigation." Thesis, La Rochelle, 2017. http://www.theses.fr/2017LAROS001/document.

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Les travaux décrits dans cette thèse apportent une contribution au problème de la navigation autonome de robots mobiles dans un contexte de vision indoor. Il s’agit de chercher à concilier les avantages des différents paradigmes d’architecture de contrôle et des stratégies de navigation. Ainsi, nous proposons l’architecture RHIZOME (Robotic Hybrid Indoor-Zone Operational ModulE) : une architecture unique de contrôle robotique mettant en synergie ces différentes approches en s’appuyant sur un système neuronal. Les interactions du robot avec son environnement ainsi que les multiples connexions neuronales permettent à l’ensemble du système de s’adapter aux conditions de navigation. L’architecture RHIZOME proposée combine les avantages des approches comportementales (e.g. rapidité de réaction face à des problèmes imprévus dans un contexte d’environnement dynamique), et ceux des approches délibératives qui tirent profit d’une connaissance a priori de l’environnement. Cependant, cette connaissance est uniquement exploitée pour corroborer les informations perçues visuellement avec celles embarquées. Elle est représentée par une séquence de symboles artificiels de navigation guidant le robot vers sa destination finale. Cette séquence est présentée au robot soit sous la forme d’une liste de paramètres, soit sous la forme d’un plan. Dans ce dernier cas, le robot doit extraire lui-même la séquence de symboles à suivre grâce à une chaine de traitements d’images. Ainsi, afin de prendre la bonne décision lors de sa navigation, le robot traite l’ensemble de l’information perçue, la compare en temps réel avec l’information a priori apportée ou extraite, et réagit en conséquence. Lorsque certains symboles de navigation ne sont plus présents dans l’environnement de navigation, l’architecture RHIZOME construit de nouveaux lieux de référence à partir des panoramas extraits de ces lieux. Ainsi, le robot, lors de phases exploratoires, peut s’appuyer sur ces nouvelles informations pour atteindre sa destination finale, et surmonter des situations imprévues. Nous avons mis en place notre architecture sur le robot humanoïde NAO. Les résultats expérimentaux obtenus lors d’une navigation indoor, dans des scenarios à la fois déterministes et stochastiques, montrent la faisabilité et la robustesse de cette approche unifiée
The work described in this dissertation is a contribution to the problem of autonomous indoor vision-based mobile robot navigation, which is still a vast ongoing research topic. It addresses it by trying to conciliate all differences found among the state-of-the-art control architecture paradigms and navigation strategies. Hence, the author proposes the RHIZOME architecture (Robotic Hybrid Indoor-Zone Operational ModulE) : a unique robotic control architecture capable of creating a synergy of different approaches by merging them into a neural system. The interactions of the robot with its environment and the multiple neural connections allow the whole system to adapt to navigation conditions. The RHIZOME architecture preserves all the advantages of behavior-based architectures such as rapid responses to unforeseen problems in dynamic environments while combining it with the a priori knowledge of the world used indeliberative architectures. However, this knowledge is used to only corroborate the dynamic visual perception information and embedded knowledge, instead of directly controlling the actions of the robot as most hybrid architectures do. The information is represented by a sequence of artificial navigation signs leading to the final destination that are expected to be found in the navigation path. Such sequence is provided to the robot either by means of a program command or by enabling it to extract itself the sequence from a floor plan. This latter implies the execution of a floor plan analysis process. Consequently, in order to take the right decision during navigation, the robot processes both set of information, compares them in real time and reacts accordingly. When navigation signs are not present in the navigation environment as expected, the RHIZOME architecture builds new reference places from landmark constellations, which are extracted from these places and learns them. Thus, during navigation, the robot can use this new information to achieve its final destination by overcoming unforeseen situations.The overall architecture has been implemented on the NAO humanoid robot. Real-time experimental results during indoor navigation under both, deterministic and stochastic scenarios show the feasibility and robustness of the proposed unified approach
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McConnell, Michael, Daniel Chionuma, Jordan Wright, Jordan Brandt, and Liu Zhe. "Design of an Autonomous Robot for Indoor Navigation." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579708.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV
This paper describes the design and implementation of an autonomous robot to navigate indoors to a specified target using an inexpensive commercial off the shelf USB camera and processor running an imbedded Linux system. The robot identifies waypoints to aid in navigation, which in our case consists of a series of quick response (QR) codes. Using a 1080p USB camera, the robot could successfully identify waypoints at a distance of over 4 meters, and navigate at a rate of 50 cm/sec.
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Keepence, B. S. "Navigation of autonomous mobile robots." Thesis, Cardiff University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304921.

