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Relevant bibliographies by topics / Mobile robotics navigation

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Academic literature on the topic 'Mobile robotics navigation'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Mobile robotics navigation"

1

Wu, Qihan. "Research on autonomous mobile robot maze navigation problem based on Dijkstras algorithm." Applied and Computational Engineering 39, no. 1 (2024): 10–17. http://dx.doi.org/10.54254/2755-2721/39/20230570.

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In recent years, the field of autonomous mobile robotics has garnered significant attention due to its potential applications in various domains such as logistics, surveillance, and search and rescue operations. A crucial challenge in this area is the efficient navigation of robots within complex and dynamic environments, particularly when navigating through maze-like structures. The maze navigation problem involves finding optimal paths for robots to traverse from their initial positions to designated destinations while avoiding obstacles and making intelligent decisions to ensure timely and

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Kästner, Linh, Jens Lambrecht, Axel Vick, and Jörg Krüger. "DRL-basierte Navigationsansätze in der industriellen Robotik/DRL-based navigation approaches in industrial robotics." wt Werkstattstechnik online 111, no. 09 (2021): 583–86. http://dx.doi.org/10.37544/1436-4980-2021-09-9.

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Mobile Roboter sind in verschiedenen Bereichen der Industrie zu wichtigen Werkzeugen geworden, insbesondere in der Logistik. Die sichere Navigation in hochdynamischen Umgebungen stellt jedoch weiterhin eine große Herausforderung für klassische Pfadplanungsansätze dar. Deep Reinforcement Learning (DRL) hat sich als alternative Planungsmethode herauskristallisiert, um allzu konservative Ansätze zu ersetzen und verspricht eine effizientere und flexiblere Navigation. Diese Ansätze sind jedoch aufgrund ihrer Anfälligkeit für lokale Minima und das Mangeln eines Langzeitgedächtnisses nicht für die La

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Jimenez Builes, Jovani Alberto, Gustavo Acosta Amaya, and Julián López Velásquez. "Autonomous navigation and indoor mapping for a service robot." Investigación e Innovación en Ingenierías 11, no. 2 (2023): 28–38. http://dx.doi.org/10.17081/invinno.11.2.6459.

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Abstract Objective: Simultaneous Localization and Mapping (SLAM) is a quite common and interesting problem in mobile robotics. It is the basis of safe autonomous navigation of mobile robots and the entrance to new combined applications with a manipulator for instance. Method: In order to find a solution to the SLAM problem, the ROS middleware and the MRPT were selected. Autonomous navigation was tested using two methods, the MRPT navigation ROS package, which is a reactive navigation method based on Trajectory Parameter Space (TP-Space) transformations, and the ROS navigation stack, a st

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Milford, Michael, and Ruth Schulz. "Principles of goal-directed spatial robot navigation in biomimetic models." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1655 (2014): 20130484. http://dx.doi.org/10.1098/rstb.2013.0484.

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Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biolog

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Simkuns, Arturs, Rodions Saltanovs, Maksims Ivanovs, and Roberts Kadikis. "Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics." Sensors 25, no. 5 (2025): 1576. https://doi.org/10.3390/s25051576.

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Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable of handling uncertainty and dynamic environments. We then present experimental results where a grid cell network was trained using trajectories from a mobile unmanned ground vehicle (UGV) robot. After training, the network’s units exhibited spatially periodic and hexagonal activation patterns charact

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Kosser, Fazina, and Neerendra Kumar. "Autonomous Robot Navigation in Known Environment." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 2 (2023): 128–32. http://dx.doi.org/10.35940/ijrte.f7505.0712223.

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Autonomous robot navigation is one of the challenging researched topic in robotics. A secure and optimal path in known environment is required for any mobile robot navigation for navigation purpose. In this work, a Simulink model is proposed based on Pure Pursuit and path following controllers for solving the problem of mobile robot navigation in a known environment is presented. Pure Pursuit controller is used to find the linear and angular velocities of the robot. Moreover, (x, y) coordinate position of robot and waypoints are input to the pure pursuit block. Velocity commands are sent to dr

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Fazina, Kosser, and Kumar Neerendra. "Autonomous Robot Navigation in Known Environment." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 2 (2023): 128–32. https://doi.org/10.35940/ijrte.F7505.0712223.

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<strong>Abstract: </strong>Autonomous robot navigation is one of the challenging researched topic in robotics. A secure and optimal path in known environment is required for any mobile robot navigation for navigation purpose. In this work, a Simulink model is proposed based on Pure Pursuit and path following controllers for solving the problem of mobile robot navigation in a known environment is presented. Pure Pursuit controller is used to find the linear and angular velocities of the robot. Moreover, (x, y) coordinate position of robot and waypoints are input to the pure pursuit block. Veloc

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Noskov, V. P., and D. V. Gubernatorov. "Extreme 3D Image Navigation in Mobile Robotics." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 11 (2021): 594–600. http://dx.doi.org/10.17587/mau.22.594-600.

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The actual problem of determining all six coordinates of the current position of a mobile robot (unmanned aerial vehicle) from 3D-range-finding images (point clouds) generated by an onboard 3D laser sensor when moving (flying) in an unknown environment is considered. An extreme navigation algorithm based on using multidimensional optimization methods is proposed. The rules for calculating the difference between 3D images of the external environment used for optimization of the functional are described. The form of the functional of the difference of 3D images for different environments (premis

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Karthick, Vishal. K., and S. Venkatesh Kumar Dr. "A Study on Mobile Robotics in Robotics." International Journal of Trend in Scientific Research and Development 2, no. 6 (2019): 872–74. https://doi.org/10.31142/ijtsrd18649.

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Mobile Robot competitions are vital way for distribution of science and engineering to the worldwide public but are also brilliant way of testing and comparing unlike research policies. It is discuss how today's study challenges of Intelligent and Autonomous Mobile Robots are being fingered by the Autonomous Driving competition that takes place in the Portuguese Robotics Open annual mobile robotics competition. Karthick Vishal. K | Dr. S. Venkatesh Kumar "A Study on Mobile Robotics in Robotics" Published in International Journal of Trend in Scientific Research and Development (ij

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Cong, Zhaofeng. "Exploring mobile robotics: historical applications and future." Applied and Computational Engineering 6, no. 1 (2023): 45–52. http://dx.doi.org/10.54254/2755-2721/6/20230414.

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This paper reviews the history, current research status and future prospects of intelligent mobile robotics. The paper analyzes in detail the automatic navigation of mobile robots and identifies its advantages and disadvantages. Bionic robotics, artificial intelligence, machine learning, and deep learning are further analysed.

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Dissertations / Theses on the topic "Mobile robotics navigation"

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Tang, Yilun. "Robot navigation and localization in regular office environment /." View abstract or full-text, 2010. http://library.ust.hk/cgi/db/thesis.pl?CSED%202010%20TANG.

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Berg, Brian. "Navigation of a Mobile Robot with Obstacle Avoidance." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10977871.

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<p> Navigating a vehicle autonomously and safely in unknown surroundings to a desired destination is challenging due to lack of initial information about stationary and moving objects along the path. This thesis proposes a navigation system that avoids static and dynamic obstacles using weighted real-time sensor feedback. The effectiveness of the system is demonstrated by implementing it on a robot. A 16-beam solid-state LiDAR sensor is used to detect obstacles to control a differential drive mobile robot. The sensor measurements are weighted and integrated into the Pure Pursuit path following

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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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Qiu, Jiancheng. "A layered control architecture for mobile robot navigation." Thesis, University of Bedfordshire, 1998. http://hdl.handle.net/10547/622047.

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This thesis addresses the problem of how to control an autonomous mobile robot navigation in indoor environments, in the face of sensor noise, imprecise information, uncertainty and limited response time. The thesis argues that the effective control of autonomous mobile robots can be achieved by organising low level and higher level control activities into a layered architecture. The low level reactive control allows the robot to respond to contingencies quickly. The higher level control allows the robot to make longer term decisions and arranges appropriate sequences for a task execution. The

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Amayo, Paul Omondi. "Biologically inspired goal directed navigation for mobile robots." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20512.

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This project involved an investigation into low-cost navigation of mobile robots with the aim of creating and adaptive navigation system inspired by behaviour seen in animals. The navigation module developed here would need to be able to successfully localise a robot and navigate it to a defined target. A critical literature review was carried out of current localisation and path-planning architectures and a bio-inspired approach using an Echo State Network and Liquid State Machine architecture was chosen as the base for the navigation modules. The navigation module implemented in this work is

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Pipe, Anthony Graham. "Reinforcement learning and knowledge transformation in mobile robotics." Thesis, University of the West of England, Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364077.

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Ng, Matthew James. "Corridor Navigation for Monocular Vision Mobile Robots." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1856.