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Miah, Md Suruz. "Autonomous mobile robot navigation using RFID technology." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27891.

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Navigation techniques are of a paramount importance in the field of mobile robotics. They are employed in many contexts in indoor and outdoor environments such as delivering payloads in a dynamic environment, building safety, security, building measurement, research, and driving on highways. Skilled navigation in mobile robotics usually requires solving two problems, determining the position of the robot, and selecting a motion control strategy. Moreover, when no prior knowledge of the environment is available, the problem becomes even more difficult, as the robot has to build a map of its surroundings as it moves. These three tasks ought to be solved in conjunction, since they depend on each other. This dissertation explores the design of a cost-effective and modular navigation method for mobile robots. In particular, we will look at the process of navigating a mobile robot using the emerging RFID technology. A successful realization of this process has been addressed with two separate navigation modules. Each module presents a separate navigation algorithm for a mobile robot. In the first module, a customized RFID reader is mounted on the robot. The information provided by the reader will then be used for navigation. On the contrary, in the second module, custom-made RFID tags are attached at different locations in the navigation environment (on the ceiling of a building, posts, for instance). The position of the mobile robot is then determined based on the information provided by the tags in the robot's operating region. The angle between the robot's current direction and the target tag is used to provide actions to the actuators. In both modules, the algorithms take advantage of using analogue features of the RFID system instead of relying only on the binary tag number which conventional RFID-driven applications depend on.
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Campion, Joseph (Joseph F. ). "Autonomous navigation with mobile robot using ultrasonic rangefinders." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98957.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.
Cataloged from PDF version of thesis.
In this thesis, I designed and implemented an autonomous navigation system for a four-wheeled mobile robot with ultrasonic sonar sensors and a National Instruments myRIO real-time controller. LabVIEW code was developed to control the motors with PWM signals based on sensor feedback. A low-pass filter was used to improve the signal to noise ratio since the signals from the ultrasonic sonar sensors were quite noisy. Finally, I developed two basic algorithms to maneuver the mobile robot: the first algorithm uses proportional control to maintain a specific distance from a target in front of the mobile robot; the second also uses proportional control to keep the robot at a specified distance away from a wall to its side as it travels forward.
by Joseph Campion.
S.B.
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Tennety, Srinivas. "Mobile robot navigation in hilly terrains." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313757135.

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Perko, Eric Michael. "Precision Navigation for Indoor Mobile Robots." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1345513785.

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Books on the topic "Autonomous mobile robot indoor navigation"

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Joe, Bosworth, and Winkless Nels, eds. The personal robot navigator. Conifer, Colorado: Robot Press, 1998.

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Vision Based Autonomous Robot Navigation Algorithms And Implementations. Springer-Verlag Berlin and Heidelberg GmbH &, 2012.

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FLORCZYK, STEFAN. Robot Vision: Video-Based Indoor Exploration with Autonomous and Mobile Robots. Not Avail, 2005.

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Florczyk, Stefan. Robot Vision: Video-Based Indoor Exploration with Autonomous and Mobile Robots. Wiley & Sons, Incorporated, John, 2006.

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Robot Vision: Video-based Indoor Exploration with Autonomous and Mobile Robots. Wiley-VCH, 2005.

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1936-, Aggarwal J. K., and United States. National Aeronautics and Space Administration., eds. Positional estimation techniques for an autonomous mobile robot: Final report. Austin, Tex: Computer and Vision Research Center, University of Texas at Austin, 1990.

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Miller, Merl K., and Joe Bosworth. The Personal Robot Navigator. AK Peters, 1999.

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Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.
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Book chapters on the topic "Autonomous mobile robot indoor navigation"

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Noh, Sung Woo, Dong Jin Seo, Tae Gyun Kim, Seong Dae Jeong, and Kwang Jin Kim. "Implementation of Autonomous Navigation Using a Mobile Robot Indoor." In Advances in Computer Science and Ubiquitous Computing, 751–56. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0281-6_106.

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Kampmann, P., and G. Schmidt. "Indoor Navigation of Mobile Robots by Use of Learned Maps." In Information Processing in Autonomous Mobile Robots, 151–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-07896-9_11.

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Miah, M. Suruz, and Wail Gueaieb. "A Fuzzy Logic Approach for Indoor Mobile Robot Navigation Using UKF and Customized RFID Communication." In Autonomous and Intelligent Systems, 21–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21538-4_3.