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Monocular vision robots use a single camera to process information about its environment. By analyzing this scene, the robot can determine the best navigation direction. Many modern approaches to robot hallway navigation involve using a plethora of sensors to detect certain features in the environment. This can be laser range finders, inertial measurement units, motor encoders, and cameras. By combining all these sensors, there is unused data which could be useful for navigation. To draw back and develop a baseline approach, this thesis explores the reliability and capability of solely using a

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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 sur

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Cosgun, Akansel. "Navigation behavior design and representations for a people aware mobile robot system." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54944.

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There are millions of robots in operation around the world today, and almost all of them operate on factory floors in isolation from people. However, it is now becoming clear that robots can provide much more value assisting people in daily tasks in human environments. Perhaps the most fundamental capability for a mobile robot is navigating from one location to another. Advances in mapping and motion planning research in the past decades made indoor navigation a commodity for mobile robots. Yet, questions remain on how the robots should move around humans. This thesis advocates the use of sema

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Mouzakitis, Alexandros. "Hybrid control architecture for navigation of autonomous mobile robots." Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/hybrid-control-architecture-for-navigation-of-autonomous-mobile-robots(3b3f0e2d-42f9-4bdc-b55e-b5c4cbed937f).html.

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This thesis is concerned with the development, design and implementation of a novel hybrid multi-agent orientated control architecture for navigation of multiple autonomous mobile robots operating in an unknown and unstructured environment populated by static and/or dynamic obstacles. The proposed hybrid control architecture is modular and draws its design from competitive tasks architecture, production rules architecture, connectionist architecture, dynamic system architecture, multi-agent architecture and subsumption architecture. The reasoning of the control architecture is both deliberativ

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Books on the topic "Mobile robotics navigation"

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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. Computer and Vision Research Center, University of Texas at Austin, 1990.

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Hope, Julian Charles. Global navigation for autonomous mobile robots. University of Salford, 1992.

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Cook, Gerald. Mobile robots: Navigation, control and remote sensing. Wiley-IEEE Press, 2011.

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Meng, Wang. Internet-based teleoperation for mobile robot navigation. Advanced Knowledge International, 2007.

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1949-, Everett H. R., and Feng L, eds. Navigating mobile robots: Systems and techniques. A K Peters, 1996.

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Fehlman, William L. Mobile robot navigation with intelligent infrared image interpretation. Springer, 2009.

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Latt, Khine. Sonar-based localization of mobile robots using the Hough transform. Naval Postgraduate School, 1997.

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8

Knieriemen, Thomas. Autonome mobile Roboter: Sensordateninterpretation und Weltmodellierung zur Navigation in unbekannter Umgebung. BI Wissenschaftsverlag, 1991.

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9

Bayoud, Fadi Atef. Development of a robotic mobile mapping system by vision-aided inertial navigation: A geomatics approach. Institut für Geodäsie und Photogrammetrie, Eidgenössische Technische Hochschule Zürich, 2006.

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Hope, Julian Charles. A multi-sensor global navigation system for autonomous mobile robots. University of Salford, 1995.

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Book chapters on the topic "Mobile robotics navigation"

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Nehmzow, Ulrich. "Navigation." In Mobile Robotics: A Practical Introduction. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-3392-6_5.

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Nehmzow, Ulrich. "Navigation." In Mobile Robotics: A Practical Introduction. Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0025-6_5.

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Chatila, Raja, and Simon Lacroix. "Adaptive Navigation for Autonomous Mobile Robots." In Robotics Research. Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1021-7_49.

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Zhang, Rui, Wanyue Jiang, Zhonghao Zhang, Yuhan Zheng, and Shuzhi Sam Ge. "Indoor Mobile Robot Socially Concomitant Navigation System." In Social Robotics. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24667-8_43.

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Borkowski, Adam, Barbara Siemiatkowska, and Jacek Szklarski. "Towards Semantic Navigation in Mobile Robotics." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17322-6_30.

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Jiang, Rui, Shuzhi Sam Ge, Nagacharan Teja Tangirala, and Tong Heng Lee. "Interactive Navigation of Mobile Robots Based on Human’s Emotion." In Social Robotics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47437-3_24.

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Škoda, Jan, and Roman Barták. "3D Navigation for a Mobile Robot." In ROBOT 2017: Third Iberian Robotics Conference. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70836-2_29.

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Nagatani, Keiji, and Shin’ichi Yuta. "Autonomous Mobile Robot Navigation Including Door Opening Behavior." In Field and Service Robotics. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1273-0_31.