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Chatterjee, Amitava, Anjan Rakshit, and N. Nirmal Singh. "Mobile Robot Navigation." In Vision Based Autonomous Robot Navigation, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33965-3_1.

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Lobo, Jorge, Lino Marques, Jorge Dias, Urbano Nunes, and Aníbal T. de Almeida. "Sensors for mobile robot navigation." In Autonomous Robotic Systems, 50–81. London: Springer London, 1998. http://dx.doi.org/10.1007/bfb0030799.

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Chatterjee, Amitava, Anjan Rakshit, and N. Nirmal Singh. "Vision Based SLAM in Mobile Robots." In Vision Based Autonomous Robot Navigation, 207–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33965-3_8.

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Chatterjee, Amitava, Anjan Rakshit, and N. Nirmal Singh. "Interfacing External Peripherals with a Mobile Robot." In Vision Based Autonomous Robot Navigation, 21–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33965-3_2.

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Chatterjee, Amitava, Anjan Rakshit, and N. Nirmal Singh. "Vision-Based Mobile Robot Navigation Using Subgoals." In Vision Based Autonomous Robot Navigation, 47–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33965-3_3.

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Chatterjee, Amitava, Anjan Rakshit, and N. Nirmal Singh. "Indigenous Development of Vision-Based Mobile Robots." In Vision Based Autonomous Robot Navigation, 83–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33965-3_4.

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Chatterjee, Amitava, Anjan Rakshit, and N. Nirmal Singh. "Vision Based Mobile Robot Path/Line Tracking." In Vision Based Autonomous Robot Navigation, 143–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33965-3_6.

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Conference papers on the topic "Autonomous mobile robot indoor navigation"

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Biswas, Joydeep, and Manuela Veloso. "WiFi localization and navigation for autonomous indoor mobile robots." In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010). IEEE, 2010. http://dx.doi.org/10.1109/robot.2010.5509842.

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Esan, Oluwayinka, Shengzhi Du, and Beneke Lodewyk. "Review on Autonomous Indoor Wheel Mobile Robot Navigation Systems." In 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE, 2020. http://dx.doi.org/10.1109/icabcd49160.2020.9183838.

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Al-Mutib, Khalid N., Ebrahim A. Mattar, Mansour M. Alsulaiman, and H. Ramdane. "Stereo vision SLAM based indoor autonomous mobile robot navigation." In 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2014. http://dx.doi.org/10.1109/robio.2014.7090560.

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Wang, Chaoqun, Lili Meng, Sizhen She, Ian M. Mitchell, Teng Li, Frederick Tung, Weiwei Wan, Max Q. H. Meng, and Clarence W. de Silva. "Autonomous mobile robot navigation in uneven and unstructured indoor environments." In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8202145.

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Noh, Samyeul, Jiyoung Park, and Junhee Park. "Autonomous Mobile Robot Navigation in Indoor Environments: Mapping, Localization, and Planning." In 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2020. http://dx.doi.org/10.1109/ictc49870.2020.9289333.

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Camargo, Amauri B., Yisha Liu, Guojian He, and Yan Zhuang. "Mobile Robot Autonomous Exploration and Navigation in Large-scale Indoor Environments." In 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2019. http://dx.doi.org/10.1109/icicip47338.2019.9012209.

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Argush, Gabriel, William Holincheck, Jessica Krynitsky, Brian McGuire, Dax Scott, Charlie Tolleson, and Madhur Behl. "Explorer51 – Indoor Mapping, Discovery, and Navigation for an Autonomous Mobile Robot." In 2020 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2020. http://dx.doi.org/10.1109/sieds49339.2020.9106581.

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Rojas Castro, D. M., A. Revel, and M. Menard. "Document image analysis by a mobile robot for autonomous indoor navigation." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333743.

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Siyao Fu, Zeng-Guang Hou, and Guosheng Yang. "An indoor navigation system for autonomous mobile robot using wireless sensor network." In 2009 International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2009. http://dx.doi.org/10.1109/icnsc.2009.4919277.

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Khan, Fatima, Asma Alakberi, Shamma Almaamari, and Abdul R. Beig. "Navigation algorithm for autonomous mobile robots in indoor environments." In 2018 Advances in Science and Engineering Technology International Conferences (ASET). IEEE, 2018. http://dx.doi.org/10.1109/icaset.2018.8376834.

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