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Rimon, Elon, Ishay Kamon, and John F. Canny. "Local and Global Planning in Sensor Based Navigation of Mobile Robots." In Robotics Research. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1580-9_11.

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Yang, Hyun S., Byeong-Soon Ryu, and Jiyoon Chung. "Integration of Topological Map and Behaviors for Efficient Mobile Robot Navigation." In Robotics Research. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1580-9_21.

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Conference papers on the topic "Mobile robotics navigation"

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Neto, Nilton S. Thiago, Mário César Delunardo Torres, André Luiz Maciel Cid, et al. "Automated Umbilical Cable Control System for Mobile Robot Navigation." In 2024 Latin American Robotics Symposium (LARS). IEEE, 2024. https://doi.org/10.1109/lars64411.2024.10786479.

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Martinelli, Dieisson, André Schneider de Oliveira, and Vivian Cremer Kalempa. "Physarum Route Optimization: Bio-inspired Strategies for Mobile Robot Navigation." In 2024 Latin American Robotics Symposium (LARS). IEEE, 2024. https://doi.org/10.1109/lars64411.2024.10786482.

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Dutt Sharma, Vishnu, Anukriti Singh, and Pratap Tokekar. "Pre-Trained Masked Image Model for Mobile Robot Navigation." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611184.

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Moreira, Luciano Gonçalves, and Alexandre S. Brandão. "SLAM-Based 2D Mapping and Route Planning for Autonomous Mobile Robot Navigation." In 2025 Brazilian Conference on Robotics (CROS). IEEE, 2025. https://doi.org/10.1109/cros66186.2025.11066160.

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Nandikolla, Vidya K., Eden Morris, John Aquino, Thomas Paris, and Kevin Wheeler. "Navigation and Path Planning of an Autonomous Mobile Robot." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-69457.

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Abstract Autonomously navigating robots are used in many applications including assistive robotics, military, space exploration, manufacturing, etc. Unmanned ground vehicles (UGV) are an example of autonomous systems falling under the category of navigation, where navigation is dominantly composed of automatic transport and movement in real world environments. Simultaneous Localization and Mapping (SLAM) provides the best approach to the problems faced in unknown environments. Visual based cameras, light detection and ranging (LiDAR) sensors, global positioning systems, and inertial measuring

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D'Orazio, Tiziana, Arcangelo Distante, Giovanni Attolico, Laura Caponetti, and Ettore Stella. "Vision algorithm for mobile vehicle navigation." In Robotics - DL tentative, edited by William J. Wolfe and Wendell H. Chun. SPIE, 1992. http://dx.doi.org/10.1117/12.135195.

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Case, Michael P. "Single Landmark Navigation By Mobile Robots." In Cambridge Symposium_Intelligent Robotics Systems, edited by Nelson Marquina and William J. Wolfe. SPIE, 1987. http://dx.doi.org/10.1117/12.937801.

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Song, Chunlin, Cheng Chen, and Naigang Cui. "Autonomous Navigation and Mapping for Mobile Robot in Unknown Environment Using Line Segments." In ASME 2016 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/isps2016-9560.

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Used widely in military and civil applications, autonomous robots have shown promising in planet exploration, seabed survey, and disaster rescue. A lot of robotic research concentrates on localization and mapping dealing with the basic problems in robotic research: “Where I am?” and “How is the environment like?”. The two problems consist a coupled problem named Simultaneous Localization and Mapping (SLAM) in unknown environment exploration. This problem is summarized by Hugh D. Whyte in his paper published in 1991 [1]. Forced by requirement of motion in unknown environment, many researchers i

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Wicaksono, H., K. Anam, P. Hastono, I. A. Sulistijono, and S. Kuswadi. "Compact Fuzzy Q Learning for Autonomous Mobile Robot Navigation." In Robotics. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.703-010.

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Chen, Alexander Y. "Collision Avoidance Navigation For Mobile Articulated Robotic Systems." In 1988 Robotics Conferences, edited by William J. Wolfe. SPIE, 1989. http://dx.doi.org/10.1117/12.949095.

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Reports on the topic "Mobile robotics navigation"

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Graves, Kevin P. Continuous Localization and Navigation of Mobile Robots. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada418467.

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Gaudiano, Paolo. Adaptive Control and Navigation of Autonomous Mobile Robots. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada381430.

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EISLER, G. RICHARD. Robust Planning for Autonomous Navigation of Mobile Robots in Unstructured, Dynamic Environments: An LDRD Final Report. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/801404.

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